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 *.zip filter=lfs diff=lfs merge=lfs -text
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diff --git a/README.md b/README.md
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+# Activation
\ No newline at end of file
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diff --git a/exp.py b/exp.py
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+"""
+Tiny Llama GLU Lab — Consolidated training library.
+One file: model definition, activation registry, stability monitoring,
+time tracking, dataset builder, and Trainer factory.
+"""
+
+import math
+import os
+import time
+import json
+import re
+from pathlib import Path
+from itertools import chain
+from typing import Dict, Callable, Optional, List, Any, Tuple
+
+import torch
+import torch.nn as nn
+from transformers import (
+    LlamaConfig,
+    LlamaPreTrainedModel,
+    Trainer,
+    TrainerCallback,
+    TrainingArguments,
+    DataCollatorForLanguageModeling,
+    AutoTokenizer,
+    set_seed,
+)
+# CHANGE 1: LlamaSdpaAttention/LlamaFlashAttention2 no longer exist as separate
+# classes in modern transformers — there is a single LlamaAttention that
+# dispatches to eager/sdpa/flash_attention_2 based on config._attn_implementation.
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+    LlamaRMSNorm,
+    LlamaRotaryEmbedding,
+)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from datasets import load_dataset
+
+
+# =============================================================================
+# 1. ACTIVATION REGISTRY
+# =============================================================================
+
+class GLUActivationRegistry:
+    """Own every gating activation you test. Add new variants in one line."""
+    _registry: Dict[str, Callable[[torch.Tensor], torch.Tensor]] = {}
+
+    @classmethod
+    def register(cls, name: str, fn: Callable[[torch.Tensor], torch.Tensor]) -> None:
+        cls._registry[name] = fn
+
+    @classmethod
+    def get(cls, name: str) -> Callable[[torch.Tensor], torch.Tensor]:
+        if name not in cls._registry:
+            raise KeyError(
+                f"Activation '{name}' not found. Available: {list(cls._registry.keys())}"
+            )
+        return cls._registry[name]
+
+
+# Built-ins
+GLUActivationRegistry.register("silu", nn.functional.silu)
+GLUActivationRegistry.register("swish", nn.functional.silu)
+GLUActivationRegistry.register("relu", nn.functional.relu)
+GLUActivationRegistry.register("gelu", nn.functional.gelu)
+GLUActivationRegistry.register("mish", nn.functional.mish)
+GLUActivationRegistry.register("sigmoid", torch.sigmoid)
+GLUActivationRegistry.register("tanh", torch.tanh)
+GLUActivationRegistry.register("elu", nn.functional.elu)
+GLUActivationRegistry.register("softplus", nn.functional.softplus)
+
+# Identity activation – used for "linear" baseline
+GLUActivationRegistry.register("linear", lambda x: x)
+# For backward compatibility, keep "bilinear" as alias to "linear"
+GLUActivationRegistry.register("bilinear", lambda x: x)
+
+# NEW: s10 = x^2 * sigmoid(x)
+GLUActivationRegistry.register("s10", lambda x: x * x * torch.sigmoid(x))
+
+
+# =============================================================================
+# 2. CONFIG
+# =============================================================================
+
+class TinyLlamaConfig(LlamaConfig):
+    """
+    Exact Llama config plus two fields:
+      - mlp_type: "glu" or "mlp" (standard Transformer MLP)
+      - activation: name of the activation function to use inside the MLP block.
+    Enforces pure MHA by requiring num_key_value_heads == num_attention_heads.
+    """
+    model_type = "tiny_llama"
+
+    def __init__(
+        self,
+        mlp_type: str = "glu",          # default for backward compatibility, but config must override
+        activation: str = "silu",       # default for backward compatibility
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.mlp_type = mlp_type
+        self.activation = activation
+        if self.num_key_value_heads != self.num_attention_heads:
+            raise ValueError(
+                f"Pure MHA required: num_key_value_heads ({self.num_key_value_heads}) "
+                f"must equal num_attention_heads ({self.num_attention_heads})."
+            )
+
+
+# =============================================================================
+# 3. MODEL
+# =============================================================================
+
+class SiTUGLU(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        beta1: float = 4.0,
+        beta2: float = 25.0,
+    ):
+        super().__init__()
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.W_g = nn.Linear(input_dim, hidden_dim, bias=False)
+        self.W_u = nn.Linear(input_dim, hidden_dim, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        g = self.W_g(x)
+        gate = self.beta1 * torch.tanh(g / self.beta1) * torch.sigmoid(g)
+        up = self.beta2 * torch.tanh(self.W_u(x) / self.beta2)
+        return gate * up
+
+
+class TinyLlamaMLP(nn.Module):
+    """
+    MLP block supporting both:
+      - GLU: down_proj( act(gate_proj(x)) * up_proj(x) )
+      - Standard MLP: down_proj( act(up_proj(x)) )
+    For MLP, intermediate_size is auto‑scaled by 1.5× to match GLU parameter count.
+    """
+    def __init__(self, config: TinyLlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size  # base value
+        self.mlp_type = config.mlp_type
+        self.activation_name = config.activation
+
+        # Determine effective dimensions
+        if self.mlp_type == "glu":
+            # GLU: keep original intermediate_size
+            effective_intermediate = self.intermediate_size
+        elif self.mlp_type == "mlp":
+            # Standard MLP: scale by 1.5 to keep parameter count equal
+            effective_intermediate = int(self.intermediate_size * 1.5)
+            print(f"[MLP] Auto‑scaled intermediate_size from {self.intermediate_size} to {effective_intermediate} for parameter parity.")
+        else:
+            raise ValueError(f"Unknown mlp_type: {self.mlp_type}")
+
+        # Store effective size for use in forward
+        self.effective_intermediate = effective_intermediate
+
+        # Handle special activations first
+        if self.mlp_type == "glu" and self.activation_name == "situglu":
+            # SiTUGLU is a complete GLU block (has its own gate & up)
+            self.situglu = SiTUGLU(self.hidden_size, effective_intermediate)
+            self.gate_proj = None
+            self.up_proj = None
+            self.act_fn = None
+        else:
+            self.situglu = None
+            # For both GLU and MLP we need an activation function (except for situglu)
+            if self.activation_name == "situglu":
+                raise ValueError(
+                    f"Activation 'situglu' is only allowed with mlp_type='glu'. "
+                    f"Received mlp_type='{self.mlp_type}'."
+                )
+            self.act_fn = GLUActivationRegistry.get(self.activation_name)
+
+            if self.mlp_type == "glu":
+                # GLU: two parallel projections
+                self.gate_proj = nn.Linear(self.hidden_size, effective_intermediate, bias=False)
+                self.up_proj = nn.Linear(self.hidden_size, effective_intermediate, bias=False)
+            else:  # mlp
+                # Standard MLP: only one projection before activation
+                self.gate_proj = None
+                self.up_proj = nn.Linear(self.hidden_size, effective_intermediate, bias=False)
+
+        # Down projection is always present, using the effective intermediate size
+        self.down_proj = nn.Linear(effective_intermediate, self.hidden_size, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.situglu is not None:
+            # SiTUGLU handles its own gate and up
+            return self.down_proj(self.situglu(x))
+
+        if self.mlp_type == "glu":
+            # GLU: act(gate) * up
+            gate = self.gate_proj(x)
+            up = self.up_proj(x)
+            hidden = self.act_fn(gate) * up
+        else:  # mlp
+            # Standard MLP: act(up)
+            hidden = self.act_fn(self.up_proj(x))
+
+        return self.down_proj(hidden)
+
+
+class TinyLlamaDecoderLayer(nn.Module):
+    def __init__(self, config: TinyLlamaConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # CHANGE 2: LlamaAttention picks its backend (eager/sdpa/flash_attention_2)
+        # from config._attn_implementation internally.
+        self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
+        self.mlp = TinyLlamaMLP(config)
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ):
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_out = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            position_embeddings=position_embeddings,
+        )[0]
+        hidden_states = residual + attn_out
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return (hidden_states,)
+
+
+# CHANGE 3: Replace float mask with Boolean mask (required for Flash Attention)
+def _build_causal_mask(
+    attention_mask: Optional[torch.Tensor],
+    seq_len: int,
+    dtype: torch.dtype,
+    device: torch.device,
+) -> torch.Tensor:
+    """
+    Build a 4D Boolean causal + padding mask for SDPA.
+    - True  -> masked out (do NOT attend)
+    - False -> allowed to attend
+    This is the format expected by torch.nn.functional.scaled_dot_product_attention
+    when using Flash Attention kernels.
+    """
+    # Causal mask: upper triangular (future tokens) = True
+    causal = torch.triu(
+        torch.ones((seq_len, seq_len), dtype=torch.bool, device=device),
+        diagonal=1
+    )
+    causal = causal[None, None, :, :]  # (1, 1, seq_len, seq_len)
+
+    if attention_mask is None:
+        return causal
+
+    batch_size = attention_mask.shape[0]
+    causal = causal.expand(batch_size, 1, seq_len, seq_len).clone()
+
+    # Padding: where attention_mask == 0 -> masked out (True)
+    padding_mask = attention_mask[:, None, None, :].to(device) == 0
+    causal = causal | padding_mask  # OR combines both masks
+
+    return causal
+
+
+class TinyLlamaModel(LlamaPreTrainedModel):
+    config_class = TinyLlamaConfig
+
+    def __init__(self, config: TinyLlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [TinyLlamaDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
+        )
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        # RoPE: computed once per forward pass and threaded into every
+        # decoder layer's attention. Without this, LlamaAttention receives
+        # position_embeddings=None and crashes on `cos, sin = position_embeddings`.
+        self.rotary_emb = LlamaRotaryEmbedding(config=config)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if position_ids is None:
+            seq_len = inputs_embeds.shape[1]
+            position_ids = torch.arange(
+                seq_len, device=inputs_embeds.device
+            ).unsqueeze(0).expand(inputs_embeds.shape[0], -1)
+
+        hidden_states = inputs_embeds
+        # (cos, sin) tuple, shared across all layers this forward pass
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # CHANGE 3: flash_attention_2's kernel does not accept a dense 4D
+        # bool/float mask — it wants None (pure causal) or the raw 2D padding
+        # mask, and handles causality internally. Only build the dense 4D
+        # mask for eager/sdpa backends.
+        if self.config._attn_implementation == "flash_attention_2":
+            causal_mask = attention_mask
+        else:
+            seq_len = hidden_states.shape[1]
+            causal_mask = _build_causal_mask(
+                attention_mask, seq_len, hidden_states.dtype, hidden_states.device
+            )
+
+        for decoder_layer in self.layers:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                position_embeddings=position_embeddings,
+            )
+            hidden_states = layer_outputs[0]
+
+        hidden_states = self.norm(hidden_states)
+        if not return_dict:
+            return (hidden_states,)
+        return {"last_hidden_state": hidden_states, "hidden_states": None, "attentions": None}
+
+
+class TinyLlamaForCausalLM(LlamaPreTrainedModel):
+    config_class = TinyLlamaConfig
+    # Tells save_pretrained() that lm_head.weight and model.embed_tokens.weight
+    # are the SAME tensor on purpose (see tie_word_embeddings below), so it
+    # doesn't error out when it finds two names pointing at shared memory.
+    # NOTE: must be a dict (maps tied key -> source key), not a list —
+    # transformers' _get_tied_weight_keys() calls .keys() on this.
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+
+    def __init__(self, config: TinyLlamaConfig):
+        super().__init__(config)
+        self.model = TinyLlamaModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            return_dict=return_dict,
+        )
+        hidden_states = outputs["last_hidden_state"] if return_dict else outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=None,
+            hidden_states=None,
+            attentions=None,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+        position_ids = kwargs.get("position_ids")
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        return {
+            "input_ids": input_ids,
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+            "attention_mask": attention_mask,
+        }
+
+
+# =============================================================================
+# 4. MONITORING ENGINE
+# =============================================================================
+
+class StatsEngine:
+    """Compute the unified 6-scalar signature for any tensor."""
+
+    @staticmethod
+    def compute(
+        tensor: torch.Tensor, user_limit: float, dtype_ratio: float
+    ) -> Dict[str, float]:
+        with torch.no_grad():
+            abs_t = tensor.abs()
+            dtype_info = torch.finfo(tensor.dtype)
+            dtype_limit = (
+                dtype_ratio * dtype_info.max
+                if not torch.isinf(torch.tensor(dtype_info.max))
+                else float("inf")
+            )
+
+            return {
+                "norm": tensor.norm(2).item(),
+                "mean": tensor.mean().item(),
+                "std": tensor.std().item(),
+                "max_abs": abs_t.max().item(),
+                "frac_near_dtype_limit": (
+                    (abs_t > dtype_limit).float().mean().item()
+                    if not math.isinf(dtype_limit)
+                    else 0.0
+                ),
+                "frac_near_user_limit": (abs_t > user_limit).float().mean().item(),
+            }
+
+
+class StepAccumulator:
+    """
+    Stores per-tensor entries, then aggregates to layer-scope or global-scope
+    using exact population formulas (no tensor retention).
+    """
+
+    def __init__(self):
+        # name -> {numel, norm, mean, std, max_abs, frac_near_dtype_limit, frac_near_user_limit}
+        self.tensors: Dict[str, Dict[str, float]] = {}
+
+    def add(self, name: str, numel: int, stats: Dict[str, float]):
+        # With gradient_accumulation_steps > 1, a single optimizer "step"
+        # runs several forward/backward micro-batches, so the same hook
+        # (e.g. a given layer's activation, or a given param's grad) fires
+        # more than once before clear() is next called. Previously this
+        # method did a plain overwrite, silently discarding every
+        # micro-batch but the last. Merge instead, using the same exact
+        # population formulas _aggregate() uses to combine tensors.
+        new_entry = {"numel": numel, **stats}
+        existing = self.tensors.get(name)
+        self.tensors[name] = (
+            new_entry if existing is None else self._merge_entry(existing, new_entry)
+        )
+
+    @staticmethod
+    def _merge_entry(a: Dict[str, float], b: Dict[str, float]) -> Dict[str, float]:
+        total_n = a["numel"] + b["numel"]
+        if total_n == 0:
+            return a
+        norm = math.sqrt(a["norm"] ** 2 + b["norm"] ** 2)
+        max_abs = max(a["max_abs"], b["max_abs"])
+        mean = (a["mean"] * a["numel"] + b["mean"] * b["numel"]) / total_n
+        ex2 = (
+            a["numel"] * (a["std"] ** 2 + a["mean"] ** 2)
+            + b["numel"] * (b["std"] ** 2 + b["mean"] ** 2)
+        ) / total_n
+        std = math.sqrt(max(0.0, ex2 - mean ** 2))
+        frac_dtype = (
+            a["frac_near_dtype_limit"] * a["numel"] + b["frac_near_dtype_limit"] * b["numel"]
+        ) / total_n
+        frac_user = (
+            a["frac_near_user_limit"] * a["numel"] + b["frac_near_user_limit"] * b["numel"]
+        ) / total_n
+        return {
+            "numel": total_n,
+            "norm": norm,
+            "mean": mean,
+            "std": std,
+            "max_abs": max_abs,
+            "frac_near_dtype_limit": frac_dtype,
+            "frac_near_user_limit": frac_user,
+        }
+
+    def clear(self):
+        self.tensors.clear()
+
+    def _aggregate(self, entries: Dict[str, Dict[str, float]]) -> Dict[str, float]:
+        if not entries:
+            return {}
+        numels = [e["numel"] for e in entries.values()]
+        total_n = sum(numels)
+
+        # L2 norm
+        norm = math.sqrt(sum(e["norm"] ** 2 for e in entries.values()))
+        # Max abs
+        max_abs = max(e["max_abs"] for e in entries.values())
+        # Weighted mean
+        mean = sum(e["mean"] * e["numel"] for e in entries.values()) / total_n
+        # Pooled std: sqrt( E[σ² + μ²] - μ_global² )
+        ex2 = (
+            sum(e["numel"] * (e["std"] ** 2 + e["mean"] ** 2) for e in entries.values())
+            / total_n
+        )
+        std = math.sqrt(max(0.0, ex2 - mean ** 2))
+        # Weighted fractions
+        frac_dtype = (
+            sum(e["frac_near_dtype_limit"] * e["numel"] for e in entries.values())
+            / total_n
+        )
+        frac_user = (
+            sum(e["frac_near_user_limit"] * e["numel"] for e in entries.values())
+            / total_n
+        )
+
+        return {
+            "norm": norm,
+            "mean": mean,
+            "std": std,
+            "max_abs": max_abs,
+            "frac_near_dtype_limit": frac_dtype,
+            "frac_near_user_limit": frac_user,
+        }
+
+    def get_global_stats(self) -> Dict[str, float]:
+        return self._aggregate(self.tensors)
+
+    def get_layer_stats(self, layer_prefix: str) -> Dict[str, float]:
+        entries = {
+            k: v for k, v in self.tensors.items() if k.startswith(layer_prefix + ".")
+        }
+        return self._aggregate(entries)
+
+
+class HookRegistry:
+    """Attach and throttle forward/backward hooks."""
+
+    def __init__(self, model: nn.Module):
+        self.model = model
+        self.handles: List[torch.utils.hooks.RemovableHandle] = []
+        self.active = False
+
+    def attach_forward(
+        self,
+        module_patterns: List[str],
+        accumulator: StepAccumulator,
+        user_limit: float,
+        dtype_ratio: float,
+    ):
+        for name, module in self.model.named_modules():
+            if any(re.search(p, name) for p in module_patterns):
+                h = module.register_forward_hook(
+                    self._make_forward_hook(name, accumulator, user_limit, dtype_ratio)
+                )
+                self.handles.append(h)
+
+    def attach_backward(
+        self, accumulator: StepAccumulator, user_limit: float, dtype_ratio: float
+    ):
+        for name, param in self.model.named_parameters():
+            if param.requires_grad:
+                h = param.register_hook(
+                    self._make_backward_hook(
+                        f"grad.{name}", accumulator, user_limit, dtype_ratio
+                    )
+                )
+                self.handles.append(h)
+
+    def _make_forward_hook(
+        self, module_name: str, accumulator: StepAccumulator, user_limit: float, dtype_ratio: float
+    ):
+        def hook(module, inp, out):
+            if not self.active:
+                return
+
+            # Modules can return a tensor, a tuple (take first item), or a
+            # dict (e.g. TinyLlamaModel returns {"last_hidden_state": ...}).
+            # Pull out the first real tensor we find; skip cleanly if none.
+            if isinstance(out, dict):
+                out_dict = out
+                out = out_dict.get("last_hidden_state")
+                if out is None:
+                    out = next(
+                        (v for v in out_dict.values() if torch.is_tensor(v)), None
+                    )
+            elif isinstance(out, (tuple, list)):
+                out = out[0] if len(out) > 0 else None
+
+            if not torch.is_tensor(out):
+                return
+
+            stats = StatsEngine.compute(out.detach(), user_limit, dtype_ratio)
+            accumulator.add(f"act.{module_name}", out.numel(), stats)
+
+        return hook
+
+    def _make_backward_hook(
+        self, param_name: str, accumulator: StepAccumulator, user_limit: float, dtype_ratio: float
+    ):
+        def hook(grad):
+            if not self.active:
+                return
+            stats = StatsEngine.compute(grad.detach(), user_limit, dtype_ratio)
+            accumulator.add(param_name, grad.numel(), stats)
+
+        return hook
+
+    def set_active(self, active: bool):
+        self.active = active
+
+    def clear(self):
+        for h in self.handles:
+            h.remove()
+        self.handles.clear()
+
+
+class StabilityMonitorCallback(TrainerCallback):
+    """
+    Full stability instrumentation: grad / param / act statistics
+    at global, per-layer, and per-tensor scope.
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        monitor_every_n_steps: int = 10,
+        module_patterns: Optional[List[str]] = None,
+        user_limits: Optional[Dict[str, float]] = None,
+        dtype_proximity_ratio: float = 0.9,
+        log_scope: Optional[Dict[str, bool]] = None,
+        monitor_during_eval: bool = False,
+    ):
+        self.model = model
+        self.monitor_every_n_steps = monitor_every_n_steps
+        self.module_patterns = module_patterns or [".*mlp.*", ".*self_attn.*"]
+        self.user_limits = user_limits or {"grad": 1.0, "param": 100.0, "act": 50.0}
+        self.dtype_ratio = dtype_proximity_ratio
+        self.log_scope = log_scope or {
+            "global": True,
+            "per_layer": True,
+            "per_tensor": False,
+        }
+        self.monitor_during_eval = monitor_during_eval
+
+        self.accumulator = StepAccumulator()
+        self.hooks = HookRegistry(model)
+        self.hooks.attach_forward(
+            self.module_patterns,
+            self.accumulator,
+            self.user_limits["act"],
+            self.dtype_ratio,
+        )
+        self.hooks.attach_backward(
+            self.accumulator, self.user_limits["grad"], self.dtype_ratio
+        )
+
+        self.pending_metrics: Optional[Dict[str, float]] = None
+
+    def _should_monitor(self, state) -> bool:
+        return state.global_step % self.monitor_every_n_steps == 0
+
+    def on_step_begin(self, args, state, control, **kwargs):
+        if self._should_monitor(state):
+            self.accumulator.clear()
+            self.hooks.set_active(True)
+
+    def on_step_end(self, args, state, control, **kwargs):
+        if not self.hooks.active:
+            return
+
+        # Parameter stats (post-optimizer step)
+        for name, param in self.model.named_parameters():
+            stats = StatsEngine.compute(
+                param.data, self.user_limits["param"], self.dtype_ratio
+            )
+            self.accumulator.add(f"param.{name}", param.numel(), stats)
+
+        self.hooks.set_active(False)
+        self.pending_metrics = self._build_metrics()
+
+    @staticmethod
+    def _kind_of(name: str) -> str:
+        """Classify a tensor key by its source: activation, gradient, or parameter."""
+        if name.startswith("act."):
+            return "act"
+        if name.startswith("grad."):
+            return "grad"
+        if name.startswith("param."):
+            return "param"
+        return "other"
+
+    @staticmethod
+    def _strip_kind(name: str) -> str:
+        if name.startswith("act."):
+            return name[4:]
+        if name.startswith(("grad.", "param.")):
+            return name[5:]
+        return name
+
+    def _build_metrics(self, scope: str = "train") -> Dict[str, float]:
+        metrics: Dict[str, float] = {}
+
+        # --- Global (split by kind: act / grad / param — never pooled together) ---
+        if self.log_scope.get("global", True):
+            by_kind: Dict[str, Dict[str, Dict[str, float]]] = {}
+            for k, v in self.accumulator.tensors.items():
+                by_kind.setdefault(self._kind_of(k), {})[k] = v
+
+            for kind, entries in by_kind.items():
+                stats = self.accumulator._aggregate(entries)
+                for kk, vv in stats.items():
+                    metrics[f"{scope}/global/{kind}/{kk}"] = vv
+
+        # --- Per-layer (group by model.layers.{i}, split by kind) ---
+        if self.log_scope.get("per_layer", True):
+            layer_prefixes = set()
+            for name in self.accumulator.tensors:
+                clean = self._strip_kind(name)
+                parts = clean.split(".")
+                for i, p in enumerate(parts):
+                    if p == "layers" and i + 1 < len(parts):
+                        prefix = ".".join(parts[: i + 2])
+                        layer_prefixes.add(prefix)
+
+            for prefix in layer_prefixes:
+                by_kind: Dict[str, Dict[str, Dict[str, float]]] = {}
+                for k, v in self.accumulator.tensors.items():
+                    clean = self._strip_kind(k)
+                    if clean.startswith(prefix + ".") or clean == prefix:
+                        by_kind.setdefault(self._kind_of(k), {})[k] = v
+
+                safe = prefix.replace(".", "_")
+                for kind, entries in by_kind.items():
+                    if not entries:
+                        continue
+                    stats = self.accumulator._aggregate(entries)
+                    for kk, vv in stats.items():
+                        metrics[f"{scope}/layer_{safe}/{kind}/{kk}"] = vv
+
+        # --- Per-tensor ---
+        if self.log_scope.get("per_tensor", False):
+            for name, stats in self.accumulator.tensors.items():
+                safe = name.replace(".", "_")
+                for kk, vv in stats.items():
+                    if kk == "numel":
+                        continue
+                    metrics[f"{scope}/tensor_{safe}/{kk}"] = vv
+
+        return metrics
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs is not None and self.pending_metrics is not None:
+            logs.update(self.pending_metrics)
+            self.pending_metrics = None
+
+    def on_prediction_step(self, args, state, control, **kwargs):
+        """Fires once per eval/predict batch. Trainer.evaluate() calls this
+        for every batch in the eval loop, then calls self.log(output.metrics)
+        (which dispatches on_log to every callback, including the wandb/
+        tensorboard reporting callbacks) BEFORE on_evaluate() runs. So to get
+        eval-time stats into that same on_log dispatch, we have to build
+        pending_metrics here, not in on_evaluate — by the time on_evaluate
+        fires, self.log() has already happened and it's too late.
+        """
+        if not self.monitor_during_eval:
+            return
+        if not self.hooks.active:
+            # First batch of this eval pass: start a fresh accumulation and
+            # snapshot parameter stats once (they don't change during eval).
+            self.accumulator.clear()
+            self.hooks.set_active(True)
+            for name, param in self.model.named_parameters():
+                stats = StatsEngine.compute(
+                    param.data, self.user_limits["param"], self.dtype_ratio
+                )
+                self.accumulator.add(f"param.{name}", param.numel(), stats)
+        self.pending_metrics = self._build_metrics(scope="eval")
+
+    def on_evaluate(self, args, state, control, metrics=None, **kwargs):
+        self.hooks.set_active(False)
+        self.accumulator.clear()
+
+
+class TimeTrackerCallback(TrainerCallback):
+    """Precise training & eval timing with remaining-time estimates."""
+
+    def __init__(self):
+        self.step_start: Optional[float] = None
+        self.epoch_start: Optional[float] = None
+        self.total_train_time = 0.0
+        self.step_times: List[float] = []
+
+    def on_epoch_begin(self, args, state, control, **kwargs):
+        self.epoch_start = time.perf_counter()
+
+    def on_step_begin(self, args, state, control, **kwargs):
+        self.step_start = time.perf_counter()
+
+    def on_step_end(self, args, state, control, **kwargs):
+        if self.step_start is not None:
+            dt = time.perf_counter() - self.step_start
+            self.step_times.append(dt)
+            self.total_train_time += dt
+            self.step_start = None
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs is None:
+            return
+
+        logs["train/total_time_seconds"] = self.total_train_time
+
+        if self.step_times:
+            recent = self.step_times[-100:]
+            logs["train/time_per_step_avg"] = sum(recent) / len(recent)
+
+        if self.epoch_start is not None:
+            logs["train/epoch_time_elapsed"] = time.perf_counter() - self.epoch_start
+
+        if state.max_steps and state.global_step > 0:
+            avg = self.total_train_time / state.global_step
+            remaining = (state.max_steps - state.global_step) * avg
+            logs["train/estimated_remaining_minutes"] = remaining / 60.0
+
+    def on_evaluate(self, args, state, control, metrics=None, **kwargs):
+        pass
+
+
+class MetricsLoggerCallback(TrainerCallback):
+    """Persist every logged dict as JSONL in the output dir."""
+
+    def __init__(self, output_dir: str):
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        self.log_file = self.output_dir / "training_log.jsonl"
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs is None:
+            return
+        entry = {
+            "step": state.global_step,
+            "epoch": state.epoch,
+            "timestamp": time.time(),
+            **logs,
+        }
+        with open(self.log_file, "a") as f:
+            f.write(json.dumps(entry, default=str) + "\n")
+
+
+# =============================================================================
+# 5. DATA & TRAINER FACTORY
+# =============================================================================
+
+def build_dataset(
+    tokenizer,
+    max_seq_len: int = 512,
+    split: str = "train",
+    dataset_name: str = "roneneldan/TinyStories",
+):
+    """Concatenate and chunk TinyStories for causal LM. Fast path with multiprocessing."""
+    ds = load_dataset(dataset_name, split=split)
+
+    def tokenize(examples):
+        # add_special_tokens=False so we control separators ourselves.
+        # Without an explicit boundary token, group_texts() below would
+        # concatenate unrelated stories back-to-back with nothing marking
+        # where one ends and the next begins, teaching the model spurious
+        # cross-document continuations. Append EOS to each example so every
+        # packed chunk still carries a clear "new document" signal.
+        out = tokenizer(examples["text"], add_special_tokens=False)
+        eos_id = tokenizer.eos_token_id
+        out["input_ids"] = [ids + [eos_id] for ids in out["input_ids"]]
+        if "attention_mask" in out:
+            out["attention_mask"] = [mask + [1] for mask in out["attention_mask"]]
+        return out
+
+    tokenized = ds.map(
+        tokenize,
+        batched=True,
+        num_proc=4,
+        remove_columns=ds.column_names,
+        desc=f"Tokenizing {split}",
+    )
+
+    def group_texts(examples):
+        # chain.from_iterable is O(total) instead of O(n²)
+        concatenated = {
+            k: list(chain.from_iterable(examples[k])) for k in examples.keys()
+        }
+        total_length = len(concatenated[list(examples.keys())[0]])
+        total_length = (total_length // max_seq_len) * max_seq_len
+        result = {
+            k: [t[i : i + max_seq_len] for i in range(0, total_length, max_seq_len)]
+            for k, t in concatenated.items()
+        }
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+    return tokenized.map(
+        group_texts,
+        batched=True,
+        batch_size=10000,
+        num_proc=4,
+        desc=f"Chunking {split}",
+    )
+
+
+def create_trainer(
+    model,
+    tokenizer,
+    config: Dict[str, Any],
+    train_dataset,
+    eval_dataset=None,
+):
+    """Assemble HF Trainer with all custom callbacks."""
+    tc = config.get("training", {})
+    mc = config.get("monitor", {})
+
+    # Allow override of run_name via config (used by sweep)
+    run_name = tc.get("run_name", None)
+
+    args = TrainingArguments(
+        output_dir=tc.get("output_dir", "./out"),
+        run_name=run_name,                     # explicit run name for WandB
+        num_train_epochs=tc.get("num_train_epochs", 3),
+        per_device_train_batch_size=tc.get("per_device_train_batch_size", 16),
+        per_device_eval_batch_size=tc.get("per_device_eval_batch_size", 16),
+        gradient_accumulation_steps=tc.get("gradient_accumulation_steps", 4),
+        learning_rate=tc.get("learning_rate", 3e-4),
+        weight_decay=tc.get("weight_decay", 0.0),
+        max_grad_norm=tc.get("max_grad_norm", 1.0),
+        optim=tc.get("optim", "adamw_torch"),
+        warmup_steps=tc.get("warmup_steps", 0),
+        lr_scheduler_type=tc.get("lr_scheduler_type", "cosine"),
+        bf16=tc.get("bf16", True),
+        logging_steps=tc.get("logging_steps", 10),
+        eval_strategy=tc.get("eval_strategy", "steps"),
+        eval_steps=tc.get("eval_steps", 500),
+        save_strategy=tc.get("save_strategy", "steps"),
+        save_steps=tc.get("save_steps", 1000),
+        load_best_model_at_end=tc.get("load_best_model_at_end", False),
+        report_to=tc.get("report_to", "tensorboard"),
+        push_to_hub=tc.get("push_to_hub", False),
+        hub_model_id=tc.get("hub_model_id", None),
+        # Never commit real tokens to config files. Prefer an explicit value
+        # in the config only if someone deliberately put one there; normal
+        # case is HF_TOKEN in the environment.
+        hub_token=tc.get("hub_token") or os.environ.get("HF_TOKEN"),
+        max_steps=tc.get("max_steps", -1),
+        seed=tc.get("seed", 42),
+        data_seed=tc.get("data_seed", 42),
+        remove_unused_columns=False,
+    )
+
+    callbacks = [
+        TimeTrackerCallback(),
+    ]
+
+    if mc.get("enabled", True):
+        callbacks.append(
+            StabilityMonitorCallback(
+                model=model,
+                monitor_every_n_steps=mc.get("monitor_every_n_steps", 10),
+                module_patterns=mc.get("module_patterns", [".*mlp.*", ".*self_attn.*"]),
+                user_limits=mc.get(
+                    "user_limits", {"grad": 1.0, "param": 100.0, "act": 50.0}
+                ),
+                dtype_proximity_ratio=mc.get("dtype_proximity_ratio", 0.9),
+                log_scope=mc.get(
+                    "log_scope",
+                    {"global": True, "per_layer": True, "per_tensor": False},
+                ),
+                monitor_during_eval=mc.get("monitor_during_eval", False),
+            )
+        )
+
+    # Must be added LAST: on_log() writes the shared `logs` dict to disk, so
+    # every callback that injects keys into that dict (e.g. StabilityMonitor's
+    # max_abs/norm/std stats) needs to run BEFORE this one, not after.
+    callbacks.append(MetricsLoggerCallback(args.output_dir))
+
+    collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+    trainer = Trainer(
+        model=model,
+        args=args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        data_collator=collator,
+        callbacks=callbacks,
+    )
+
+    # Trainer.__init__ builds its callback list as:
+    #   [DEFAULT_CALLBACKS..., <report_to integrations, e.g. WandbCallback>,
+    #    *our callbacks (TimeTracker, StabilityMonitor, MetricsLogger)]
+    # All callbacks share the *same* `logs` dict object on on_log(), and are
+    # invoked in that list order. That means WandbCallback.on_log() was
+    # reading `logs` and shipping it off BEFORE TimeTrackerCallback /
+    # StabilityMonitorCallback ever mutated it with their train/global/*,
+    # train/layer_*, eval/* keys — so those metrics only ever reached the
+    # local training_log.jsonl (written by our MetricsLoggerCallback, which
+    # happens to run after within our own sublist) and never wandb.
+    # Move every reporting-integration callback to the very end so all of
+    # our metric-producing callbacks mutate `logs` first.
+    try:
+        from transformers.integrations import get_reporting_integration_callbacks
+
+        reporting_types = tuple(get_reporting_integration_callbacks(args.report_to))
+    except Exception:
+        reporting_types = ()
+
+    if reporting_types:
+        handler = trainer.callback_handler
+        reporting_cbs = [cb for cb in handler.callbacks if isinstance(cb, reporting_types)]
+        other_cbs = [cb for cb in handler.callbacks if not isinstance(cb, reporting_types)]
+        handler.callbacks = other_cbs + reporting_cbs
+
+    return trainer
diff --git a/h.py b/h.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d3692c1b48d1e1e6f1d78364903ff7431dce2e4
--- /dev/null
+++ b/h.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+# Re-occupy GPU3: relaunch the JSL-Med-9B-v3.2 vLLM server (port 8010).
+set -uo pipefail
+SERVE_SCRIPT=/mnt/data/zainulabideen/aws/serve_jsl_9b_v3_2.sh
+LOG=/mnt/data/zainulabideen/Data/RV3/fine_tune/logs/serve_jsl_9b_v3_2_resume.log
+echo ">> checking GPU3 is free before starting..."
+USED=$(nvidia-smi -i 3 --query-gpu=memory.used --format=csv,noheader,nounits)
+if [ "$USED" -gt 5000 ]; then
+  echo ">> GPU3 has ${USED} MiB in use — someone else may be using it. Run free-gpu3.sh first if that's stale."
+  exit 1
+fi
+echo ">> launching JSL-Med-9B-v3.2 on GPU3, port 8010..."
+nohup bash "$SERVE_SCRIPT" > "$LOG" 2>&1 &
+disown
+echo ">> launched, PID $!"
+echo ">> waiting for health check..."
+for i in $(seq 1 40); do
+  r=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8010/v1/models -H "Authorization: Bearer jsl-1" 2>/dev/null)
+  if [ "$r" == "200" ]; then
+    echo ">> HEALTHY after ${i}0s"
+    exit 0
+  fi
+  sleep 10
+done
+echo ">> WARNING: not healthy after 400s, check $LOG"
diff --git a/llm_analyzer_wandb.py b/llm_analyzer_wandb.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9ccd97d8cf2f0c1671161f280f2a3e9970ccbba
--- /dev/null
+++ b/llm_analyzer_wandb.py
@@ -0,0 +1,570 @@
+"""
+Multi-LLM Activation & Loss Analyzer with WandB Logging
+
+Evaluates multiple language models on WikiText dataset,
+computing per-tensor and global activation statistics (mean, max_abs, std, norm)
+and logging to Weights & Biases with separate runs per model.
+
+Requirements:
+    pip install transformers torch datasets wandb tqdm
+
+Usage:
+    export WANDB_PROJECT="llm-activation-analysis"
+    export WANDB_API_KEY="your-key"
+    python llm_analyzer_wandb.py
+"""
+
+import math
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+from datasets import load_dataset
+from typing import List, Dict, Optional, Union, Tuple
+from dataclasses import dataclass, asdict
+from collections import defaultdict
+import json
+import warnings
+import os
+from tqdm import tqdm
+
+import wandb
+
+warnings.filterwarnings("ignore")
+
+
+@dataclass
+class TensorStats:
+    mean: float
+    max_abs: float
+    std: float
+    norm: float
+    numel: int
+
+
+@dataclass
+class ModelResult:
+    model_name: str
+    loss: float
+    perplexity: float
+    global_act: TensorStats
+    layer_acts: Dict[str, TensorStats]
+    num_tokens: int
+    num_layers: int
+    hidden_size: int
+    num_params: int
+
+
+class ActivationHookManager:
+    """Manages forward hooks to capture activations from every tensor."""
+
+    def __init__(self):
+        self.activations = {}
+        self.hooks = []
+        # Set once per batch via set_attention_mask(); used to exclude
+        # padding-token positions from activation statistics.
+        self._attention_mask: Optional[torch.Tensor] = None
+
+    def set_attention_mask(self, attention_mask: Optional[torch.Tensor]):
+        """Call once per batch before the forward pass so hooks can mask
+        out padding positions when computing stats."""
+        self._attention_mask = (
+            attention_mask.detach().cpu() if attention_mask is not None else None
+        )
+
+    def _make_hook(self, name: str):
+        def hook(module, input, output):
+            # Handle different output types
+            if isinstance(output, torch.Tensor):
+                tensor = output
+            elif isinstance(output, tuple) and isinstance(output[0], torch.Tensor):
+                tensor = output[0]
+            else:
+                return
+
+            # Detach and move to CPU to avoid GPU memory blowup
+            self.activations[name] = tensor.detach().cpu().float()
+        return hook
+
+    def register_hooks(self, model: torch.nn.Module):
+        """Register hooks on all modules that produce activations."""
+        for name, module in model.named_modules():
+            # Skip trivial containers
+            if len(list(module.children())) == 0 and hasattr(module, 'forward'):
+                hook = module.register_forward_hook(self._make_hook(name))
+                self.hooks.append(hook)
+
+    def clear(self):
+        self.activations.clear()
+
+    def remove_hooks(self):
+        for hook in self.hooks:
+            hook.remove()
+        self.hooks.clear()
+
+    def _select_real_tokens(self, tensor: torch.Tensor) -> torch.Tensor:
+        """
+        BUG FIX: previously every captured tensor (including activations at
+        padding-token positions) was flattened and used as-is. With
+        padding_side="left" and a small batch_size, the padding fraction
+        varies a lot batch-to-batch, so padding-token activations (which
+        are real, non-zero values — not zeros) were silently mixed into
+        mean/std/max_abs/norm, biasing exactly the saturation signal this
+        script exists to measure.
+
+        Here we mask out padding positions whenever a tensor's shape is
+        consistent with (batch, seq_len, ...) against the stored
+        attention_mask (batch, seq_len). Tensors that don't match that
+        shape (e.g. a module operating on the pooled/final dimension only)
+        are left as-is rather than guessing.
+        """
+        mask = self._attention_mask
+        if mask is None or tensor.dim() < 2:
+            return tensor.reshape(-1)
+        if tensor.shape[0] != mask.shape[0] or tensor.shape[1] != mask.shape[1]:
+            return tensor.reshape(-1)
+
+        bool_mask = mask.bool()
+        # Expand mask across any trailing dims (e.g. hidden_size) and select.
+        expand_shape = bool_mask.shape + (1,) * (tensor.dim() - 2)
+        bool_mask = bool_mask.view(expand_shape).expand_as(tensor)
+        return tensor[bool_mask].reshape(-1)
+
+    def compute_stats(self) -> Dict[str, TensorStats]:
+        """Compute statistics for all captured activations, excluding
+        padding-token positions where identifiable."""
+        stats = {}
+        for name, tensor in self.activations.items():
+            if tensor.numel() == 0:
+                continue
+            flat = self._select_real_tokens(tensor)
+            if flat.numel() == 0:
+                continue
+            stats[name] = TensorStats(
+                mean=flat.mean().item(),
+                max_abs=flat.abs().max().item(),
+                std=flat.std().item(),
+                norm=flat.norm().item(),
+                numel=flat.numel()
+            )
+        return stats
+
+
+class LLMAnalyzer:
+    def __init__(
+        self,
+        device: Optional[str] = None,
+        max_length: int = 512,
+        max_samples: int = 1000,  # number of wikitext samples to eval
+        batch_size: int = 4,
+        dtype: torch.dtype = torch.float16,
+        wandb_project: Optional[str] = None,
+    ):
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.max_length = max_length
+        self.max_samples = max_samples
+        self.batch_size = batch_size
+        self.dtype = dtype if self.device == "cuda" else torch.float32
+        self.wandb_project = wandb_project or os.environ.get("WANDB_PROJECT", "llm-activation-analysis")
+        self._cache = {}
+
+    def load_dataset(self, split: str = "test"):
+        """Load Salesforce/wikitext dataset."""
+        print(f"[Dataset] Loading Salesforce/wikitext ({split}) ...")
+        ds = load_dataset("Salesforce/wikitext", "wikitext-2-raw-v1", split=split)
+        # Filter out empty lines
+        texts = [t for t in ds["text"] if len(t.strip()) > 50]
+        print(f"[Dataset] Loaded {len(texts)} non-empty samples")
+        return texts[:self.max_samples]
+
+    def load_model(self, model_name: str):
+        """Load model and tokenizer with caching."""
+        if model_name in self._cache:
+            return self._cache[model_name]
+
+        print(f"[Loading] {model_name} ...")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_name,
+            trust_remote_code=True,
+            padding_side="left"
+        )
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            config=config,
+            torch_dtype=self.dtype,
+            device_map="auto" if self.device == "cuda" else None,
+            trust_remote_code=True,
+        )
+
+        if self.device == "cpu":
+            model = model.to(self.device)
+
+        model.eval()
+
+        num_params = sum(p.numel() for p in model.parameters())
+
+        self._cache[model_name] = (tokenizer, model, config, num_params)
+        print(f"[Loaded]  {model_name} | Params: {num_params/1e6:.1f}M | Layers: {config.num_hidden_layers} | Hidden: {config.hidden_size}")
+        return tokenizer, model, config, num_params
+
+    def compute(
+        self,
+        model_names: List[str],
+    ) -> List[ModelResult]:
+        """
+        Compute loss and per-tensor activation statistics for multiple models.
+        Logs each model as a separate WandB run.
+        """
+        texts = self.load_dataset()
+        results = []
+
+        for model_name in model_names:
+            try:
+                result = self._evaluate_model(model_name, texts)
+                results.append(result)
+            except Exception as e:
+                print(f"[Error] {model_name}: {e}")
+                import traceback
+                traceback.print_exc()
+                continue
+
+        return results
+
+    def _evaluate_model(
+        self,
+        model_name: str,
+        texts: List[str],
+    ) -> ModelResult:
+        tokenizer, model, config, num_params = self.load_model(model_name)
+
+        # Initialize WandB run for this model
+        run_name = model_name.replace("/", "-")
+        wandb.init(
+            project=self.wandb_project,
+            name=run_name,
+            config={
+                "model": model_name,
+                "max_length": self.max_length,
+                "max_samples": self.max_samples,
+                "batch_size": self.batch_size,
+                "dtype": str(self.dtype),
+                "num_params": num_params,
+                "num_layers": config.num_hidden_layers,
+                "hidden_size": config.hidden_size,
+            },
+            reinit=True
+        )
+
+        hook_mgr = ActivationHookManager()
+        hook_mgr.register_hooks(model)
+
+        total_loss = 0.0
+        total_tokens = 0
+
+        # Global activation accumulator
+        global_acts = []
+
+        # Per-layer activation accumulators
+        # We'll aggregate stats across batches, then compute final stats
+        layer_act_values = defaultdict(list)
+
+        num_batches = (len(texts) + self.batch_size - 1) // self.batch_size
+
+        for i in tqdm(range(0, len(texts), self.batch_size), desc=f"Eval {run_name}", total=num_batches):
+            batch_texts = texts[i:i + self.batch_size]
+
+            # Tokenize
+            inputs = tokenizer(
+                batch_texts,
+                return_tensors="pt",
+                truncation=True,
+                max_length=self.max_length,
+                padding=True
+            )
+
+            # Move to device
+            if self.device == "cuda" and hasattr(model, "device"):
+                # model is on auto device map
+                input_ids = inputs["input_ids"]
+                if hasattr(model, "device") and model.device != torch.device("meta"):
+                    input_ids = input_ids.to(model.device)
+                attention_mask = inputs.get("attention_mask")
+                if attention_mask is not None:
+                    attention_mask = attention_mask.to(input_ids.device)
+            else:
+                input_ids = inputs["input_ids"].to(self.device)
+                attention_mask = inputs.get("attention_mask")
+                if attention_mask is not None:
+                    attention_mask = attention_mask.to(self.device)
+
+            labels = input_ids.clone()
+
+            # BUG FIX: with padding_side="left" and no explicit position_ids,
+            # HF's default `position_ids = arange(seq_len)` is applied
+            # identically to every row in the batch, regardless of how much
+            # left-padding precedes the real tokens in that row (verified
+            # against transformers' LlamaModel.forward / GPT2Model.forward
+            # source — neither adjusts for padding when position_ids=None).
+            # That means a real token's absolute position (and therefore its
+            # RoPE rotation / absolute position embedding) depends on how
+            # much padding happened to precede it in this particular batch,
+            # not on its logical position within its own sequence. This
+            # silently corrupts logits -> loss -> perplexity, with the
+            # amount of corruption varying batch-to-batch. Fix: derive
+            # position_ids from attention_mask so they restart at 0 for the
+            # first real token of every row, and are stable (0) on padding.
+            if attention_mask is not None:
+                position_ids = attention_mask.long().cumsum(-1) - 1
+                position_ids.masked_fill_(attention_mask == 0, 0)
+            else:
+                position_ids = None
+
+            hook_mgr.set_attention_mask(attention_mask)
+
+            with torch.no_grad():
+                outputs = model(
+                    input_ids=input_ids,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    labels=labels,
+                )
+
+            # --- Loss Computation ---
+            logits = outputs.logits
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            shift_mask = attention_mask[..., 1:].contiguous() if attention_mask is not None else None
+
+            loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+            token_losses = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1)
+            )
+
+            if shift_mask is not None:
+                token_losses = token_losses * shift_mask.view(-1)
+                num_valid_tokens = shift_mask.sum().item()
+            else:
+                num_valid_tokens = token_losses.numel()
+
+            batch_loss = token_losses.sum().item()
+            total_loss += batch_loss
+            total_tokens += num_valid_tokens
+
+            # --- Activation Statistics ---
+            # Get activations captured by hooks
+            act_stats = hook_mgr.compute_stats()
+
+            for name, stats in act_stats.items():
+                # Skip dtype-limit fraction metrics entirely
+                # We only log mean, max_abs, std, norm
+
+                # For global: aggregate raw values
+                # We can't store all raw values due to memory, so we store running sums
+                # But for accurate std across all batches, we need a streaming algorithm
+                # For simplicity and correctness, we'll store per-batch stats and weight them
+                layer_act_values[name].append(asdict(stats))
+
+            hook_mgr.clear()
+
+            # Log per-batch metrics to wandb
+            if num_valid_tokens > 0:
+                batch_avg_loss = batch_loss / num_valid_tokens
+                wandb.log({
+                    "batch_loss": batch_avg_loss,
+                    "batch_perplexity": torch.exp(torch.tensor(batch_avg_loss)).item(),
+                    "batch_tokens": num_valid_tokens,
+                    "progress": i / len(texts)
+                }, step=i)
+
+        hook_mgr.remove_hooks()
+
+        # --- Final Aggregation ---
+        avg_loss = total_loss / max(total_tokens, 1)
+        perplexity = torch.exp(torch.tensor(avg_loss)).item()
+
+        # Aggregate per-tensor stats across all batches
+        # Weighted by numel for mean, max for max_abs, pooled std, pooled norm
+        final_layer_stats = {}
+
+        for name, batch_stats_list in layer_act_values.items():
+            total_numel = sum(s["numel"] for s in batch_stats_list)
+            if total_numel == 0:
+                continue
+
+            # Weighted mean
+            weighted_mean = sum(s["mean"] * s["numel"] for s in batch_stats_list) / total_numel
+
+            # Max abs across all batches
+            max_abs = max(s["max_abs"] for s in batch_stats_list)
+
+            # BUG FIX: the previous formula (weighted average of per-batch
+            # variances only) drops the between-batch term that accounts for
+            # per-batch means differing from the global mean. Whenever batch
+            # means differ (they will — different texts, different lengths),
+            # this systematically UNDERESTIMATES the true global std — in a
+            # quick numeric test with two batches of different means this was
+            # off by ~2.8x. Correct pooled-variance formula (population form,
+            # matches exp.py's StepAccumulator._merge_entry):
+            #   E[X^2] = weighted_avg(var_i + mean_i^2)
+            #   Var(X) = E[X^2] - mean_global^2
+            ex2 = sum(
+                s["numel"] * (s["std"] ** 2 + s["mean"] ** 2) for s in batch_stats_list
+            ) / total_numel
+            pooled_std = math.sqrt(max(0.0, ex2 - weighted_mean ** 2))
+
+            # Norm: sqrt(sum of squared norms / total_numel) * sqrt(total_numel)
+            # Actually norm^2 = sum(x_i^2), so pooled_norm = sqrt(sum(norm_i^2))
+            pooled_norm = (sum(s["norm"] ** 2 for s in batch_stats_list)) ** 0.5
+
+            final_layer_stats[name] = TensorStats(
+                mean=weighted_mean,
+                max_abs=max_abs,
+                std=pooled_std,
+                norm=pooled_norm,
+                numel=total_numel
+            )
+
+        # Compute global stats across all layers
+        if final_layer_stats:
+            all_numel = sum(s.numel for s in final_layer_stats.values())
+            global_mean = sum(s.mean * s.numel for s in final_layer_stats.values()) / all_numel
+            global_max_abs = max(s.max_abs for s in final_layer_stats.values())
+            # Same pooled-std correction as above — same bug was present here.
+            global_ex2 = sum(
+                s.numel * (s.std ** 2 + s.mean ** 2) for s in final_layer_stats.values()
+            ) / all_numel
+            global_std = math.sqrt(max(0.0, global_ex2 - global_mean ** 2))
+            global_norm = (sum(s.norm ** 2 for s in final_layer_stats.values())) ** 0.5
+
+            global_stats = TensorStats(
+                mean=global_mean,
+                max_abs=global_max_abs,
+                std=global_std,
+                norm=global_norm,
+                numel=all_numel
+            )
+        else:
+            global_stats = TensorStats(0.0, 0.0, 0.0, 0.0, 0)
+
+        result = ModelResult(
+            model_name=model_name,
+            loss=avg_loss,
+            perplexity=perplexity,
+            global_act=global_stats,
+            layer_acts=final_layer_stats,
+            num_tokens=total_tokens,
+            num_layers=config.num_hidden_layers,
+            hidden_size=config.hidden_size,
+            num_params=num_params
+        )
+
+        # --- WandB Logging ---
+        self._log_to_wandb(result)
+        wandb.finish()
+
+        return result
+
+    def _log_to_wandb(self, result: ModelResult):
+        """Log final metrics to WandB. No frac_near_dtype_limit."""
+
+        # Global metrics
+        wandb.log({
+            "final/loss": result.loss,
+            "final/perplexity": result.perplexity,
+            "final/num_tokens": result.num_tokens,
+
+            "train/global/act/mean": result.global_act.mean,
+            "train/global/act/max_abs": result.global_act.max_abs,
+            "train/global/act/std": result.global_act.std,
+            "train/global/act/norm": result.global_act.norm,
+            # Intentionally NOT logging frac_near_dtype_limit or frac_near_user_limit
+        })
+
+        # Per-tensor (per-layer) metrics
+        # Organize by layer for cleaner WandB UI
+        for tensor_name, stats in result.layer_acts.items():
+            # Clean name for wandb: replace dots with slashes
+            clean_name = tensor_name.replace(".", "/")
+
+            wandb.log({
+                f"train/{clean_name}/act/mean": stats.mean,
+                f"train/{clean_name}/act/max_abs": stats.max_abs,
+                f"train/{clean_name}/act/std": stats.std,
+                f"train/{clean_name}/act/norm": stats.norm,
+                # No frac_near_dtype_limit
+            })
+
+        # Also log as a wandb.Table for easy comparison
+        table_data = []
+        for tensor_name, stats in sorted(result.layer_acts.items()):
+            table_data.append([
+                tensor_name,
+                stats.mean,
+                stats.max_abs,
+                stats.std,
+                stats.norm,
+                stats.numel
+            ])
+
+        if table_data:
+            table = wandb.Table(
+                columns=["tensor_name", "mean", "max_abs", "std", "norm", "numel"],
+                data=table_data
+            )
+            wandb.log({"activation_table": table})
+
+    def print_report(self, results: List[ModelResult]):
+        """Pretty-print comparison report."""
+        print("\n" + "=" * 110)
+        print(f"{'Model':<35} {'Loss':>10} {'PPL':>10} {'ActMean':>12} {'ActMaxAbs':>12} {'ActStd':>12} {'Tokens':>8}")
+        print("-" * 110)
+
+        for r in results:
+            name = r.model_name.split("/")[-1][:33]
+            print(
+                f"{name:<35} "
+                f"{r.loss:>10.4f} "
+                f"{r.perplexity:>10.2f} "
+                f"{r.global_act.mean:>12.6f} "
+                f"{r.global_act.max_abs:>12.6f} "
+                f"{r.global_act.std:>12.6f} "
+                f"{r.num_tokens:>8}"
+            )
+
+        print("=" * 110)
+
+        # Print top 5 layers by max_abs for each model
+        print("\n[Per-Tensor Max Abs Top 5]")
+        for r in results:
+            name = r.model_name.split("/")[-1]
+            sorted_layers = sorted(r.layer_acts.items(), key=lambda x: x[1].max_abs, reverse=True)[:5]
+            print(f"\n  {name}:")
+            for tensor_name, stats in sorted_layers:
+                print(f"    {tensor_name:<50} max_abs={stats.max_abs:>10.4f}  mean={stats.mean:>10.6f}  std={stats.std:>10.4f}")
+
+    def export_json(self, results: List[ModelResult], path: str):
+        """Export results to JSON."""
+        data = []
+        for r in results:
+            entry = {
+                "model": r.model_name,
+                "loss": r.loss,
+                "perplexity": r.perplexity,
+                "num_tokens": r.num_tokens,
+                "num_layers": r.num_layers,
+                "hidden_size": r.hidden_size,
+                "num_params": r.num_params,
+                "global_act": asdict(r.global_act),
+                "layer_acts": {k: asdict(v) for k, v in r.layer_acts.items()}
+            }
+            data.append(entry)
+
+        with open(path, "w") as f:
+            json.dump(data, f, indent=2)
+        print(f"[Exported] Results saved to {path}")
diff --git a/out/glu-gelu-9L_run/README.md b/out/glu-gelu-9L_run/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..bd68e673f3c24ea1296fbbc1eecb6d19d41beb12
--- /dev/null
+++ b/out/glu-gelu-9L_run/README.md
@@ -0,0 +1,65 @@
+---
+library_name: transformers
+tags:
+- generated_from_trainer
+model-index:
+- name: ACTio-glu-gelu-9L
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ACTio-glu-gelu-9L
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2151
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: constant
+- training_steps: 2000
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 3.3152        | 0.0135 | 200  | 3.1407          |
+| 0.8976        | 0.0270 | 400  | 0.8561          |
+| 0.4081        | 0.0404 | 600  | 0.4029          |
+| 0.3257        | 0.0539 | 800  | 0.3262          |
+| 0.2851        | 0.0674 | 1000 | 0.2846          |
+| 0.2629        | 0.0809 | 1200 | 0.2618          |
+| 0.2410        | 0.0944 | 1400 | 0.2429          |
+| 0.2452        | 0.1079 | 1600 | 0.2447          |
+| 0.2224        | 0.1213 | 1800 | 0.2213          |
+| 0.2135        | 0.1348 | 2000 | 0.2151          |
+
+
+### Framework versions
+
+- Transformers 5.15.0.dev0
+- Pytorch 2.6.0+cu124
+- Datasets 5.0.1
+- Tokenizers 0.22.2
diff --git a/out/glu-gelu-9L_run/checkpoint-1000/config.json b/out/glu-gelu-9L_run/checkpoint-1000/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..2287672d22766a55164fb2d34ba92e2aa7bcaf85
--- /dev/null
+++ b/out/glu-gelu-9L_run/checkpoint-1000/config.json
@@ -0,0 +1,35 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "TinyLlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 32,
+  "hidden_act": "silu",
+  "hidden_size": 128,
+  "initializer_range": 0.02,
+  "intermediate_size": 256,
+  "max_position_embeddings": 512,
+  "mlp_bias": false,
+  "mlp_type": "glu",
+  "model_type": "tiny_llama",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 9,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "tokenizer_name": "w-ahmad/tiny-stories-tokenizer",
+  "transformers_version": "5.15.0.dev0",
+  "use_cache": false,
+  "vocab_size": 4096
+}
diff --git a/out/glu-gelu-9L_run/checkpoint-1000/model.safetensors b/out/glu-gelu-9L_run/checkpoint-1000/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..2b35a2e8799451bd04786baf374700ef4928366d
--- /dev/null
+++ b/out/glu-gelu-9L_run/checkpoint-1000/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e35b0e11a46d87fbedb0dbd610a7d8df1fe3ebbc9c047856d41f4791f8532b2
+size 4011496
diff --git a/out/glu-gelu-9L_run/checkpoint-1000/optimizer.pt b/out/glu-gelu-9L_run/checkpoint-1000/optimizer.pt
new file mode 100644
index 0000000000000000000000000000000000000000..5a27a3ef7f55ff3fa63a625a6aacce59993733ca
--- /dev/null
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+      [
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+      [
+        "Ġenv",
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
+        "Ġmar",
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+        "Ġtreasure",
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+      [
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+      [
+        "t",
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+      [
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+      [
+        "Ġg",
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+      [
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+      [
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+      [
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+      [
+        "Ġre",
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+      [
+        "Ġse",
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+      [
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+      [
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+      [
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+      [
+        "Ġbre",
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+      [
+        "Ġrep",
+        "air"
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+      [
+        "Ġfoot",
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+      [
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+      [
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+        "er"
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+      [
+        "Ġang",
+        "el"
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+      [
+        "Ġtriang",
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+      [
+        "Ġl",
+        "ed"
+      ],
+      [
+        "Ġm",
+        "ill"
+      ],
+      [
+        "ĠS",
+        "h"
+      ],
+      [
+        "ch",
+        "anic"
+      ],
+      [
+        "Ġnam",
+        "es"
+      ],
+      [
+        "Ġle",
+        "opard"
+      ],
+      [
+        "Ġno",
+        "body"
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+      [
+        "Ġany",
+        "way"
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+      [
+        "uc",
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+      [
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+      [
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+        "eb"
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+      [
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+        "ven"
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+      [
+        "Ġhold",
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+      [
+        "Ġbar",
+        "rel"
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+      [
+        "Ġband",
+        "age"
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+      [
+        "Ġent",
+        "h"
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+      [
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+      [
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+      [
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+      [
+        "u",
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+      [
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+      [
+        "Ġp",
+        "or"
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+      [
+        "Ġp",
+        "and"
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+      [
+        "Ġo",
+        "tter"
+      ],
+      [
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+        "ibb"
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+      [
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+      [
+        "Ġme",
+        "chanic"
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+      [
+        "Ġdon",
+        "â"
+      ],
+      [
+        "Ġdis",
+        "ag"
+      ],
+      [
+        "Ġdis",
+        "play"
+      ],
+      [
+        "Ġmusic",
+        "ian"
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+      [
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+        "os"
+      ],
+      [
+        "Ġspe",
+        "ak"
+      ],
+      [
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+        "b"
+      ],
+      [
+        "Ġca",
+        "ctus"
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+        "sy"
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+      [
+        "Ġbul",
+        "b"
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+      [
+        "C",
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+      [
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+      [
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+      [
+        "x",
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+      [
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+      [
+        "Ġa",
+        "head"
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+      [
+        "Ġs",
+        "ummer"
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+      [
+        "Ġs",
+        "kelet"
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+      [
+        "Ġp",
+        "urse"
+      ],
+      [
+        "Ġkn",
+        "ob"
+      ],
+      [
+        "Ġdidn",
+        "â"
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+      [
+        "Ġcouldn",
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+      [
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+      [
+        "Ġwindow",
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+      [
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+      [
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+        "ted"
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+      [
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+        "oon"
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+      [
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+      [
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+      [
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+      [
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+      [
+        "ĠM",
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+      [
+        "Ġsc",
+        "rew"
+      ],
+      [
+        "Ġfl",
+        "our"
+      ],
+      [
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+        "in"
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+      [
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+      [
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+        "ing"
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+      [
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+        "fish"
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+        "pack"
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+        "iting"
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+      [
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+        "Ġro",
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+      [
+        "Ġmu",
+        "ff"
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+        "port"
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+      [
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+        "iling"
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+        "ould"
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+      [
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+      [
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+        "ano"
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+      [
+        "Ġsal",
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+      [
+        "Ġmis",
+        "erable"
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+      [
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+        "ord"
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+      [
+        "Ġd",
+        "ess"
+      ],
+      [
+        "Ġre",
+        "fused"
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+      [
+        "Ġsc",
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+      [
+        "ĠD",
+        "an"
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+      [
+        "Ġstr",
+        "ugg"
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+      [
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+      [
+        "Ġbear",
+        "s"
+      ],
+      [
+        "ĠAnd",
+        "y"
+      ],
+      [
+        "Ġhead",
+        "ed"
+      ],
+      [
+        "Ġstick",
+        "y"
+      ],
+      [
+        "Ġfree",
+        "z"
+      ],
+      [
+        "Ġzoom",
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+      ],
+      [
+        "Ġimag",
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+      [
+        "Ġmed",
+        "al"
+      ],
+      [
+        "Ġmagn",
+        "et"
+      ],
+      [
+        "rec",
+        "i"
+      ],
+      [
+        "Ġobs",
+        "er"
+      ],
+      [
+        "Ġ",
+        "er"
+      ],
+      [
+        "Ġli",
+        "ves"
+      ],
+      [
+        "ĠA",
+        "l"
+      ],
+      [
+        "ĠP",
+        "at"
+      ],
+      [
+        "Ġapp",
+        "reci"
+      ],
+      [
+        "ass",
+        "es"
+      ],
+      [
+        "Ġsail",
+        "or"
+      ],
+      [
+        "Ġminut",
+        "e"
+      ],
+      [
+        "Ġfisher",
+        "man"
+      ],
+      [
+        "v",
+        "ision"
+      ],
+      [
+        "Ġr",
+        "at"
+      ],
+      [
+        "al",
+        "ed"
+      ],
+      [
+        "Ġbr",
+        "us"
+      ],
+      [
+        "Ġch",
+        "ance"
+      ],
+      [
+        "Ġpo",
+        "st"
+      ],
+      [
+        "Ġsun",
+        "gl"
+      ],
+      [
+        "Ġche",
+        "ek"
+      ],
+      [
+        "ree",
+        "ze"
+      ],
+      [
+        "Ġde",
+        "af"
+      ],
+      [
+        "Ġru",
+        "les"
+      ],
+      [
+        "Ġfrog",
+        "s"
+      ],
+      [
+        "Ġair",
+        "pl"
+      ],
+      [
+        "ĠGo",
+        "d"
+      ],
+      [
+        "Ġstrawber",
+        "ry"
+      ],
+      [
+        "Ġsle",
+        "pt"
+      ],
+      [
+        "Ġliz",
+        "ard"
+      ],
+      [
+        "Ġsungl",
+        "asses"
+      ],
+      [
+        "Ġl",
+        "uck"
+      ],
+      [
+        "Ġin",
+        "f"
+      ],
+      [
+        "Ġbe",
+        "ep"
+      ],
+      [
+        "ĠB",
+        "unny"
+      ],
+      [
+        "Ġme",
+        "as"
+      ],
+      [
+        "Ġro",
+        "d"
+      ],
+      [
+        "Ġor",
+        "der"
+      ],
+      [
+        "Ġrest",
+        "less"
+      ],
+      [
+        "Ġsand",
+        "box"
+      ],
+      [
+        "Ġmess",
+        "age"
+      ],
+      [
+        "ĠJoe",
+        "y"
+      ],
+      [
+        "Ġcomp",
+        "let"
+      ],
+      [
+        "Ġatt",
+        "ract"
+      ],
+      [
+        "Ġgold",
+        "en"
+      ],
+      [
+        "Gra",
+        "ndma"
+      ],
+      [
+        "Ġpil",
+        "ot"
+      ],
+      [
+        "Ġpor",
+        "ch"
+      ],
+      [
+        "L",
+        "ittle"
+      ],
+      [
+        "l",
+        "er"
+      ],
+      [
+        "s",
+        "es"
+      ]
+    ]
+  }
+}
\ No newline at end of file
diff --git a/out/glu-gelu-9L_run/checkpoint-1000/tokenizer_config.json b/out/glu-gelu-9L_run/checkpoint-1000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
+++ b/out/glu-gelu-9L_run/checkpoint-1000/tokenizer_config.json
@@ -0,0 +1,13 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1024,
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diff --git a/out/glu-gelu-9L_run/checkpoint-1500/tokenizer_config.json b/out/glu-gelu-9L_run/checkpoint-1500/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
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+}
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new file mode 100644
index 0000000000000000000000000000000000000000..9c8146accee72a14680bebabe32bedbf0e6fb4c7
--- /dev/null
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diff --git a/out/glu-gelu-9L_run/checkpoint-2000/tokenizer_config.json b/out/glu-gelu-9L_run/checkpoint-2000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
+++ b/out/glu-gelu-9L_run/checkpoint-2000/tokenizer_config.json
@@ -0,0 +1,13 @@
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+}
diff --git a/out/glu-gelu-9L_run/checkpoint-2000/trainer_state.json b/out/glu-gelu-9L_run/checkpoint-2000/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..10e7743ff07db257f6239ce7ebe8912c2f06e552
--- /dev/null
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diff --git a/out/glu-linear-9L_run/checkpoint-1000/tokenizer_config.json b/out/glu-linear-9L_run/checkpoint-1000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
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+}
diff --git a/out/glu-linear-9L_run/checkpoint-1000/trainer_state.json b/out/glu-linear-9L_run/checkpoint-1000/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..bc288706863af6f842815c78974dcdbd99d91476
--- /dev/null
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+{"step": 1440, "epoch": 0.09706774519716886, "timestamp": 1786049114.4478006, "loss": 0.22553596496582032, "grad_norm": 0.2138671875, "learning_rate": 0.0003, "train/total_time_seconds": 43.657894525676966, "train/time_per_step_avg": 0.03023282878100872, "train/epoch_time_elapsed": 172.37214321643114, "train/estimated_remaining_minutes": 0.282967834888647}
diff --git a/out/glu-relu-9L_run/README.md b/out/glu-relu-9L_run/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d98d6e3f9ec71b2dd311367bc56df06554011746
--- /dev/null
+++ b/out/glu-relu-9L_run/README.md
@@ -0,0 +1,65 @@
+---
+library_name: transformers
+tags:
+- generated_from_trainer
+model-index:
+- name: ACTio-glu-relu-9L
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ACTio-glu-relu-9L
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2152
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: constant
+- training_steps: 2000
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 3.1300        | 0.0135 | 200  | 2.9256          |
+| 0.7347        | 0.0270 | 400  | 0.6944          |
+| 0.4105        | 0.0404 | 600  | 0.4061          |
+| 0.3393        | 0.0539 | 800  | 0.3342          |
+| 0.2869        | 0.0674 | 1000 | 0.2864          |
+| 0.2769        | 0.0809 | 1200 | 0.2695          |
+| 0.2422        | 0.0944 | 1400 | 0.2439          |
+| 0.2395        | 0.1079 | 1600 | 0.2467          |
+| 0.2244        | 0.1213 | 1800 | 0.2234          |
+| 0.2135        | 0.1348 | 2000 | 0.2152          |
+
+
+### Framework versions
+
+- Transformers 5.15.0.dev0
+- Pytorch 2.6.0+cu124
+- Datasets 5.0.1
+- Tokenizers 0.22.2
diff --git a/out/glu-relu-9L_run/checkpoint-1000/config.json b/out/glu-relu-9L_run/checkpoint-1000/config.json
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index 0000000000000000000000000000000000000000..15ed1eadfe396fc1364cfd962d357764fb54be65
--- /dev/null
+++ b/out/glu-relu-9L_run/checkpoint-1000/config.json
@@ -0,0 +1,35 @@
+{
+  "activation": "relu",
+  "architectures": [
+    "TinyLlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 32,
+  "hidden_act": "silu",
+  "hidden_size": 128,
+  "initializer_range": 0.02,
+  "intermediate_size": 256,
+  "max_position_embeddings": 512,
+  "mlp_bias": false,
+  "mlp_type": "glu",
+  "model_type": "tiny_llama",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 9,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "tokenizer_name": "w-ahmad/tiny-stories-tokenizer",
+  "transformers_version": "5.15.0.dev0",
+  "use_cache": false,
+  "vocab_size": 4096
+}
diff --git a/out/glu-relu-9L_run/checkpoint-1000/model.safetensors b/out/glu-relu-9L_run/checkpoint-1000/model.safetensors
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+        "ched"
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+        "ful"
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+      [
+        "Ġav",
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+      [
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+        "scope"
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+      [
+        "I",
+        "f"
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+      [
+        "i",
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+      [
+        "Ġnot",
+        "eb"
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+      [
+        "ĠE",
+        "ventually"
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+      [
+        "Ġold",
+        "er"
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+        "Ġsn",
+        "iff"
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+      [
+        "Ġad",
+        "vice"
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+      [
+        "Ġstop",
+        "s"
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+      [
+        "Ġperf",
+        "orm"
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+      [
+        "Ġfur",
+        "ther"
+      ],
+      [
+        "Ġenv",
+        "ious"
+      ],
+      [
+        "Ġpige",
+        "on"
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+      [
+        "Ġmush",
+        "room"
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+        "Ġnoteb",
+        "ook"
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+      [
+        "u",
+        "bby"
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+      [
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+      [
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+      [
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+      [
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+        "ilt"
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+      [
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+        "l"
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+      [
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+        "com"
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+      [
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+        "ing"
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+        "urt"
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+      [
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+        "fortable"
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+      [
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+      [
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+        "es"
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+      [
+        "ĠT",
+        "ony"
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+      [
+        "Ġloo",
+        "p"
+      ],
+      [
+        "Ġch",
+        "ubby"
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+      [
+        "Ġsw",
+        "itch"
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+      [
+        "Ġsk",
+        "ip"
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+      [
+        "Ġad",
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+      [
+        "Åĵ",
+        "It"
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+      [
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+      [
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+      [
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+        "ut"
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+      [
+        "ic",
+        "op"
+      ],
+      [
+        "Ġqu",
+        "ite"
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+      [
+        "Ġsn",
+        "uggled"
+      ],
+      [
+        "ĠGra",
+        "ndpa"
+      ],
+      [
+        "Ġtrou",
+        "bled"
+      ],
+      [
+        "Ġgigg",
+        "le"
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+      [
+        "Ġblow",
+        "ing"
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+      [
+        "Ġhel",
+        "icop"
+      ],
+      [
+        "Ġfis",
+        "her"
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+      [
+        "Ġpist",
+        "ol"
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+      [
+        "Ġhelicop",
+        "ter"
+      ],
+      [
+        "p",
+        "ar"
+      ],
+      [
+        "ĠS",
+        "ophie"
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+      [
+        "oo",
+        "ped"
+      ],
+      [
+        "ip",
+        "s"
+      ],
+      [
+        "Ġnow",
+        "here"
+      ],
+      [
+        "Ġforg",
+        "ave"
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+      [
+        "Ġmar",
+        "ched"
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+      [
+        "Ġtreasure",
+        "s"
+      ],
+      [
+        "ho",
+        "st"
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+      [
+        "Ġpass",
+        "port"
+      ],
+      [
+        "Ġstir",
+        "red"
+      ],
+      [
+        "Ġpus",
+        "hing"
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+      [
+        "Ġcompetit",
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+      [
+        "Ġdestro",
+        "y"
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+      [
+        "Y",
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+      ],
+      [
+        "a",
+        "ked"
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+      [
+        "o",
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+      [
+        "t",
+        "ter"
+      ],
+      [
+        "Ġg",
+        "ir"
+      ],
+      [
+        "Ġg",
+        "host"
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+      [
+        "an",
+        "ic"
+      ],
+      [
+        "ke",
+        "let"
+      ],
+      [
+        "ht",
+        "ub"
+      ],
+      [
+        "Ġre",
+        "ef"
+      ],
+      [
+        "Ġse",
+        "par"
+      ],
+      [
+        "op",
+        "ard"
+      ],
+      [
+        "pl",
+        "ay"
+      ],
+      [
+        "Ġen",
+        "vel"
+      ],
+      [
+        "Ġbre",
+        "at"
+      ],
+      [
+        "Ġrep",
+        "air"
+      ],
+      [
+        "Ġfoot",
+        "ball"
+      ],
+      [
+        "Ġbat",
+        "htub"
+      ],
+      [
+        "Ġrubb",
+        "er"
+      ],
+      [
+        "Ġang",
+        "el"
+      ],
+      [
+        "Ġtriang",
+        "le"
+      ],
+      [
+        "Ġl",
+        "ed"
+      ],
+      [
+        "Ġm",
+        "ill"
+      ],
+      [
+        "ĠS",
+        "h"
+      ],
+      [
+        "ch",
+        "anic"
+      ],
+      [
+        "Ġnam",
+        "es"
+      ],
+      [
+        "Ġle",
+        "opard"
+      ],
+      [
+        "Ġno",
+        "body"
+      ],
+      [
+        "Ġany",
+        "way"
+      ],
+      [
+        "uc",
+        "t"
+      ],
+      [
+        "ct",
+        "us"
+      ],
+      [
+        "Ġshould",
+        "n"
+      ],
+      [
+        "Ġz",
+        "eb"
+      ],
+      [
+        "Ġgi",
+        "ven"
+      ],
+      [
+        "Ġhold",
+        "s"
+      ],
+      [
+        "Ġbar",
+        "rel"
+      ],
+      [
+        "Ġband",
+        "age"
+      ],
+      [
+        "Ġent",
+        "h"
+      ],
+      [
+        "Dad",
+        "dy"
+      ],
+      [
+        "Ġzeb",
+        "ra"
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+      [
+        "i",
+        "ves"
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+      [
+        "u",
+        "be"
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+      [
+        "y",
+        "ear"
+      ],
+      [
+        "Ġp",
+        "or"
+      ],
+      [
+        "Ġp",
+        "and"
+      ],
+      [
+        "Ġo",
+        "tter"
+      ],
+      [
+        "Ġr",
+        "ibb"
+      ],
+      [
+        "Ġsh",
+        "ield"
+      ],
+      [
+        "Ġme",
+        "chanic"
+      ],
+      [
+        "Ġdon",
+        "â"
+      ],
+      [
+        "Ġdis",
+        "ag"
+      ],
+      [
+        "Ġdis",
+        "play"
+      ],
+      [
+        "Ġmusic",
+        "ian"
+      ],
+      [
+        "cer",
+        "os"
+      ],
+      [
+        "Ġspe",
+        "ak"
+      ],
+      [
+        "Ġca",
+        "b"
+      ],
+      [
+        "Ġca",
+        "ctus"
+      ],
+      [
+        "ĠBet",
+        "sy"
+      ],
+      [
+        "Ġbul",
+        "b"
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+      [
+        "C",
+        "h"
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+      [
+        "e",
+        "ath"
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+      [
+        "i",
+        "kes"
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+      [
+        "x",
+        "y"
+      ],
+      [
+        "Ġt",
+        "ube"
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+      [
+        "Ġa",
+        "head"
+      ],
+      [
+        "Ġs",
+        "ummer"
+      ],
+      [
+        "Ġs",
+        "kelet"
+      ],
+      [
+        "Ġp",
+        "urse"
+      ],
+      [
+        "Ġkn",
+        "ob"
+      ],
+      [
+        "Ġdidn",
+        "â"
+      ],
+      [
+        "Ġcouldn",
+        "â"
+      ],
+      [
+        "Ġcol",
+        "ours"
+      ],
+      [
+        "Ġwindow",
+        "s"
+      ],
+      [
+        "oom",
+        "y"
+      ],
+      [
+        "Ġgif",
+        "ted"
+      ],
+      [
+        "Ġcart",
+        "oon"
+      ],
+      [
+        "Ġrhino",
+        "ceros"
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+      [
+        "g",
+        "l"
+      ],
+      [
+        "he",
+        "art"
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+      [
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+      [
+        "Ġb",
+        "et"
+      ],
+      [
+        "ĠM",
+        "iss"
+      ],
+      [
+        "Ġsc",
+        "rew"
+      ],
+      [
+        "Ġfl",
+        "our"
+      ],
+      [
+        "Ġbl",
+        "in"
+      ],
+      [
+        "ap",
+        "a"
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+      [
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+        "hes"
+      ],
+      [
+        "Ġrain",
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+      ],
+      [
+        "Ġhop",
+        "ing"
+      ],
+      [
+        "Ġsho",
+        "vel"
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+      [
+        "Ġmin",
+        "t"
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+      [
+        "Ġjelly",
+        "fish"
+      ],
+      [
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+        "ater"
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+      [
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+        "ming"
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+      [
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+      [
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+        "ope"
+      ],
+      [
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+      ],
+      [
+        "Ġcab",
+        "in"
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+      [
+        "re",
+        "c"
+      ],
+      [
+        "Ġp",
+        "ant"
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+      [
+        "Ġth",
+        "read"
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+      [
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+        "se"
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+      [
+        "Ġst",
+        "aff"
+      ],
+      [
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+        "z"
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+      [
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+        "pack"
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+      [
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+        "iting"
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+      [
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+        "sh"
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+      [
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+      [
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+      [
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+        "iron"
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+      [
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+        "ing"
+      ],
+      [
+        "er",
+        "able"
+      ],
+      [
+        "Ġshe",
+        "et"
+      ],
+      [
+        "ĠB",
+        "uddy"
+      ],
+      [
+        "Ġsh",
+        "adow"
+      ],
+      [
+        "Ġro",
+        "ared"
+      ],
+      [
+        "Ġmu",
+        "ff"
+      ],
+      [
+        "Ġair",
+        "port"
+      ],
+      [
+        "Ġce",
+        "iling"
+      ],
+      [
+        "Ġatt",
+        "ic"
+      ],
+      [
+        "Ġorgan",
+        "ize"
+      ],
+      [
+        "Ġstret",
+        "ch"
+      ],
+      [
+        "Ġgrap",
+        "es"
+      ],
+      [
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+        "bs"
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+      [
+        "ĠA",
+        "re"
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+      [
+        "ĠW",
+        "ould"
+      ],
+      [
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+        "st"
+      ],
+      [
+        "Ġpi",
+        "ano"
+      ],
+      [
+        "Ġsal",
+        "t"
+      ],
+      [
+        "Ġmis",
+        "erable"
+      ],
+      [
+        "Ġc",
+        "ord"
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+      [
+        "Ġd",
+        "ess"
+      ],
+      [
+        "Ġre",
+        "fused"
+      ],
+      [
+        "Ġsc",
+        "reen"
+      ],
+      [
+        "ĠD",
+        "an"
+      ],
+      [
+        "Ġstr",
+        "ugg"
+      ],
+      [
+        "Ġneed",
+        "le"
+      ],
+      [
+        "Ġbear",
+        "s"
+      ],
+      [
+        "ĠAnd",
+        "y"
+      ],
+      [
+        "Ġhead",
+        "ed"
+      ],
+      [
+        "Ġstick",
+        "y"
+      ],
+      [
+        "Ġfree",
+        "z"
+      ],
+      [
+        "Ġzoom",
+        "ed"
+      ],
+      [
+        "Ġimag",
+        "ined"
+      ],
+      [
+        "Ġmed",
+        "al"
+      ],
+      [
+        "Ġmagn",
+        "et"
+      ],
+      [
+        "rec",
+        "i"
+      ],
+      [
+        "Ġobs",
+        "er"
+      ],
+      [
+        "Ġ",
+        "er"
+      ],
+      [
+        "Ġli",
+        "ves"
+      ],
+      [
+        "ĠA",
+        "l"
+      ],
+      [
+        "ĠP",
+        "at"
+      ],
+      [
+        "Ġapp",
+        "reci"
+      ],
+      [
+        "ass",
+        "es"
+      ],
+      [
+        "Ġsail",
+        "or"
+      ],
+      [
+        "Ġminut",
+        "e"
+      ],
+      [
+        "Ġfisher",
+        "man"
+      ],
+      [
+        "v",
+        "ision"
+      ],
+      [
+        "Ġr",
+        "at"
+      ],
+      [
+        "al",
+        "ed"
+      ],
+      [
+        "Ġbr",
+        "us"
+      ],
+      [
+        "Ġch",
+        "ance"
+      ],
+      [
+        "Ġpo",
+        "st"
+      ],
+      [
+        "Ġsun",
+        "gl"
+      ],
+      [
+        "Ġche",
+        "ek"
+      ],
+      [
+        "ree",
+        "ze"
+      ],
+      [
+        "Ġde",
+        "af"
+      ],
+      [
+        "Ġru",
+        "les"
+      ],
+      [
+        "Ġfrog",
+        "s"
+      ],
+      [
+        "Ġair",
+        "pl"
+      ],
+      [
+        "ĠGo",
+        "d"
+      ],
+      [
+        "Ġstrawber",
+        "ry"
+      ],
+      [
+        "Ġsle",
+        "pt"
+      ],
+      [
+        "Ġliz",
+        "ard"
+      ],
+      [
+        "Ġsungl",
+        "asses"
+      ],
+      [
+        "Ġl",
+        "uck"
+      ],
+      [
+        "Ġin",
+        "f"
+      ],
+      [
+        "Ġbe",
+        "ep"
+      ],
+      [
+        "ĠB",
+        "unny"
+      ],
+      [
+        "Ġme",
+        "as"
+      ],
+      [
+        "Ġro",
+        "d"
+      ],
+      [
+        "Ġor",
+        "der"
+      ],
+      [
+        "Ġrest",
+        "less"
+      ],
+      [
+        "Ġsand",
+        "box"
+      ],
+      [
+        "Ġmess",
+        "age"
+      ],
+      [
+        "ĠJoe",
+        "y"
+      ],
+      [
+        "Ġcomp",
+        "let"
+      ],
+      [
+        "Ġatt",
+        "ract"
+      ],
+      [
+        "Ġgold",
+        "en"
+      ],
+      [
+        "Gra",
+        "ndma"
+      ],
+      [
+        "Ġpil",
+        "ot"
+      ],
+      [
+        "Ġpor",
+        "ch"
+      ],
+      [
+        "L",
+        "ittle"
+      ],
+      [
+        "l",
+        "er"
+      ],
+      [
+        "s",
+        "es"
+      ]
+    ]
+  }
+}
\ No newline at end of file
diff --git a/out/glu-relu-9L_run/checkpoint-1000/tokenizer_config.json b/out/glu-relu-9L_run/checkpoint-1000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
+++ b/out/glu-relu-9L_run/checkpoint-1000/tokenizer_config.json
@@ -0,0 +1,13 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}
diff --git a/out/glu-relu-9L_run/checkpoint-1000/trainer_state.json b/out/glu-relu-9L_run/checkpoint-1000/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..7c712c8f8c2220a85b228d32796f46e282c5b883
--- /dev/null
+++ b/out/glu-relu-9L_run/checkpoint-1000/trainer_state.json
@@ -0,0 +1,424 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.06740815638692282,
+  "eval_steps": 200,
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diff --git a/out/glu-relu-9L_run/checkpoint-1500/tokenizer_config.json b/out/glu-relu-9L_run/checkpoint-1500/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
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+}
diff --git a/out/glu-relu-9L_run/checkpoint-1500/trainer_state.json b/out/glu-relu-9L_run/checkpoint-1500/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..4185050d224d394cea0ff1d6d8b3a0954588fede
--- /dev/null
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diff --git a/out/glu-relu-9L_run/checkpoint-2000/tokenizer_config.json b/out/glu-relu-9L_run/checkpoint-2000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
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+}
diff --git a/out/glu-relu-9L_run/checkpoint-2000/trainer_state.json b/out/glu-relu-9L_run/checkpoint-2000/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..f667356949617d74139c066033ea8708a4729549
--- /dev/null
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diff --git a/out/glu-silu-9L_run/README.md b/out/glu-silu-9L_run/README.md
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+++ b/out/glu-silu-9L_run/README.md
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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+model-index:
+- name: ACTio-glu-silu-9L
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ACTio-glu-silu-9L
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2126
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: constant
+- training_steps: 2000
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 3.3162        | 0.0135 | 200  | 3.1383          |
+| 0.8013        | 0.0270 | 400  | 0.7547          |
+| 0.4088        | 0.0404 | 600  | 0.4044          |
+| 0.3265        | 0.0539 | 800  | 0.3272          |
+| 0.2864        | 0.0674 | 1000 | 0.2859          |
+| 0.2629        | 0.0809 | 1200 | 0.2619          |
+| 0.2403        | 0.0944 | 1400 | 0.2424          |
+| 0.2472        | 0.1079 | 1600 | 0.2468          |
+| 0.2213        | 0.1213 | 1800 | 0.2201          |
+| 0.2111        | 0.1348 | 2000 | 0.2126          |
+
+
+### Framework versions
+
+- Transformers 5.15.0.dev0
+- Pytorch 2.6.0+cu124
+- Datasets 5.0.1
+- Tokenizers 0.22.2
diff --git a/out/glu-silu-9L_run/checkpoint-1000/config.json b/out/glu-silu-9L_run/checkpoint-1000/config.json
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+{
+  "activation": "silu",
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+    "TinyLlamaForCausalLM"
+  ],
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+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 32,
+  "hidden_act": "silu",
+  "hidden_size": 128,
+  "initializer_range": 0.02,
+  "intermediate_size": 256,
+  "max_position_embeddings": 512,
+  "mlp_bias": false,
+  "mlp_type": "glu",
+  "model_type": "tiny_llama",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 9,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "tokenizer_name": "w-ahmad/tiny-stories-tokenizer",
+  "transformers_version": "5.15.0.dev0",
+  "use_cache": false,
+  "vocab_size": 4096
+}
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diff --git a/out/glu-silu-9L_run/checkpoint-1000/tokenizer_config.json b/out/glu-silu-9L_run/checkpoint-1000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
+++ b/out/glu-silu-9L_run/checkpoint-1000/tokenizer_config.json
@@ -0,0 +1,13 @@
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+}
diff --git a/out/glu-silu-9L_run/checkpoint-1000/trainer_state.json b/out/glu-silu-9L_run/checkpoint-1000/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..2b42c965f14377e69f3ea067c62022dac553b599
--- /dev/null
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diff --git a/out/glu-silu-9L_run/checkpoint-1500/tokenizer_config.json b/out/glu-silu-9L_run/checkpoint-1500/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
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+}
diff --git a/out/glu-silu-9L_run/checkpoint-1500/trainer_state.json b/out/glu-silu-9L_run/checkpoint-1500/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..1d861acd4101cd007e4c79592bda326d13faab38
--- /dev/null
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diff --git a/out/glu-silu-9L_run/checkpoint-2000/tokenizer_config.json b/out/glu-silu-9L_run/checkpoint-2000/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
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+}
diff --git a/out/glu-silu-9L_run/checkpoint-2000/trainer_state.json b/out/glu-silu-9L_run/checkpoint-2000/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b50b5495976c587d0be7082a95e2ded11423127
--- /dev/null
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+      [
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+      [
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+        "Ġbe",
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+        "Ġro",
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+      [
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+        "box"
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+        "Ġmess",
+        "age"
+      ],
+      [
+        "ĠJoe",
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+      [
+        "Ġcomp",
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+      [
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+      [
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+        "ndma"
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+      [
+        "Ġpil",
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+      [
+        "Ġpor",
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+      [
+        "l",
+        "er"
+      ],
+      [
+        "s",
+        "es"
+      ]
+    ]
+  }
+}
\ No newline at end of file
diff --git a/out/glu-silu-9L_run/checkpoint-500/tokenizer_config.json b/out/glu-silu-9L_run/checkpoint-500/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1578d3b6947e13e4001d559b79d4944be52b9c1
--- /dev/null
+++ b/out/glu-silu-9L_run/checkpoint-500/tokenizer_config.json
@@ -0,0 +1,13 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}
diff --git a/out/glu-silu-9L_run/checkpoint-500/trainer_state.json b/out/glu-silu-9L_run/checkpoint-500/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..ea3b91ecb333857452bff6431e459e88bbc11c65
--- /dev/null
+++ b/out/glu-silu-9L_run/checkpoint-500/trainer_state.json
@@ -0,0 +1,225 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.03370407819346141,
+  "eval_steps": 200,
+  "global_step": 500,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
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+      "grad_norm": 1.2578125,
+      "learning_rate": 0.0003,
+      "loss": 7.793735504150391,
+      "step": 20
+    },
+    {
+      "epoch": 0.002696326255476913,
+      "grad_norm": 1.1796875,
+      "learning_rate": 0.0003,
+      "loss": 7.022547912597656,
+      "step": 40
+    },
+    {
+      "epoch": 0.004044489383215369,
+      "grad_norm": 1.0,
+      "learning_rate": 0.0003,
+      "loss": 6.450266265869141,
+      "step": 60
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+{"step": 1840, "epoch": 0.12403100775193798, "timestamp": 1786048372.7096107, "loss": 0.21561825275421143, "grad_norm": 0.212890625, "learning_rate": 0.0003, "train/total_time_seconds": 56.13440829142928, "train/time_per_step_avg": 0.030602788478136064, "train/epoch_time_elapsed": 221.1990993283689, "train/estimated_remaining_minutes": 0.08135421491511491}
+{"step": 1860, "epoch": 0.12537917087967643, "timestamp": 1786048374.2976956, "loss": 0.21509158611297607, "grad_norm": 0.2158203125, "learning_rate": 0.0003, "train/total_time_seconds": 56.74812373146415, "train/time_per_step_avg": 0.030761414356529713, "train/epoch_time_elapsed": 222.78718342259526, "train/estimated_remaining_minutes": 0.07118940253051058}
+{"step": 1880, "epoch": 0.1267273340074149, "timestamp": 1786048375.8626852, "loss": 0.2144625186920166, "grad_norm": 0.2158203125, "learning_rate": 0.0003, "train/total_time_seconds": 57.35314633697271, "train/time_per_step_avg": 0.03083673045039177, "train/epoch_time_elapsed": 224.35217301174998, "train/estimated_remaining_minutes": 0.06101398546486459}
+{"step": 1900, "epoch": 0.12807549713515334, "timestamp": 1786048377.414862, "loss": 0.21395645141601563, "grad_norm": 0.220703125, "learning_rate": 0.0003, "train/total_time_seconds": 57.95155442506075, "train/time_per_step_avg": 0.030835555158555506, "train/epoch_time_elapsed": 225.90434988960624, "train/estimated_remaining_minutes": 0.050834696864088376}
+{"step": 1920, "epoch": 0.12942366026289182, "timestamp": 1786048378.9742465, "loss": 0.21237080097198485, "grad_norm": 0.2216796875, "learning_rate": 0.0003, "train/total_time_seconds": 58.55127212405205, "train/time_per_step_avg": 0.03024916835129261, "train/epoch_time_elapsed": 227.46373507753015, "train/estimated_remaining_minutes": 0.04066060564170281}
+{"step": 1940, "epoch": 0.13077182339063026, "timestamp": 1786048380.5286531, "loss": 0.21282241344451905, "grad_norm": 0.2177734375, "learning_rate": 0.0003, "train/total_time_seconds": 59.14938234910369, "train/time_per_step_avg": 0.03014974057674408, "train/epoch_time_elapsed": 229.01814108341932, "train/estimated_remaining_minutes": 0.030489372344898808}
+{"step": 1960, "epoch": 0.13211998651836873, "timestamp": 1786048382.0777707, "loss": 0.21062977313995362, "grad_norm": 0.212890625, "learning_rate": 0.0003, "train/total_time_seconds": 59.74724072962999, "train/time_per_step_avg": 0.029991169981658457, "train/epoch_time_elapsed": 230.56725925952196, "train/estimated_remaining_minutes": 0.02032219072436394}
+{"step": 1980, "epoch": 0.13346814964610718, "timestamp": 1786048383.6284516, "loss": 0.2115839719772339, "grad_norm": 0.21875, "learning_rate": 0.0003, "train/total_time_seconds": 60.34360493719578, "train/time_per_step_avg": 0.029904586002230643, "train/epoch_time_elapsed": 232.11793911457062, "train/estimated_remaining_minutes": 0.01015885605003296}
+{"step": 2000, "epoch": 0.13481631277384565, "timestamp": 1786048385.1866074, "loss": 0.21105265617370605, "grad_norm": 0.212890625, "learning_rate": 0.0003, "train/total_time_seconds": 60.94049885869026, "train/time_per_step_avg": 0.02988944433629513, "train/epoch_time_elapsed": 233.67609576135874, "train/estimated_remaining_minutes": 0.0}
+{"step": 2000, "epoch": 0.13481631277384565, "timestamp": 1786048393.7743077, "eval_loss": 0.21264217793941498, "eval_runtime": 8.5858, "eval_samples_per_second": 1109.629, "eval_steps_per_second": 17.354, "train/total_time_seconds": 60.94049885869026, "train/time_per_step_avg": 0.02988944433629513, "train/epoch_time_elapsed": 242.2637957520783, "train/estimated_remaining_minutes": 0.0}
+{"step": 2000, "epoch": 0.13481631277384565, "timestamp": 1786048393.8330839, "train_runtime": 243.167, "train_samples_per_second": 526.387, "train_steps_per_second": 8.225, "total_flos": 580776886272000.0, "train_loss": 0.9386080443859101, "train/total_time_seconds": 60.94049885869026, "train/time_per_step_avg": 0.02988944433629513, "train/epoch_time_elapsed": 242.32256953045726, "train/estimated_remaining_minutes": 0.0}
+{"step": 2000, "epoch": 0.13481631277384565, "timestamp": 1786048403.727387, "eval_loss": 0.21264217793941498, "eval_runtime": 8.5207, "eval_samples_per_second": 1118.101, "eval_steps_per_second": 17.487, "train/total_time_seconds": 60.94049885869026, "train/time_per_step_avg": 0.02988944433629513, "train/epoch_time_elapsed": 252.2168759033084, "train/estimated_remaining_minutes": 0.0}
diff --git a/out/sweep_summary.json b/out/sweep_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..bb9c8b1436c40754ee8a8097829fe6fad1a06474
--- /dev/null
+++ b/out/sweep_summary.json
@@ -0,0 +1,8 @@
+[
+  {
+    "variant": "glu-gelu-9L",
+    "eval_loss": 0.21510063111782074,
+    "out": "out/glu-gelu-9L_run",
+    "run_name": "LM-glu-gelu-9L-2.0M-20260806-203757"
+  }
+]
\ No newline at end of file
diff --git a/sweep.py b/sweep.py
new file mode 100644
index 0000000000000000000000000000000000000000..df304cc6312dc0931c0b5e2b1f8290b9361ca3f4
--- /dev/null
+++ b/sweep.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+"""Sweep explicit GLU / MLP variants with identical data and hyperparameters."""
+import argparse
+import copy
+import json
+import re
+import time
+import os
+from pathlib import Path
+
+import yaml
+import wandb
+import torch
+from transformers import AutoTokenizer, set_seed
+from exp import TinyLlamaConfig, TinyLlamaForCausalLM, build_dataset, create_trainer
+
+
+def format_param_count(total_params: int) -> str:
+    """Return human‑readable string with M or B suffix, 1 decimal."""
+    if total_params >= 1e9:
+        return f"{total_params / 1e9:.1f}B"
+    else:
+        return f"{total_params / 1e6:.1f}M"
+
+
+def parse_variant(variant: str):
+    """
+    Parse variant string into (prefix, activation, layers).
+    Formats:
+        glu-silu        -> ('glu', 'silu', None)
+        mlp-s10-10L     -> ('mlp', 's10', 10)
+        glu-relu-8L     -> ('glu', 'relu', 8)
+    """
+    # Pattern: optional layers at end with 'L'
+    match = re.fullmatch(r'(glu|mlp)-([a-zA-Z0-9]+)(?:-(\d+)L)?', variant)
+    if not match:
+        raise ValueError(
+            f"Invalid variant format: '{variant}'. "
+            "Expected: <glu|mlp>-<activation>[-<layers>L] e.g. glu-silu-10L"
+        )
+    prefix, act, layers_str = match.groups()
+    layers = int(layers_str) if layers_str is not None else None
+    return prefix, act, layers
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", required=True, help="Base YAML config")
+    parser.add_argument(
+        "--variants",
+        nargs="+",
+        required=True,
+        help="List of variants: e.g. glu-silu-10L mlp-relu-8L"
+    )
+    parser.add_argument("--push", action="store_true")
+    args = parser.parse_args()
+
+    with open(args.config) as f:
+        base = yaml.safe_load(f)
+
+    seed = base.get("training", {}).get("seed", 42)
+    set_seed(seed)
+
+    tok_name = base["model"].get("tokenizer_name", "meta-llama/Llama-2-7b-hf")
+    tokenizer = AutoTokenizer.from_pretrained(tok_name)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    msl = base["model"].get("max_position_embeddings", 512)
+    train_ds = build_dataset(tokenizer, max_seq_len=msl, split="train")
+    eval_ds = build_dataset(tokenizer, max_seq_len=msl, split="validation")
+
+    results = []
+
+    for variant in args.variants:
+        prefix, act, layers = parse_variant(variant)
+
+        # Validation: mlp-situglu is banned
+        if prefix == "mlp" and act == "situglu":
+            raise ValueError(
+                f"Activation 'situglu' requires a gated architecture (GLU). "
+                f"Please use 'glu-situglu' instead."
+            )
+
+        # Build config overrides
+        cfg = copy.deepcopy(base)
+        cfg["model"]["mlp_type"] = prefix
+        cfg["model"]["activation"] = act
+        if layers is not None:
+            cfg["model"]["num_hidden_layers"] = layers
+
+        # Create a descriptive suffix for folders / run names
+        layer_suffix = f"-{layers}L" if layers is not None else ""
+        variant_label = f"{prefix}-{act}{layer_suffix}"
+
+        # Unique output directory
+        out_dir = Path(cfg["training"]["output_dir"]).parent / f"{variant_label}_run"
+        cfg["training"]["output_dir"] = str(out_dir)
+
+        # Re-seed for reproducibility across variants
+        set_seed(seed)
+
+        print(f"\n{'='*60}\n>>> Variant: {variant_label} | Out: {out_dir}\n{'='*60}")
+
+        # Instantiate model to get parameter count BEFORE trainer
+        config = TinyLlamaConfig(**cfg["model"])
+        # Use SDPA backend (or flash_attention_2 if you have it)
+        config._attn_implementation = "sdpa"   # consistent across runs
+        model = TinyLlamaForCausalLM(config)
+        model = model.to(torch.bfloat16)
+
+        total_params = sum(p.numel() for p in model.parameters())
+        param_str = format_param_count(total_params)
+        timestamp = time.strftime("%Y%m%d-%H%M%S")
+        run_name = f"LM-{variant_label}-{param_str}-{timestamp}"
+        cfg["training"]["run_name"] = run_name
+
+        # Also update hub_model_id to include variant and layers
+        hub_id_base = cfg["training"].get("hub_model_id", "tiny-llama-lab")
+        cfg["training"]["hub_model_id"] = f"{hub_id_base}-{variant_label}"
+
+        # Ensure a fresh WandB run – remove any global WANDB_RUN_ID
+        os.environ.pop("WANDB_RUN_ID", None)
+
+        trainer = create_trainer(model, tokenizer, cfg, train_ds, eval_ds)
+
+        try:
+            trainer.train()
+            metrics = trainer.evaluate()
+            results.append({
+                "variant": variant_label,
+                "eval_loss": metrics.get("eval_loss"),
+                "out": str(out_dir),
+                "run_name": run_name,
+            })
+            trainer.save_model(str(out_dir))
+            if args.push or cfg["training"].get("push_to_hub", False):
+                trainer.push_to_hub()
+        finally:
+            # Explicitly finish WandB run to avoid re‑using the same run
+            wandb.finish()
+
+    # Save summary
+    summary = Path(base["training"]["output_dir"]).parent / "sweep_summary.json"
+    summary.write_text(json.dumps(results, indent=2))
+    print("\nSweep complete:")
+    for r in results:
+        print(f"  {r['variant']:20s}  eval_loss={r['eval_loss']:.4f}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2bd7a59a38e56164d23497de6dec84d94745e31
--- /dev/null
+++ b/train.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+"""Train one TinyLlama variant from a YAML config."""
+import argparse
+import yaml
+import torch
+
+from transformers import AutoTokenizer, set_seed
+from exp import TinyLlamaConfig, TinyLlamaForCausalLM, build_dataset, create_trainer
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", required=True, help="Path to YAML config")
+    parser.add_argument("--push", action="store_true", help="Push final model to HF Hub")
+    args = parser.parse_args()
+
+    with open(args.config) as f:
+        cfg = yaml.safe_load(f)
+
+    # Explicit seed before any randomness
+    seed = cfg.get("training", {}).get("seed", 42)
+    set_seed(seed)
+
+    model_cfg = cfg["model"]
+    train_cfg = cfg.get("training", {})
+
+    # Tokenizer
+    tok_name = model_cfg.pop("tokenizer_name", "meta-llama/Llama-2-7b-hf")
+    tokenizer = AutoTokenizer.from_pretrained(tok_name)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    # Model – the config must contain mlp_type and activation
+    tiny_config = TinyLlamaConfig(**model_cfg)
+    # Choose backend: "sdpa" (default, fast) or "flash_attention_2" (if installed)
+    tiny_config._attn_implementation = "sdpa"
+    model = TinyLlamaForCausalLM(tiny_config)
+    model = model.to(torch.bfloat16)
+
+    n_params = sum(p.numel() for p in model.parameters()) / 1e6
+    print(f"Model: {n_params:.2f}M params | MLP type: {tiny_config.mlp_type} | Activation: {tiny_config.activation}")
+    print(f"Attention implementation: {model.config._attn_implementation}")
+
+    # Optional: verify backend if you expect a specific one
+    # assert model.config._attn_implementation == "sdpa", "Backend mismatch"
+
+    # Data
+    msl = model_cfg.get("max_position_embeddings", 512)
+    train_ds = build_dataset(tokenizer, max_seq_len=msl, split="train")
+    eval_ds = build_dataset(tokenizer, max_seq_len=msl, split="validation")
+
+    # Train
+    trainer = create_trainer(model, tokenizer, cfg, train_ds, eval_ds)
+    trainer.train()
+
+    # Save & push
+    out = train_cfg.get("output_dir", "./out")
+    trainer.save_model(out)
+    if args.push or train_cfg.get("push_to_hub", False):
+        trainer.push_to_hub()
+    print(f"Done. Artifacts in {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/wandb/debug-internal.log b/wandb/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..5776a2f560e50179f29d335117c50629ab3ebcaf
--- /dev/null
+++ b/wandb/debug-internal.log
@@ -0,0 +1,33 @@
+{"time":"2026-08-06T20:42:21.543464314Z","level":"INFO","msg":"wandb-core"}
+{"time":"2026-08-06T20:42:21.543641595Z","level":"INFO","msg":"stream: starting","core version":"0.28.1"}
+{"time":"2026-08-06T20:42:21.803418363Z","level":"INFO","msg":"stream: created new stream","id":"wp360ttn"}
+{"time":"2026-08-06T20:42:21.803501768Z","level":"INFO","msg":"handler: started"}
+{"time":"2026-08-06T20:42:21.80359043Z","level":"INFO","msg":"stream: started"}
+{"time":"2026-08-06T20:42:21.803604162Z","level":"INFO","msg":"writer: started","stream_id":"wp360ttn"}
+{"time":"2026-08-06T20:42:21.80365358Z","level":"INFO","msg":"sender: started"}
+{"time":"2026-08-06T20:42:22.265785034Z","level":"INFO","msg":"filestream: sending request","total_files":1,"console_offset":0,"console_lines":1}
+{"time":"2026-08-06T20:42:22.362573435Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-08-06T20:42:24.460541572Z","level":"INFO","msg":"flowcontrol: backed up, offloading to disk","recordNumber":65}
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index 0000000000000000000000000000000000000000..6f22199b271ae9f37bf0a83935d0221549b19264
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+                cudaVersion: "12.4"
+                disk:
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+                        used: "703687757824"
+                email: deepnevro@gmail.com
+                executable: /mnt/data/zainulabideen/zain-exp/notebooks/my_env/bin/python
+                git:
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+                    remote: https://github.com/deepnevro/Activation.git
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+                      memoryTotal: "85520809984"
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+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
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+                      memoryTotal: "85520809984"
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+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-bfa16575-1d94-1aa2-4537-2c93433f42ef
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+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
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+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
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+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-1c4d29a2-4647-6fce-d8fc-0c5ecfbbd6ea
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+                memory:
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+                os: Linux-5.15.0-126-generic-x86_64-with-glibc2.35
+                program: /mnt/data/zainulabideen/zain-exp/notebooks/Activation/sweep.py
+                python: CPython 3.11.15
+                root: /mnt/data/zainulabideen/zain-exp/notebooks/Activation
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+save_on_each_node:
+    value: false
+save_only_model:
+    value: false
+save_steps:
+    value: 500
+save_strategy:
+    value: steps
+save_total_limit:
+    value: null
+seed:
+    value: 42
+skip_memory_metrics:
+    value: true
+tf32:
+    value: null
+tie_word_embeddings:
+    value: true
+tokenizer_name:
+    value: w-ahmad/tiny-stories-tokenizer
+torch_compile:
+    value: false
+torch_compile_backend:
+    value: null
+torch_compile_mode:
+    value: null
+torch_empty_cache_steps:
+    value: null
+trackio_bucket_id:
+    value: null
+trackio_space_id:
+    value: null
+trackio_static_space_id:
+    value: null
+train_sampling_strategy:
+    value: random
+transformers_version:
+    value: 5.15.0.dev0
+use_cache:
+    value: false
+use_cpu:
+    value: false
+use_liger_kernel:
+    value: false
+vocab_size:
+    value: 4096
+warmup_steps:
+    value: 0
+weight_decay:
+    value: 0.01
diff --git a/wandb/run-20260806_202910-50qq810b/files/output.log b/wandb/run-20260806_202910-50qq810b/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..038e4c0978d7f83dffcdbd845633ec46840665e6
--- /dev/null
+++ b/wandb/run-20260806_202910-50qq810b/files/output.log
@@ -0,0 +1,172 @@
+[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 25%|█████████▊                             | 500/2000 [00:56<01:55, 12.99it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '7.794', 'grad_norm': '1.258', 'learning_rate': '0.0003', 'epoch': '0.001348', 'train/total_time_seconds': '1.244', 'train/time_per_step_avg': '0.06218', 'train/epoch_time_elapsed': '2.352', 'train/estimated_remaining_minutes': '2.052', 'train/global/act/norm': '1.026e+04', 'train/global/act/mean': '0.007169', 'train/global/act/std': '0', 'train/global/act/max_abs': '8.327', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '2.188', 'train/global/grad/mean': '3.833e-07', 'train/global/grad/std': '0.001548', 'train/global/grad/max_abs': '0.1748', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '56.84', 'train/global/param/mean': '0.001193', 'train/global/param/std': '0.04018', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '17.93', 'train/layer__model_layers_0/param/mean': '0.001573', 'train/layer__model_layers_0/param/std': '0.04425', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '3150', 'train/layer_model_layers_5/act/mean': '0.01258', 'train/layer_model_layers_5/act/std': '0.411', 'train/layer_model_layers_5/act/max_abs': '4.406', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.4721', 'train/layer_model_layers_5/grad/mean': '-1.767e-06', 'train/layer_model_layers_5/grad/std': '0.001165', 'train/layer_model_layers_5/grad/max_abs': '0.02173', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '17.93', 'train/layer__model_layers_2/param/mean': '0.001509', 'train/layer__model_layers_2/param/std': '0.04425', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '17.93', 'train/layer__model_layers_6/param/mean': '0.001631', 'train/layer__model_layers_6/param/std': '0.04423', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '3158', 'train/layer_model_layers_7/act/mean': '0.004433', 'train/layer_model_layers_7/act/std': '0.4127', 'train/layer_model_layers_7/act/max_abs': '4.469', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.3769', 'train/layer_model_layers_7/grad/mean': '7.89e-07', 'train/layer_model_layers_7/grad/std': '0.0009305', 'train/layer_model_layers_7/grad/max_abs': '0.02124', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer__model_layers_1/param/norm': '17.93', 'train/layer__model_layers_1/param/mean': '0.001504', 'train/layer__model_layers_1/param/std': '0.04425', 'train/layer__model_layers_1/param/max_abs': '1', 'train/layer__model_layers_1/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_1/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '3146', 'train/layer_model_layers_2/act/mean': '0.004279', 'train/layer_model_layers_2/act/std': '0.4101', 'train/layer_model_layers_2/act/max_abs': '5.094', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.9033', 'train/layer_model_layers_2/grad/mean': '4.024e-06', 'train/layer_model_layers_2/grad/std': '0.00223', 'train/layer_model_laye
+{'loss': '7.023', 'grad_norm': '1.18', 'learning_rate': '0.0003', 'epoch': '0.002696', 'train/total_time_seconds': '1.855', 'train/time_per_step_avg': '0.04638', 'train/epoch_time_elapsed': '3.943', 'train/estimated_remaining_minutes': '1.515'}
+{'loss': '6.45', 'grad_norm': '1', 'learning_rate': '0.0003', 'epoch': '0.004044', 'train/total_time_seconds': '2.445', 'train/time_per_step_avg': '0.04075', 'train/epoch_time_elapsed': '5.533', 'train/estimated_remaining_minutes': '1.318'}
+{'loss': '6.004', 'grad_norm': '1.961', 'learning_rate': '0.0003', 'epoch': '0.005393', 'train/total_time_seconds': '3.034', 'train/time_per_step_avg': '0.03792', 'train/epoch_time_elapsed': '7.064', 'train/estimated_remaining_minutes': '1.213'}
+{'loss': '5.463', 'grad_norm': '1.875', 'learning_rate': '0.0003', 'epoch': '0.006741', 'train/total_time_seconds': '3.612', 'train/time_per_step_avg': '0.03612', 'train/epoch_time_elapsed': '8.583', 'train/estimated_remaining_minutes': '1.144'}
+{'loss': '4.925', 'grad_norm': '2.969', 'learning_rate': '0.0003', 'epoch': '0.008089', 'train/total_time_seconds': '4.191', 'train/time_per_step_avg': '0.02947', 'train/epoch_time_elapsed': '10.11', 'train/estimated_remaining_minutes': '1.094'}
+{'loss': '4.452', 'grad_norm': '2.641', 'learning_rate': '0.0003', 'epoch': '0.009437', 'train/total_time_seconds': '4.77', 'train/time_per_step_avg': '0.02915', 'train/epoch_time_elapsed': '11.63', 'train/estimated_remaining_minutes': '1.056'}
+{'loss': '4.057', 'grad_norm': '2.562', 'learning_rate': '0.0003', 'epoch': '0.01079', 'train/total_time_seconds': '5.353', 'train/time_per_step_avg': '0.02907', 'train/epoch_time_elapsed': '13.15', 'train/estimated_remaining_minutes': '1.026'}
+{'loss': '3.684', 'grad_norm': '2.328', 'learning_rate': '0.0003', 'epoch': '0.01213', 'train/total_time_seconds': '5.934', 'train/time_per_step_avg': '0.029', 'train/epoch_time_elapsed': '14.67', 'train/estimated_remaining_minutes': '1'}
+{'loss': '3.316', 'grad_norm': '2.688', 'learning_rate': '0.0003', 'epoch': '0.01348', 'train/total_time_seconds': '6.52', 'train/time_per_step_avg': '0.02908', 'train/epoch_time_elapsed': '16.21', 'train/estimated_remaining_minutes': '0.978'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '3.138', 'eval_runtime': '8.308', 'eval_samples_per_second': '1147', 'eval_steps_per_second': '17.93', 'epoch': '0.01348', 'train/total_time_seconds': '6.52', 'train/time_per_step_avg': '0.02908', 'train/epoch_time_elapsed': '24.52', 'train/estimated_remaining_minutes': '0.978'}
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+{'loss': '2.621', 'grad_norm': '2.375', 'learning_rate': '0.0003', 'epoch': '0.01618', 'train/total_time_seconds': '7.735', 'train/time_per_step_avg': '0.02964', 'train/epoch_time_elapsed': '27.68', 'train/estimated_remaining_minutes': '0.9454'}
+{'loss': '2.286', 'grad_norm': '2.047', 'learning_rate': '0.0003', 'epoch': '0.01753', 'train/total_time_seconds': '8.325', 'train/time_per_step_avg': '0.02973', 'train/epoch_time_elapsed': '29.25', 'train/estimated_remaining_minutes': '0.9286'}
+{'loss': '1.95', 'grad_norm': '2.609', 'learning_rate': '0.0003', 'epoch': '0.01887', 'train/total_time_seconds': '8.918', 'train/time_per_step_avg': '0.02984', 'train/epoch_time_elapsed': '30.79', 'train/estimated_remaining_minutes': '0.913'}
+{'loss': '1.661', 'grad_norm': '1.539', 'learning_rate': '0.0003', 'epoch': '0.02022', 'train/total_time_seconds': '9.501', 'train/time_per_step_avg': '0.02981', 'train/epoch_time_elapsed': '32.32', 'train/estimated_remaining_minutes': '0.8973'}
+{'loss': '1.425', 'grad_norm': '2.578', 'learning_rate': '0.0003', 'epoch': '0.02157', 'train/total_time_seconds': '10.1', 'train/time_per_step_avg': '0.02942', 'train/epoch_time_elapsed': '33.88', 'train/estimated_remaining_minutes': '0.8837'}
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+{'loss': '1.076', 'grad_norm': '1.297', 'learning_rate': '0.0003', 'epoch': '0.02427', 'train/total_time_seconds': '11.28', 'train/time_per_step_avg': '0.02956', 'train/epoch_time_elapsed': '36.96', 'train/estimated_remaining_minutes': '0.8565'}
+{'loss': '0.9284', 'grad_norm': '1.445', 'learning_rate': '0.0003', 'epoch': '0.02562', 'train/total_time_seconds': '11.87', 'train/time_per_step_avg': '0.02947', 'train/epoch_time_elapsed': '38.48', 'train/estimated_remaining_minutes': '0.8431'}
+{'loss': '0.8013', 'grad_norm': '0.9492', 'learning_rate': '0.0003', 'epoch': '0.02696', 'train/total_time_seconds': '12.45', 'train/time_per_step_avg': '0.02947', 'train/epoch_time_elapsed': '40.01', 'train/estimated_remaining_minutes': '0.8299'}
+{'eval_loss': '0.7547', 'eval_runtime': '8.287', 'eval_samples_per_second': '1150', 'eval_steps_per_second': '17.98', 'epoch': '0.02696', 'train/total_time_seconds': '12.45', 'train/time_per_step_avg': '0.02947', 'train/epoch_time_elapsed': '48.29', 'train/estimated_remaining_minutes': '0.8299'}
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+{'loss': '0.6362', 'grad_norm': '0.4805', 'learning_rate': '0.0003', 'epoch': '0.02966', 'train/total_time_seconds': '13.68', 'train/time_per_step_avg': '0.02987', 'train/epoch_time_elapsed': '51.46', 'train/estimated_remaining_minutes': '0.8086'}
+{'loss': '0.5719', 'grad_norm': '0.4062', 'learning_rate': '0.0003', 'epoch': '0.03101', 'train/total_time_seconds': '14.27', 'train/time_per_step_avg': '0.02986', 'train/epoch_time_elapsed': '52.98', 'train/estimated_remaining_minutes': '0.7961'}
+{'loss': '0.5331', 'grad_norm': '0.3887', 'learning_rate': '0.0003', 'epoch': '0.03236', 'train/total_time_seconds': '14.87', 'train/time_per_step_avg': '0.03005', 'train/epoch_time_elapsed': '54.59', 'train/estimated_remaining_minutes': '0.7848'}
+{'loss': '0.4973', 'grad_norm': '0.4609', 'learning_rate': '0.0003', 'epoch': '0.0337', 'train/total_time_seconds': '15.46', 'train/time_per_step_avg': '0.0301', 'train/epoch_time_elapsed': '56.13', 'train/estimated_remaining_minutes': '0.7729'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 132.83it/s]
+ 30%|███████████▋                           | 600/2000 [01:13<01:48, 12.87it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.4724', 'grad_norm': '0.4238', 'learning_rate': '0.0003', 'epoch': '0.03505', 'train/total_time_seconds': '16.05', 'train/time_per_step_avg': '0.02959', 'train/epoch_time_elapsed': '57.73', 'train/estimated_remaining_minutes': '0.7613'}
+{'loss': '0.4475', 'grad_norm': '0.3652', 'learning_rate': '0.0003', 'epoch': '0.0364', 'train/total_time_seconds': '16.64', 'train/time_per_step_avg': '0.02954', 'train/epoch_time_elapsed': '59.26', 'train/estimated_remaining_minutes': '0.7497'}
+{'loss': '0.4345', 'grad_norm': '0.4434', 'learning_rate': '0.0003', 'epoch': '0.03775', 'train/total_time_seconds': '17.24', 'train/time_per_step_avg': '0.02974', 'train/epoch_time_elapsed': '60.86', 'train/estimated_remaining_minutes': '0.7389'}
+{'loss': '0.4183', 'grad_norm': '0.3711', 'learning_rate': '0.0003', 'epoch': '0.0391', 'train/total_time_seconds': '17.84', 'train/time_per_step_avg': '0.02968', 'train/epoch_time_elapsed': '62.42', 'train/estimated_remaining_minutes': '0.7279'}
+{'loss': '0.4088', 'grad_norm': '0.3613', 'learning_rate': '0.0003', 'epoch': '0.04044', 'train/total_time_seconds': '18.44', 'train/time_per_step_avg': '0.02979', 'train/epoch_time_elapsed': '63.97', 'train/estimated_remaining_minutes': '0.717'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.4044', 'eval_runtime': '8.483', 'eval_samples_per_second': '1123', 'eval_steps_per_second': '17.57', 'epoch': '0.04044', 'train/total_time_seconds': '18.44', 'train/time_per_step_avg': '0.02979', 'train/epoch_time_elapsed': '72.46', 'train/estimated_remaining_minutes': '0.717'}
+ 50%|███████████████████                   | 1000/2000 [02:01<01:16, 13.04it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.3957', 'grad_norm': '0.4316', 'learning_rate': '0.0003', 'epoch': '0.04179', 'train/total_time_seconds': '19.09', 'train/time_per_step_avg': '0.0304', 'train/epoch_time_elapsed': '74.08', 'train/estimated_remaining_minutes': '0.7081', 'train/global/act/norm': '2.763e+04', 'train/global/act/mean': '-0.2299', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.5', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.4065', 'train/global/grad/mean': '-9.941e-07', 'train/global/grad/std': '0.0002872', 'train/global/grad/max_abs': '0.03198', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '72.97', 'train/global/param/mean': '0.001699', 'train/global/param/std': '0.05161', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.46', 'train/layer__model_layers_0/param/mean': '0.001547', 'train/layer__model_layers_0/param/std': '0.04555', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '6672', 'train/layer_model_layers_5/act/mean': '0.02066', 'train/layer_model_layers_5/act/std': '0.8701', 'train/layer_model_layers_5/act/max_abs': '5.438', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.08592', 'train/layer_model_layers_5/grad/mean': '-1.081e-07', 'train/layer_model_layers_5/grad/std': '0.0002122', 'train/layer_model_layers_5/grad/max_abs': '0.003189', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.44', 'train/layer__model_layers_2/param/mean': '0.001464', 'train/layer__model_layers_2/param/std': '0.0455', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.72', 'train/layer__model_layers_6/param/mean': '0.0017', 'train/layer__model_layers_6/param/std': '0.0462', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6439', 'train/layer_model_layers_7/act/mean': '0.02575', 'train/layer_model_layers_7/act/std': '0.8404', 'train/layer_model_layers_7/act/max_abs': '4.562', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.07927', 'train/layer_model_layers_7/grad/mean': '1.916e-08', 'train/layer_model_layers_7/grad/std': '0.0001958', 'train/layer_model_layers_7/grad/max_abs': '0.002762', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer__model_layers_1/param/norm': '18.35', 'train/layer__model_layers_1/param/mean': '0.001492', 'train/layer__model_layers_1/param/std': '0.04528', 'train/layer__model_layers_1/param/max_abs': '1', 'train/layer__model_layers_1/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_1/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5828', 'train/layer_model_layers_2/act/mean': '-0.0004916', 'train/layer_model_layers_2/act/std': '0.7605', 'train/layer_model_layers_2/act/max_abs': '5.438', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.1367', 'train/layer_model_layers_2/grad/mean': '7.483e-07', 'train/layer_model_layers_2/grad/std': '0.0003374', 'train/layer_m
+{'loss': '0.3884', 'grad_norm': '2.734', 'learning_rate': '0.0003', 'epoch': '0.04314', 'train/total_time_seconds': '19.68', 'train/time_per_step_avg': '0.03047', 'train/epoch_time_elapsed': '75.66', 'train/estimated_remaining_minutes': '0.6971'}
+{'loss': '0.3993', 'grad_norm': '1', 'learning_rate': '0.0003', 'epoch': '0.04449', 'train/total_time_seconds': '20.28', 'train/time_per_step_avg': '0.03041', 'train/epoch_time_elapsed': '77.2', 'train/estimated_remaining_minutes': '0.6863'}
+{'loss': '0.3756', 'grad_norm': '0.3867', 'learning_rate': '0.0003', 'epoch': '0.04584', 'train/total_time_seconds': '20.88', 'train/time_per_step_avg': '0.03038', 'train/epoch_time_elapsed': '78.73', 'train/estimated_remaining_minutes': '0.6754'}
+{'loss': '0.3619', 'grad_norm': '0.332', 'learning_rate': '0.0003', 'epoch': '0.04719', 'train/total_time_seconds': '21.47', 'train/time_per_step_avg': '0.03032', 'train/epoch_time_elapsed': '80.26', 'train/estimated_remaining_minutes': '0.6645'}
+{'loss': '0.355', 'grad_norm': '0.3027', 'learning_rate': '0.0003', 'epoch': '0.04853', 'train/total_time_seconds': '22.06', 'train/time_per_step_avg': '0.02974', 'train/epoch_time_elapsed': '81.8', 'train/estimated_remaining_minutes': '0.6537'}
+{'loss': '0.3448', 'grad_norm': '0.293', 'learning_rate': '0.0003', 'epoch': '0.04988', 'train/total_time_seconds': '22.66', 'train/time_per_step_avg': '0.02972', 'train/epoch_time_elapsed': '83.36', 'train/estimated_remaining_minutes': '0.6429'}
+{'loss': '0.3416', 'grad_norm': '0.2793', 'learning_rate': '0.0003', 'epoch': '0.05123', 'train/total_time_seconds': '23.26', 'train/time_per_step_avg': '0.02975', 'train/epoch_time_elapsed': '84.91', 'train/estimated_remaining_minutes': '0.6324'}
+{'loss': '0.3343', 'grad_norm': '0.2871', 'learning_rate': '0.0003', 'epoch': '0.05258', 'train/total_time_seconds': '23.85', 'train/time_per_step_avg': '0.02978', 'train/epoch_time_elapsed': '86.46', 'train/estimated_remaining_minutes': '0.6218'}
+{'loss': '0.3265', 'grad_norm': '0.3105', 'learning_rate': '0.0003', 'epoch': '0.05393', 'train/total_time_seconds': '24.46', 'train/time_per_step_avg': '0.02988', 'train/epoch_time_elapsed': '88.01', 'train/estimated_remaining_minutes': '0.6114'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.3272', 'eval_runtime': '8.415', 'eval_samples_per_second': '1132', 'eval_steps_per_second': '17.71', 'epoch': '0.05393', 'train/total_time_seconds': '24.46', 'train/time_per_step_avg': '0.02988', 'train/epoch_time_elapsed': '96.43', 'train/estimated_remaining_minutes': '0.6114'}
+{'loss': '0.3226', 'grad_norm': '0.3145', 'learning_rate': '0.0003', 'epoch': '0.05527', 'train/total_time_seconds': '25.11', 'train/time_per_step_avg': '0.03047', 'train/epoch_time_elapsed': '98.05', 'train/estimated_remaining_minutes': '0.6022', 'train/global/act/norm': '2.739e+04', 'train/global/act/mean': '-0.2199', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.69', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.29', 'train/global/grad/mean': '-7.857e-07', 'train/global/grad/std': '0.0002054', 'train/global/grad/max_abs': '0.02954', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '74.32', 'train/global/param/mean': '0.001692', 'train/global/param/std': '0.05262', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.48', 'train/layer__model_layers_0/param/mean': '0.00155', 'train/layer__model_layers_0/param/std': '0.04559', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '6881', 'train/layer_model_layers_5/act/mean': '0.02114', 'train/layer_model_layers_5/act/std': '0.8977', 'train/layer_model_layers_5/act/max_abs': '5.5', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.0501', 'train/layer_model_layers_5/grad/mean': '6.008e-08', 'train/layer_model_layers_5/grad/std': '0.0001236', 'train/layer_model_layers_5/grad/max_abs': '0.002106', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.47', 'train/layer__model_layers_2/param/mean': '0.001463', 'train/layer__model_layers_2/param/std': '0.04557', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.76', 'train/layer__model_layers_6/param/mean': '0.001706', 'train/layer__model_layers_6/param/std': '0.04628', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6495', 'train/layer_model_layers_7/act/mean': '0.02691', 'train/layer_model_layers_7/act/std': '0.8478', 'train/layer_model_layers_7/act/max_abs': '4.625', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.04351', 'train/layer_model_layers_7/grad/mean': '-1.407e-07', 'train/layer_model_layers_7/grad/std': '0.0001075', 'train/layer_model_layers_7/grad/max_abs': '0.001457', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer__model_layers_1/param/norm': '18.36', 'train/layer__model_layers_1/param/mean': '0.001493', 'train/layer__model_layers_1/param/std': '0.04532', 'train/layer__model_layers_1/param/max_abs': '1', 'train/layer__model_layers_1/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_1/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5927', 'train/layer_model_layers_2/act/mean': '0.0002659', 'train/layer_model_layers_2/act/std': '0.7732', 'train/layer_model_layers_2/act/max_abs': '5.531', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.05777', 'train/layer_model_layers_2/grad/mean': '-6.67e-07', 'train/layer_model_layers_2/grad/std': '0.0001426', 'train/layer_m
+{'loss': '0.3174', 'grad_norm': '0.2852', 'learning_rate': '0.0003', 'epoch': '0.05662', 'train/total_time_seconds': '25.7', 'train/time_per_step_avg': '0.03046', 'train/epoch_time_elapsed': '99.61', 'train/estimated_remaining_minutes': '0.5916'}
+{'loss': '0.3103', 'grad_norm': '0.3203', 'learning_rate': '0.0003', 'epoch': '0.05797', 'train/total_time_seconds': '26.29', 'train/time_per_step_avg': '0.03035', 'train/epoch_time_elapsed': '101.1', 'train/estimated_remaining_minutes': '0.5809'}
+{'loss': '0.3037', 'grad_norm': '0.2969', 'learning_rate': '0.0003', 'epoch': '0.05932', 'train/total_time_seconds': '26.9', 'train/time_per_step_avg': '0.0305', 'train/epoch_time_elapsed': '102.7', 'train/estimated_remaining_minutes': '0.5707'}
+{'loss': '0.3027', 'grad_norm': '0.375', 'learning_rate': '0.0003', 'epoch': '0.06067', 'train/total_time_seconds': '27.5', 'train/time_per_step_avg': '0.03045', 'train/epoch_time_elapsed': '104.3', 'train/estimated_remaining_minutes': '0.5602'}
+{'loss': '0.2994', 'grad_norm': '0.293', 'learning_rate': '0.0003', 'epoch': '0.06202', 'train/total_time_seconds': '28.1', 'train/time_per_step_avg': '0.02988', 'train/epoch_time_elapsed': '105.8', 'train/estimated_remaining_minutes': '0.5497'}
+{'loss': '0.2953', 'grad_norm': '0.3027', 'learning_rate': '0.0003', 'epoch': '0.06336', 'train/total_time_seconds': '28.69', 'train/time_per_step_avg': '0.02986', 'train/epoch_time_elapsed': '107.4', 'train/estimated_remaining_minutes': '0.5392'}
+{'loss': '0.2904', 'grad_norm': '0.3027', 'learning_rate': '0.0003', 'epoch': '0.06471', 'train/total_time_seconds': '29.28', 'train/time_per_step_avg': '0.02989', 'train/epoch_time_elapsed': '108.9', 'train/estimated_remaining_minutes': '0.5287'}
+{'loss': '0.2887', 'grad_norm': '0.373', 'learning_rate': '0.0003', 'epoch': '0.06606', 'train/total_time_seconds': '29.87', 'train/time_per_step_avg': '0.02969', 'train/epoch_time_elapsed': '110.5', 'train/estimated_remaining_minutes': '0.5182'}
+{'loss': '0.2864', 'grad_norm': '0.3105', 'learning_rate': '0.0003', 'epoch': '0.06741', 'train/total_time_seconds': '30.46', 'train/time_per_step_avg': '0.02961', 'train/epoch_time_elapsed': '112', 'train/estimated_remaining_minutes': '0.5077'}
+{'eval_loss': '0.2859', 'eval_runtime': '8.423', 'eval_samples_per_second': '1131', 'eval_steps_per_second': '17.69', 'epoch': '0.06741', 'train/total_time_seconds': '30.46', 'train/time_per_step_avg': '0.02961', 'train/epoch_time_elapsed': '120.4', 'train/estimated_remaining_minutes': '0.5077'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 122.01it/s]
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 75%|████████████████████████████▌         | 1500/2000 [02:57<00:38, 12.86it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
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+{'loss': '0.2775', 'grad_norm': '0.3379', 'learning_rate': '0.0003', 'epoch': '0.0701', 'train/total_time_seconds': '31.75', 'train/time_per_step_avg': '0.03061', 'train/epoch_time_elapsed': '123.8', 'train/estimated_remaining_minutes': '0.4885'}
+{'loss': '0.277', 'grad_norm': '0.3281', 'learning_rate': '0.0003', 'epoch': '0.07145', 'train/total_time_seconds': '32.35', 'train/time_per_step_avg': '0.03064', 'train/epoch_time_elapsed': '125.4', 'train/estimated_remaining_minutes': '0.4781'}
+{'loss': '0.2724', 'grad_norm': '0.3438', 'learning_rate': '0.0003', 'epoch': '0.0728', 'train/total_time_seconds': '32.95', 'train/time_per_step_avg': '0.03078', 'train/epoch_time_elapsed': '126.9', 'train/estimated_remaining_minutes': '0.4678'}
+{'loss': '0.2828', 'grad_norm': '4.562', 'learning_rate': '0.0003', 'epoch': '0.07415', 'train/total_time_seconds': '33.55', 'train/time_per_step_avg': '0.03085', 'train/epoch_time_elapsed': '128.5', 'train/estimated_remaining_minutes': '0.4575'}
+{'loss': '0.2863', 'grad_norm': '0.6445', 'learning_rate': '0.0003', 'epoch': '0.0755', 'train/total_time_seconds': '34.16', 'train/time_per_step_avg': '0.03014', 'train/epoch_time_elapsed': '130.1', 'train/estimated_remaining_minutes': '0.4473'}
+{'loss': '0.2692', 'grad_norm': '0.3008', 'learning_rate': '0.0003', 'epoch': '0.07685', 'train/total_time_seconds': '34.78', 'train/time_per_step_avg': '0.0303', 'train/epoch_time_elapsed': '131.7', 'train/estimated_remaining_minutes': '0.4373'}
+{'loss': '0.266', 'grad_norm': '0.2656', 'learning_rate': '0.0003', 'epoch': '0.07819', 'train/total_time_seconds': '35.38', 'train/time_per_step_avg': '0.03034', 'train/epoch_time_elapsed': '133.3', 'train/estimated_remaining_minutes': '0.427'}
+{'loss': '0.2647', 'grad_norm': '0.2988', 'learning_rate': '0.0003', 'epoch': '0.07954', 'train/total_time_seconds': '35.98', 'train/time_per_step_avg': '0.03026', 'train/epoch_time_elapsed': '134.8', 'train/estimated_remaining_minutes': '0.4167'}
+{'loss': '0.2629', 'grad_norm': '0.2773', 'learning_rate': '0.0003', 'epoch': '0.08089', 'train/total_time_seconds': '36.59', 'train/time_per_step_avg': '0.03041', 'train/epoch_time_elapsed': '136.4', 'train/estimated_remaining_minutes': '0.4066'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.2619', 'eval_runtime': '8.47', 'eval_samples_per_second': '1125', 'eval_steps_per_second': '17.59', 'epoch': '0.08089', 'train/total_time_seconds': '36.59', 'train/time_per_step_avg': '0.03041', 'train/epoch_time_elapsed': '144.9', 'train/estimated_remaining_minutes': '0.4066'}
+{'loss': '0.2583', 'grad_norm': '0.2715', 'learning_rate': '0.0003', 'epoch': '0.08224', 'train/total_time_seconds': '37.25', 'train/time_per_step_avg': '0.03088', 'train/epoch_time_elapsed': '146.5', 'train/estimated_remaining_minutes': '0.3969', 'train/global/act/norm': '2.734e+04', 'train/global/act/mean': '-0.2083', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.94', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.2675', 'train/global/grad/mean': '-7.067e-07', 'train/global/grad/std': '0.0001892', 'train/global/grad/max_abs': '0.02686', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '75.87', 'train/global/param/mean': '0.001699', 'train/global/param/std': '0.05367', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.51', 'train/layer__model_layers_0/param/mean': '0.001553', 'train/layer__model_layers_0/param/std': '0.04569', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '7065', 'train/layer_model_layers_5/act/mean': '0.02129', 'train/layer_model_layers_5/act/std': '0.9212', 'train/layer_model_layers_5/act/max_abs': '5.625', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.04526', 'train/layer_model_layers_5/grad/mean': '2.376e-07', 'train/layer_model_layers_5/grad/std': '0.0001118', 'train/layer_model_layers_5/grad/max_abs': '0.001602', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.51', 'train/layer__model_layers_2/param/mean': '0.001467', 'train/layer__model_layers_2/param/std': '0.04568', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.8', 'train/layer__model_layers_6/param/mean': '0.001711', 'train/layer__model_layers_6/param/std': '0.0464', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6606', 'train/layer_model_layers_7/act/mean': '0.02828', 'train/layer_model_layers_7/act/std': '0.8619', 'train/layer_model_layers_7/act/max_abs': '4.656', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.0391', 'train/layer_model_layers_7/grad/mean': '-4.031e-08', 'train/layer_model_layers_7/grad/std': '9.653e-05', 'train/layer_model_layers_7/grad/max_abs': '0.001999', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer__model_layers_1/param/norm': '18.39', 'train/layer__model_layers_1/param/mean': '0.001501', 'train/layer__model_layers_1/param/std': '0.04536', 'train/layer__model_layers_1/param/max_abs': '1', 'train/layer__model_layers_1/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_1/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6082', 'train/layer_model_layers_2/act/mean': '0.0008311', 'train/layer_model_layers_2/act/std': '0.7938', 'train/layer_model_layers_2/act/max_abs': '5.812', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.05441', 'train/layer_model_layers_2/grad/mean': '5.72e-07', 'train/layer_model_layers_2/grad/std': '0.0001343', 'train/layer
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+{'loss': '0.2549', 'grad_norm': '0.2598', 'learning_rate': '0.0003', 'epoch': '0.08493', 'train/total_time_seconds': '38.45', 'train/time_per_step_avg': '0.03073', 'train/epoch_time_elapsed': '149.7', 'train/estimated_remaining_minutes': '0.3764'}
+{'loss': '0.2501', 'grad_norm': '0.2617', 'learning_rate': '0.0003', 'epoch': '0.08628', 'train/total_time_seconds': '39.06', 'train/time_per_step_avg': '0.03079', 'train/epoch_time_elapsed': '151.2', 'train/estimated_remaining_minutes': '0.3662'}
+{'loss': '0.25', 'grad_norm': '0.2578', 'learning_rate': '0.0003', 'epoch': '0.08763', 'train/total_time_seconds': '39.66', 'train/time_per_step_avg': '0.03068', 'train/epoch_time_elapsed': '152.8', 'train/estimated_remaining_minutes': '0.3559'}
+{'loss': '0.2466', 'grad_norm': '0.2373', 'learning_rate': '0.0003', 'epoch': '0.08898', 'train/total_time_seconds': '40.27', 'train/time_per_step_avg': '0.03018', 'train/epoch_time_elapsed': '154.4', 'train/estimated_remaining_minutes': '0.3457'}
+{'loss': '0.2456', 'grad_norm': '0.25', 'learning_rate': '0.0003', 'epoch': '0.09033', 'train/total_time_seconds': '40.87', 'train/time_per_step_avg': '0.0302', 'train/epoch_time_elapsed': '156', 'train/estimated_remaining_minutes': '0.3355'}
+{'loss': '0.2432', 'grad_norm': '0.2637', 'learning_rate': '0.0003', 'epoch': '0.09168', 'train/total_time_seconds': '41.47', 'train/time_per_step_avg': '0.03015', 'train/epoch_time_elapsed': '157.5', 'train/estimated_remaining_minutes': '0.3253'}
+{'loss': '0.2427', 'grad_norm': '0.2539', 'learning_rate': '0.0003', 'epoch': '0.09302', 'train/total_time_seconds': '42.07', 'train/time_per_step_avg': '0.03011', 'train/epoch_time_elapsed': '159.1', 'train/estimated_remaining_minutes': '0.315'}
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+{'eval_loss': '0.2424', 'eval_runtime': '8.554', 'eval_samples_per_second': '1114', 'eval_steps_per_second': '17.42', 'epoch': '0.09437', 'train/total_time_seconds': '42.68', 'train/time_per_step_avg': '0.03024', 'train/epoch_time_elapsed': '169.2', 'train/estimated_remaining_minutes': '0.3049'}
+{'loss': '0.2401', 'grad_norm': '0.2891', 'learning_rate': '0.0003', 'epoch': '0.09572', 'train/total_time_seconds': '43.35', 'train/time_per_step_avg': '0.0308', 'train/epoch_time_elapsed': '170.9', 'train/estimated_remaining_minutes': '0.2951', 'train/global/act/norm': '2.743e+04', 'train/global/act/mean': '-0.2026', 'train/global/act/std': '0', 'train/global/act/max_abs': '11', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.3323', 'train/global/grad/mean': '-7.181e-07', 'train/global/grad/std': '0.0002348', 'train/global/grad/max_abs': '0.02551', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.38', 'train/global/param/mean': '0.001701', 'train/global/param/std': '0.05401', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.52', 'train/layer__model_layers_0/param/mean': '0.001552', 'train/layer__model_layers_0/param/std': '0.04569', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '7205', 'train/layer_model_layers_5/act/mean': '0.0226', 'train/layer_model_layers_5/act/std': '0.9394', 'train/layer_model_layers_5/act/max_abs': '5.656', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.08179', 'train/layer_model_layers_5/grad/mean': '5.582e-07', 'train/layer_model_layers_5/grad/std': '0.0002022', 'train/layer_model_layers_5/grad/max_abs': '0.002823', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.52', 'train/layer__model_layers_2/param/mean': '0.00147', 'train/layer__model_layers_2/param/std': '0.04569', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.81', 'train/layer__model_layers_6/param/mean': '0.00171', 'train/layer__model_layers_6/param/std': '0.0464', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6674', 'train/layer_model_layers_7/act/mean': '0.0305', 'train/layer_model_layers_7/act/std': '0.8701', 'train/layer_model_layers_7/act/max_abs': '4.656', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.07058', 'train/layer_model_layers_7/grad/mean': '4.922e-08', 'train/layer_model_layers_7/grad/std': '0.0001741', 'train/layer_model_layers_7/grad/max_abs': '0.003708', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer__model_layers_1/param/norm': '18.39', 'train/layer__model_layers_1/param/mean': '0.001502', 'train/layer__model_layers_1/param/std': '0.04538', 'train/layer__model_layers_1/param/max_abs': '1', 'train/layer__model_layers_1/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_1/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6136', 'train/layer_model_layers_2/act/mean': '0.001762', 'train/layer_model_layers_2/act/std': '0.8013', 'train/layer_model_layers_2/act/max_abs': '5.719', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.1005', 'train/layer_model_layers_2/grad/mean': '9.604e-07', 'train/layer_model_layers_2/grad/std': '0.0002482', 'train/layer_model_l
+{'loss': '0.2382', 'grad_norm': '0.2578', 'learning_rate': '0.0003', 'epoch': '0.09707', 'train/total_time_seconds': '43.95', 'train/time_per_step_avg': '0.03079', 'train/epoch_time_elapsed': '172.5', 'train/estimated_remaining_minutes': '0.2849'}
+{'loss': '0.2359', 'grad_norm': '0.252', 'learning_rate': '0.0003', 'epoch': '0.09842', 'train/total_time_seconds': '44.56', 'train/time_per_step_avg': '0.03086', 'train/epoch_time_elapsed': '174', 'train/estimated_remaining_minutes': '0.2747'}
+{'loss': '0.2341', 'grad_norm': '0.2539', 'learning_rate': '0.0003', 'epoch': '0.09976', 'train/total_time_seconds': '45.16', 'train/time_per_step_avg': '0.03087', 'train/epoch_time_elapsed': '175.6', 'train/estimated_remaining_minutes': '0.2644'}
+{'loss': '0.2331', 'grad_norm': '0.2637', 'learning_rate': '0.0003', 'epoch': '0.1011', 'train/total_time_seconds': '45.76', 'train/time_per_step_avg': '0.03073', 'train/epoch_time_elapsed': '177.1', 'train/estimated_remaining_minutes': '0.2542'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 131.64it/s]
+ 80%|██████████████████████████████▍       | 1600/2000 [03:14<00:32, 12.50it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.2312', 'grad_norm': '0.2559', 'learning_rate': '0.0003', 'epoch': '0.1025', 'train/total_time_seconds': '46.37', 'train/time_per_step_avg': '0.0302', 'train/epoch_time_elapsed': '178.8', 'train/estimated_remaining_minutes': '0.244'}
+{'loss': '0.2309', 'grad_norm': '0.2559', 'learning_rate': '0.0003', 'epoch': '0.1038', 'train/total_time_seconds': '46.98', 'train/time_per_step_avg': '0.03029', 'train/epoch_time_elapsed': '180.4', 'train/estimated_remaining_minutes': '0.2339'}
+{'loss': '0.23', 'grad_norm': '0.293', 'learning_rate': '0.0003', 'epoch': '0.1052', 'train/total_time_seconds': '47.58', 'train/time_per_step_avg': '0.03024', 'train/epoch_time_elapsed': '181.9', 'train/estimated_remaining_minutes': '0.2237'}
+{'loss': '0.2306', 'grad_norm': '0.2734', 'learning_rate': '0.0003', 'epoch': '0.1065', 'train/total_time_seconds': '48.19', 'train/time_per_step_avg': '0.03031', 'train/epoch_time_elapsed': '183.5', 'train/estimated_remaining_minutes': '0.2135'}
+{'loss': '0.2472', 'grad_norm': '1.141', 'learning_rate': '0.0003', 'epoch': '0.1079', 'train/total_time_seconds': '48.81', 'train/time_per_step_avg': '0.0305', 'train/epoch_time_elapsed': '185.2', 'train/estimated_remaining_minutes': '0.2034'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.2468', 'eval_runtime': '8.507', 'eval_samples_per_second': '1120', 'eval_steps_per_second': '17.52', 'epoch': '0.1079', 'train/total_time_seconds': '48.81', 'train/time_per_step_avg': '0.0305', 'train/epoch_time_elapsed': '193.7', 'train/estimated_remaining_minutes': '0.2034'}
+100%|██████████████████████████████████████| 2000/2000 [04:03<00:00, 12.85it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.2359', 'grad_norm': '0.4277', 'learning_rate': '0.0003', 'epoch': '0.1092', 'train/total_time_seconds': '49.47', 'train/time_per_step_avg': '0.03105', 'train/epoch_time_elapsed': '195.3', 'train/estimated_remaining_minutes': '0.1934', 'train/global/act/norm': '2.694e+04', 'train/global/act/mean': '-0.2181', 'train/global/act/std': '0', 'train/global/act/max_abs': '11', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '1.028', 'train/global/grad/mean': '-5.672e-07', 'train/global/grad/std': '0.0007263', 'train/global/grad/max_abs': '0.02515', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.74', 'train/global/param/mean': '0.001705', 'train/global/param/std': '0.05419', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.52', 'train/layer__model_layers_0/param/mean': '0.001554', 'train/layer__model_layers_0/param/std': '0.04569', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '6568', 'train/layer_model_layers_5/act/mean': '0.01905', 'train/layer_model_layers_5/act/std': '0.8572', 'train/layer_model_layers_5/act/max_abs': '5.219', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.3211', 'train/layer_model_layers_5/grad/mean': '-1.111e-06', 'train/layer_model_layers_5/grad/std': '0.0007923', 'train/layer_model_layers_5/grad/max_abs': '0.01208', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.52', 'train/layer__model_layers_2/param/mean': '0.001471', 'train/layer__model_layers_2/param/std': '0.04573', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.83', 'train/layer__model_layers_6/param/mean': '0.001707', 'train/layer__model_layers_6/param/std': '0.04644', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6263', 'train/layer_model_layers_7/act/mean': '0.02781', 'train/layer_model_layers_7/act/std': '0.8167', 'train/layer_model_layers_7/act/max_abs': '4.625', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.2856', 'train/layer_model_layers_7/grad/mean': '1.495e-06', 'train/layer_model_layers_7/grad/std': '0.0007046', 'train/layer_model_layers_7/grad/max_abs': '0.009766', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer__model_layers_1/param/norm': '18.39', 'train/layer__model_layers_1/param/mean': '0.001502', 'train/layer__model_layers_1/param/std': '0.04538', 'train/layer__model_layers_1/param/max_abs': '1', 'train/layer__model_layers_1/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_1/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5752', 'train/layer_model_layers_2/act/mean': '0.001814', 'train/layer_model_layers_2/act/std': '0.7494', 'train/layer_model_layers_2/act/max_abs': '5.344', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.3714', 'train/layer_model_layers_2/grad/mean': '5.695e-06', 'train/layer_model_layers_2/grad/std': '0.0009169', 'train/layer_model
+{'loss': '0.2293', 'grad_norm': '0.252', 'learning_rate': '0.0003', 'epoch': '0.1105', 'train/total_time_seconds': '50.08', 'train/time_per_step_avg': '0.03102', 'train/epoch_time_elapsed': '196.9', 'train/estimated_remaining_minutes': '0.1832'}
+{'loss': '0.2258', 'grad_norm': '0.2246', 'learning_rate': '0.0003', 'epoch': '0.1119', 'train/total_time_seconds': '50.68', 'train/time_per_step_avg': '0.03099', 'train/epoch_time_elapsed': '198.5', 'train/estimated_remaining_minutes': '0.173'}
+{'loss': '0.225', 'grad_norm': '0.2324', 'learning_rate': '0.0003', 'epoch': '0.1132', 'train/total_time_seconds': '51.28', 'train/time_per_step_avg': '0.03089', 'train/epoch_time_elapsed': '200', 'train/estimated_remaining_minutes': '0.1628'}
+{'loss': '0.2252', 'grad_norm': '0.2266', 'learning_rate': '0.0003', 'epoch': '0.1146', 'train/total_time_seconds': '51.87', 'train/time_per_step_avg': '0.03067', 'train/epoch_time_elapsed': '201.6', 'train/estimated_remaining_minutes': '0.1526'}
+{'loss': '0.2228', 'grad_norm': '0.2207', 'learning_rate': '0.0003', 'epoch': '0.1159', 'train/total_time_seconds': '52.47', 'train/time_per_step_avg': '0.02997', 'train/epoch_time_elapsed': '203.1', 'train/estimated_remaining_minutes': '0.1424'}
+{'loss': '0.2227', 'grad_norm': '0.2188', 'learning_rate': '0.0003', 'epoch': '0.1173', 'train/total_time_seconds': '53.07', 'train/time_per_step_avg': '0.02995', 'train/epoch_time_elapsed': '204.7', 'train/estimated_remaining_minutes': '0.1322'}
+{'loss': '0.2212', 'grad_norm': '0.2314', 'learning_rate': '0.0003', 'epoch': '0.1186', 'train/total_time_seconds': '53.67', 'train/time_per_step_avg': '0.02993', 'train/epoch_time_elapsed': '206.2', 'train/estimated_remaining_minutes': '0.122'}
+{'loss': '0.2217', 'grad_norm': '0.2168', 'learning_rate': '0.0003', 'epoch': '0.12', 'train/total_time_seconds': '54.27', 'train/time_per_step_avg': '0.02994', 'train/epoch_time_elapsed': '207.8', 'train/estimated_remaining_minutes': '0.1118'}
+{'loss': '0.2213', 'grad_norm': '0.2227', 'learning_rate': '0.0003', 'epoch': '0.1213', 'train/total_time_seconds': '54.87', 'train/time_per_step_avg': '0.02995', 'train/epoch_time_elapsed': '209.3', 'train/estimated_remaining_minutes': '0.1016'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.2201', 'eval_runtime': '8.624', 'eval_samples_per_second': '1105', 'eval_steps_per_second': '17.28', 'epoch': '0.1213', 'train/total_time_seconds': '54.87', 'train/time_per_step_avg': '0.02995', 'train/epoch_time_elapsed': '218', 'train/estimated_remaining_minutes': '0.1016'}
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+{'loss': '0.2156', 'grad_norm': '0.2129', 'learning_rate': '0.0003', 'epoch': '0.124', 'train/total_time_seconds': '56.13', 'train/time_per_step_avg': '0.0306', 'train/epoch_time_elapsed': '221.2', 'train/estimated_remaining_minutes': '0.08135'}
+{'loss': '0.2151', 'grad_norm': '0.2158', 'learning_rate': '0.0003', 'epoch': '0.1254', 'train/total_time_seconds': '56.75', 'train/time_per_step_avg': '0.03076', 'train/epoch_time_elapsed': '222.8', 'train/estimated_remaining_minutes': '0.07119'}
+{'loss': '0.2145', 'grad_norm': '0.2158', 'learning_rate': '0.0003', 'epoch': '0.1267', 'train/total_time_seconds': '57.35', 'train/time_per_step_avg': '0.03084', 'train/epoch_time_elapsed': '224.4', 'train/estimated_remaining_minutes': '0.06101'}
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+{'loss': '0.2116', 'grad_norm': '0.2188', 'learning_rate': '0.0003', 'epoch': '0.1335', 'train/total_time_seconds': '60.34', 'train/time_per_step_avg': '0.0299', 'train/epoch_time_elapsed': '232.1', 'train/estimated_remaining_minutes': '0.01016'}
+{'loss': '0.2111', 'grad_norm': '0.2129', 'learning_rate': '0.0003', 'epoch': '0.1348', 'train/total_time_seconds': '60.94', 'train/time_per_step_avg': '0.02989', 'train/epoch_time_elapsed': '233.7', 'train/estimated_remaining_minutes': '0'}
+{'eval_loss': '0.2126', 'eval_runtime': '8.586', 'eval_samples_per_second': '1110', 'eval_steps_per_second': '17.35', 'epoch': '0.1348', 'train/total_time_seconds': '60.94', 'train/time_per_step_avg': '0.02989', 'train/epoch_time_elapsed': '242.3', 'train/estimated_remaining_minutes': '0'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 136.65it/s]
+100%|██████████████████████████████████████| 2000/2000 [04:03<00:00,  8.22it/s]
+{'train_runtime': '243.2', 'train_samples_per_second': '526.4', 'train_steps_per_second': '8.225', 'train_loss': '0.9386', 'epoch': '0.1348', 'train/total_time_seconds': '60.94', 'train/time_per_step_avg': '0.02989', 'train/epoch_time_elapsed': '242.3', 'train/estimated_remaining_minutes': '0'}
+100%|████████████████████████████████████████| 149/149 [00:08<00:00, 17.69it/s]
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 143.35it/s]
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 153.54it/s]
+Found 7 files to upload
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████░░░░░░░░░░░░  3 / 7
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████░░░░░░░░░░░░  3 / 7
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████████████████  7 / 7 ✓
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 139.14it/s]
+Found 7 files to upload
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████████████████  7 / 7 ✓
+No files have been modified since last commit. Skipping to prevent empty commit.
diff --git a/wandb/run-20260806_202910-50qq810b/files/requirements.txt b/wandb/run-20260806_202910-50qq810b/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..123b15ebf857859624f7f4332e92341c8ef13fdf
--- /dev/null
+++ b/wandb/run-20260806_202910-50qq810b/files/requirements.txt
@@ -0,0 +1,149 @@
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+parso==0.8.7
+platformdirs==4.11.0
+psutil==7.2.2
+ptyprocess==0.7.0
+pure_eval==0.2.3
+Pygments==2.20.0
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+six==1.17.0
+tornado==6.5.7
+traitlets==5.15.0
+fsspec==2026.4.0
+wcwidth==0.8.2
+ipython_pygments_lexers==1.1.1
+jedi==0.20.0
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+attrs==26.1.0
+hf-xet==1.5.2
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+regex==2026.7.19
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+mdurl==0.1.2
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+networkx==3.6.1
+PyYAML==6.0.3
+nvidia-cufile==1.15.1.6
+typer==0.27.0
+torchaudio==2.6.0+cu124
+rich==15.0.0
+nvidia-cufft-cu12==11.2.1.3
+h11==0.16.0
+dill==0.4.1
+cuda-pathfinder==1.6.0
+filelock==3.29.0
+nvidia-nvtx-cu12==12.4.127
+httpx==0.28.1
+anyio==4.14.2
+numpy==2.4.4
+yarl==1.24.5
+click==8.4.2
+triton==3.2.0
+frozenlist==1.8.0
+zipp==4.1.0
+propcache==0.5.2
+tokenizers==0.22.2
+markdown-it-py==4.2.0
+nvidia-cuda-runtime==13.0.96
+cuda-bindings==13.3.1
+nvidia-cuda-cupti==13.0.85
+torch==2.6.0+cu124
+multiprocess==0.70.19
+pillow==12.2.0
+transformers==5.15.0.dev0
+wandb==0.28.1
+nvidia-curand==10.4.0.35
+sympy==1.13.1
+nvidia-cusparse==12.6.3.3
+nvidia-cuda-nvrtc==13.0.88
+typing-inspection==0.4.2
+nvidia-cusolver==12.0.4.66
+nvidia-cufft==12.0.0.61
+nvidia-cudnn-cu13==9.20.0.48
+nvidia-cublas==13.1.1.3
+pyarrow==25.0.0
+evaluate==0.4.6
+diffusers==0.39.0
+pydantic==2.13.4
+annotated-types==0.8.0
+protobuf==7.35.1
+sentry-sdk==2.66.1
+einops==0.8.2
+packaging==26.2
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusparselt-cu12==0.6.2
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cuda-runtime-cu12==12.4.127
+torchvision==0.21.0+cu124
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cudnn-cu12==9.1.0.70
+huggingface_hub==1.26.0
+datasets==5.0.1
+safetensors==0.8.0
+accelerate==1.14.0
+pydantic_core==2.46.4
+ninja==1.13.0
+autocommand==2.2.2
+backports.tarfile==1.2.0
+importlib_metadata==8.7.1
+jaraco.text==4.0.0
+jaraco.context==6.1.0
+jaraco.functools==4.4.0
+more-itertools==10.8.0
+packaging==26.0
+platformdirs==4.4.0
+tomli==2.4.0
+wheel==0.46.3
+zipp==3.23.0
diff --git a/wandb/run-20260806_202910-50qq810b/files/wandb-metadata.json b/wandb/run-20260806_202910-50qq810b/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..fcc80cd9748ecd9850fbb9208e79872da7433c60
--- /dev/null
+++ b/wandb/run-20260806_202910-50qq810b/files/wandb-metadata.json
@@ -0,0 +1,96 @@
+{
+  "os":  "Linux-5.15.0-126-generic-x86_64-with-glibc2.35",
+  "python":  "CPython 3.11.15",
+  "startedAt":  "2026-08-06T20:29:10.896746Z",
+  "args":  [
+    "--config",
+    "configs/baseline.yaml",
+    "--variants",
+    "glu-silu-9L",
+    "--push"
+  ],
+  "program":  "/mnt/data/zainulabideen/zain-exp/notebooks/Activation/sweep.py",
+  "codePath":  "sweep.py",
+  "codePathLocal":  "sweep.py",
+  "git":  {
+    "remote":  "https://github.com/deepnevro/Activation.git",
+    "commit":  "8d6f97d73f5521e4c38c6e1bda59bf8d4b489655"
+  },
+  "email":  "deepnevro@gmail.com",
+  "root":  "/mnt/data/zainulabideen/zain-exp/notebooks/Activation",
+  "host":  "deeplens-k3s-node1",
+  "executable":  "/mnt/data/zainulabideen/zain-exp/notebooks/my_env/bin/python",
+  "cpu_count":  112,
+  "cpu_count_logical":  224,
+  "gpu":  "NVIDIA H100 80GB HBM3",
+  "gpu_count":  8,
+  "disk":  {
+    "/":  {
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+      "used":  "703687757824"
+    }
+  },
+  "memory":  {
+    "total":  "2164089937920"
+  },
+  "gpu_nvidia":  [
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-39c684a5-fde6-83d7-1663-0859795881ae"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-68012e5a-38b6-b643-0ca6-62fb66720bf3"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-132944c4-b689-2b5f-89a4-d730401677ab"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-2df386cc-6d26-d0e2-7a2d-a057b0d95864"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-bfa16575-1d94-1aa2-4537-2c93433f42ef"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-bc6c3e3c-9b90-09ca-c034-774961847c54"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-00a441e1-7c95-e7d6-4c35-43d6b291aea9"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-1c4d29a2-4647-6fce-d8fc-0c5ecfbbd6ea"
+    }
+  ],
+  "cudaVersion":  "12.4",
+  "writerId":  "4keil7qqzivlr1sdegmzhdtfbengkrt5"
+}
\ No newline at end of file
diff --git a/wandb/run-20260806_202910-50qq810b/files/wandb-summary.json b/wandb/run-20260806_202910-50qq810b/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5acfa6ff8a694aa78fdc1b18e4304781ffcec665
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..fff6671914de724c74233b0357105194da427ba2
--- /dev/null
+++ b/wandb/run-20260806_202910-50qq810b/logs/debug-internal.log
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+hub_always_push:
+    value: false
+hub_model_id:
+    value: w-ahmad/ACTio-glu-relu-9L
+hub_private_repo:
+    value: null
+hub_revision:
+    value: null
+hub_strategy:
+    value: every_save
+hub_token:
+    value: <HUB_TOKEN>
+id2label:
+    value:
+        "0": LABEL_0
+        "1": LABEL_1
+ignore_data_skip:
+    value: false
+include_for_metrics:
+    value: []
+include_num_input_tokens_seen:
+    value: "no"
+initializer_range:
+    value: 0.02
+intermediate_size:
+    value: 256
+is_encoder_decoder:
+    value: false
+label_names:
+    value: null
+label_smoothing_factor:
+    value: 0
+label2id:
+    value:
+        LABEL_0: 0
+        LABEL_1: 1
+learning_rate:
+    value: 0.0003
+length_column_name:
+    value: length
+liger_kernel_config:
+    value: null
+load_best_model_at_end:
+    value: false
+local_rank:
+    value: -1
+log_level:
+    value: passive
+log_level_replica:
+    value: warning
+log_on_each_node:
+    value: true
+logging_first_step:
+    value: false
+logging_nan_inf_filter:
+    value: true
+logging_steps:
+    value: 20
+logging_strategy:
+    value: steps
+lr_scheduler_kwargs:
+    value: null
+lr_scheduler_type:
+    value: constant
+max_grad_norm:
+    value: 1
+max_position_embeddings:
+    value: 512
+max_steps:
+    value: 2000
+metric_for_best_model:
+    value: null
+mlp_bias:
+    value: false
+mlp_type:
+    value: glu
+model/num_parameters:
+    value: 2001280
+model_type:
+    value: tiny_llama
+neftune_noise_alpha:
+    value: null
+num_attention_heads:
+    value: 4
+num_hidden_layers:
+    value: 9
+num_key_value_heads:
+    value: 4
+num_train_epochs:
+    value: 1
+optim:
+    value: adamw_torch
+optim_args:
+    value: null
+optim_target_modules:
+    value: null
+output_attentions:
+    value: false
+output_dir:
+    value: out/glu-relu-9L_run
+output_hidden_states:
+    value: false
+pad_token_id:
+    value: 0
+parallelism_config:
+    value: null
+per_device_eval_batch_size:
+    value: 64
+per_device_train_batch_size:
+    value: 64
+prediction_loss_only:
+    value: false
+pretraining_tp:
+    value: 1
+problem_type:
+    value: null
+project:
+    value: huggingface
+push_to_hub:
+    value: true
+remove_unused_columns:
+    value: false
+report_to:
+    value:
+        - wandb
+restore_callback_states_from_checkpoint:
+    value: false
+resume_from_checkpoint:
+    value: null
+return_dict:
+    value: true
+rms_norm_eps:
+    value: 1e-06
+rope_parameters:
+    value:
+        rope_theta: 10000
+        rope_type: default
+run_name:
+    value: LM-glu-relu-9L-2.0M-20260806-203334
+save_on_each_node:
+    value: false
+save_only_model:
+    value: false
+save_steps:
+    value: 500
+save_strategy:
+    value: steps
+save_total_limit:
+    value: null
+seed:
+    value: 42
+skip_memory_metrics:
+    value: true
+tf32:
+    value: null
+tie_word_embeddings:
+    value: true
+tokenizer_name:
+    value: w-ahmad/tiny-stories-tokenizer
+torch_compile:
+    value: false
+torch_compile_backend:
+    value: null
+torch_compile_mode:
+    value: null
+torch_empty_cache_steps:
+    value: null
+trackio_bucket_id:
+    value: null
+trackio_space_id:
+    value: null
+trackio_static_space_id:
+    value: null
+train_sampling_strategy:
+    value: random
+transformers_version:
+    value: 5.15.0.dev0
+use_cache:
+    value: false
+use_cpu:
+    value: false
+use_liger_kernel:
+    value: false
+vocab_size:
+    value: 4096
+warmup_steps:
+    value: 0
+weight_decay:
+    value: 0.01
diff --git a/wandb/run-20260806_203335-6n8s22c0/files/output.log b/wandb/run-20260806_203335-6n8s22c0/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..97e5b50cee3d9c3863294179e303afc02340e427
--- /dev/null
+++ b/wandb/run-20260806_203335-6n8s22c0/files/output.log
@@ -0,0 +1,169 @@
+[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 25%|█████████▊                             | 500/2000 [00:57<01:54, 13.05it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '7.792', 'grad_norm': '1.258', 'learning_rate': '0.0003', 'epoch': '0.001348', 'train/total_time_seconds': '1.231', 'train/time_per_step_avg': '0.06154', 'train/epoch_time_elapsed': '2.412', 'train/estimated_remaining_minutes': '2.031', 'train/global/act/norm': '1.026e+04', 'train/global/act/mean': '0.006206', 'train/global/act/std': '0', 'train/global/act/max_abs': '8.331', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '2.297', 'train/global/grad/mean': '3.617e-07', 'train/global/grad/std': '0.001625', 'train/global/grad/max_abs': '0.167', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '56.84', 'train/global/param/mean': '0.001191', 'train/global/param/std': '0.04018', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '17.94', 'train/layer__model_layers_3/param/mean': '0.001448', 'train/layer__model_layers_3/param/std': '0.04427', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '17.94', 'train/layer__model_layers_4/param/mean': '0.001546', 'train/layer__model_layers_4/param/std': '0.04425', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '17.93', 'train/layer__model_layers_5/param/mean': '0.001555', 'train/layer__model_layers_5/param/std': '0.04425', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '3158', 'train/layer_model_layers_7/act/mean': '0.004716', 'train/layer_model_layers_7/act/std': '0.4126', 'train/layer_model_layers_7/act/max_abs': '4.5', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.3846', 'train/layer_model_layers_7/grad/mean': '1.121e-06', 'train/layer_model_layers_7/grad/std': '0.0009495', 'train/layer_model_layers_7/grad/max_abs': '0.01929', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '3155', 'train/layer_model_layers_8/act/mean': '0.009389', 'train/layer_model_layers_8/act/std': '0.4121', 'train/layer_model_layers_8/act/max_abs': '4.688', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.3108', 'train/layer_model_layers_8/grad/mean': '6.669e-07', 'train/layer_model_layers_8/grad/std': '0.0007672', 'train/layer_model_layers_8/grad/max_abs': '0.01233', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '17.93', 'train/layer__model_layers_0/param/mean': '0.001573', 'train/layer__model_layers_0/param/std': '0.04425', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '3147', 'train/layer_model_layers_2/act/mean': '0.003464', 'train/layer_model_layers_2/act/std': '0.4101', 'train/layer_model_layers_2/act/max_abs': '5', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.978', 'train/layer_model_layers_2/grad/mean': '7.209e-06', 'train/layer_model_layers_2/grad/std': '0.002414', 'train/layer_model_layers_2
+{'loss': '7.008', 'grad_norm': '1.172', 'learning_rate': '0.0003', 'epoch': '0.002696', 'train/total_time_seconds': '1.812', 'train/time_per_step_avg': '0.04531', 'train/epoch_time_elapsed': '3.971', 'train/estimated_remaining_minutes': '1.48'}
+{'loss': '6.428', 'grad_norm': '1.055', 'learning_rate': '0.0003', 'epoch': '0.004044', 'train/total_time_seconds': '2.4', 'train/time_per_step_avg': '0.03999', 'train/epoch_time_elapsed': '5.522', 'train/estimated_remaining_minutes': '1.293'}
+{'loss': '5.938', 'grad_norm': '2.062', 'learning_rate': '0.0003', 'epoch': '0.005393', 'train/total_time_seconds': '2.985', 'train/time_per_step_avg': '0.03731', 'train/epoch_time_elapsed': '7.064', 'train/estimated_remaining_minutes': '1.194'}
+{'loss': '5.357', 'grad_norm': '3.609', 'learning_rate': '0.0003', 'epoch': '0.006741', 'train/total_time_seconds': '3.572', 'train/time_per_step_avg': '0.03572', 'train/epoch_time_elapsed': '8.633', 'train/estimated_remaining_minutes': '1.131'}
+{'loss': '4.846', 'grad_norm': '3.312', 'learning_rate': '0.0003', 'epoch': '0.008089', 'train/total_time_seconds': '4.162', 'train/time_per_step_avg': '0.02931', 'train/epoch_time_elapsed': '10.2', 'train/estimated_remaining_minutes': '1.087'}
+{'loss': '4.375', 'grad_norm': '3.766', 'learning_rate': '0.0003', 'epoch': '0.009437', 'train/total_time_seconds': '4.751', 'train/time_per_step_avg': '0.02939', 'train/epoch_time_elapsed': '11.76', 'train/estimated_remaining_minutes': '1.052'}
+{'loss': '3.941', 'grad_norm': '2.391', 'learning_rate': '0.0003', 'epoch': '0.01079', 'train/total_time_seconds': '5.339', 'train/time_per_step_avg': '0.0294', 'train/epoch_time_elapsed': '13.31', 'train/estimated_remaining_minutes': '1.023'}
+{'loss': '3.531', 'grad_norm': '2.625', 'learning_rate': '0.0003', 'epoch': '0.01213', 'train/total_time_seconds': '5.93', 'train/time_per_step_avg': '0.02945', 'train/epoch_time_elapsed': '14.86', 'train/estimated_remaining_minutes': '0.9992'}
+{'loss': '3.13', 'grad_norm': '2.203', 'learning_rate': '0.0003', 'epoch': '0.01348', 'train/total_time_seconds': '6.518', 'train/time_per_step_avg': '0.02946', 'train/epoch_time_elapsed': '16.41', 'train/estimated_remaining_minutes': '0.9777'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
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+{'loss': '0.9642', 'grad_norm': '1.039', 'learning_rate': '0.0003', 'epoch': '0.02427', 'train/total_time_seconds': '11.26', 'train/time_per_step_avg': '0.02931', 'train/epoch_time_elapsed': '37.25', 'train/estimated_remaining_minutes': '0.8553'}
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+{'eval_loss': '0.6944', 'eval_runtime': '8.391', 'eval_samples_per_second': '1135', 'eval_steps_per_second': '17.76', 'epoch': '0.02696', 'train/total_time_seconds': '12.45', 'train/time_per_step_avg': '0.02949', 'train/epoch_time_elapsed': '48.84', 'train/estimated_remaining_minutes': '0.8303'}
+{'loss': '0.6503', 'grad_norm': '0.4883', 'learning_rate': '0.0003', 'epoch': '0.02831', 'train/total_time_seconds': '13.1', 'train/time_per_step_avg': '0.0301', 'train/epoch_time_elapsed': '50.45', 'train/estimated_remaining_minutes': '0.8212', 'train/global/act/norm': '2.715e+04', 'train/global/act/mean': '-0.2162', 'train/global/act/std': '0', 'train/global/act/max_abs': '10', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.5171', 'train/global/grad/mean': '-1.04e-06', 'train/global/grad/std': '0.0003653', 'train/global/grad/max_abs': '0.04517', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '69.26', 'train/global/param/mean': '0.00132', 'train/global/param/std': '0.04898', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.5', 'train/layer__model_layers_3/param/mean': '0.001453', 'train/layer__model_layers_3/param/std': '0.04564', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '18.48', 'train/layer__model_layers_4/param/mean': '0.001577', 'train/layer__model_layers_4/param/std': '0.0456', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '18.53', 'train/layer__model_layers_5/param/mean': '0.001579', 'train/layer__model_layers_5/param/std': '0.04573', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '5919', 'train/layer_model_layers_7/act/mean': '0.03543', 'train/layer_model_layers_7/act/std': '0.7716', 'train/layer_model_layers_7/act/max_abs': '6.219', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.1034', 'train/layer_model_layers_7/grad/mean': '1.526e-07', 'train/layer_model_layers_7/grad/std': '0.0002554', 'train/layer_model_layers_7/grad/max_abs': '0.004913', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '5980', 'train/layer_model_layers_8/act/mean': '0.05432', 'train/layer_model_layers_8/act/std': '0.7781', 'train/layer_model_layers_8/act/max_abs': '5.594', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.09015', 'train/layer_model_layers_8/grad/mean': '-2.775e-07', 'train/layer_model_layers_8/grad/std': '0.0002228', 'train/layer_model_layers_8/grad/max_abs': '0.003128', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.32', 'train/layer__model_layers_0/param/mean': '0.001576', 'train/layer__model_layers_0/param/std': '0.04519', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6305', 'train/layer_model_layers_2/act/mean': '0.01427', 'train/layer_model_layers_2/act/std': '0.8234', 'train/layer_model_layers_2/act/max_abs': '5.812', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.1553', 'train/layer_model_layers_2/grad/mean': '-4.762e-07', 'train/layer_model_layers_2/grad/std': '0.0003834', 'train/layer_model_l
+{'loss': '0.6034', 'grad_norm': '0.4395', 'learning_rate': '0.0003', 'epoch': '0.02966', 'train/total_time_seconds': '13.69', 'train/time_per_step_avg': '0.0301', 'train/epoch_time_elapsed': '52.02', 'train/estimated_remaining_minutes': '0.8089'}
+{'loss': '0.5518', 'grad_norm': '0.4512', 'learning_rate': '0.0003', 'epoch': '0.03101', 'train/total_time_seconds': '14.28', 'train/time_per_step_avg': '0.0302', 'train/epoch_time_elapsed': '53.56', 'train/estimated_remaining_minutes': '0.7971'}
+{'loss': '0.5193', 'grad_norm': '0.3945', 'learning_rate': '0.0003', 'epoch': '0.03236', 'train/total_time_seconds': '14.87', 'train/time_per_step_avg': '0.0301', 'train/epoch_time_elapsed': '55.09', 'train/estimated_remaining_minutes': '0.7846'}
+{'loss': '0.4868', 'grad_norm': '0.3691', 'learning_rate': '0.0003', 'epoch': '0.0337', 'train/total_time_seconds': '15.45', 'train/time_per_step_avg': '0.02998', 'train/epoch_time_elapsed': '56.62', 'train/estimated_remaining_minutes': '0.7726'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 133.90it/s]
+ 30%|███████████▋                           | 600/2000 [01:13<01:46, 13.11it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.4642', 'grad_norm': '0.3848', 'learning_rate': '0.0003', 'epoch': '0.03505', 'train/total_time_seconds': '16.04', 'train/time_per_step_avg': '0.02946', 'train/epoch_time_elapsed': '58.21', 'train/estimated_remaining_minutes': '0.761'}
+{'loss': '0.4412', 'grad_norm': '0.5195', 'learning_rate': '0.0003', 'epoch': '0.0364', 'train/total_time_seconds': '16.63', 'train/time_per_step_avg': '0.02944', 'train/epoch_time_elapsed': '59.75', 'train/estimated_remaining_minutes': '0.7495'}
+{'loss': '0.4318', 'grad_norm': '1.5', 'learning_rate': '0.0003', 'epoch': '0.03775', 'train/total_time_seconds': '17.22', 'train/time_per_step_avg': '0.02936', 'train/epoch_time_elapsed': '61.29', 'train/estimated_remaining_minutes': '0.738'}
+{'loss': '0.4201', 'grad_norm': '0.4863', 'learning_rate': '0.0003', 'epoch': '0.0391', 'train/total_time_seconds': '17.81', 'train/time_per_step_avg': '0.0294', 'train/epoch_time_elapsed': '62.82', 'train/estimated_remaining_minutes': '0.7266'}
+{'loss': '0.4105', 'grad_norm': '0.3535', 'learning_rate': '0.0003', 'epoch': '0.04044', 'train/total_time_seconds': '18.39', 'train/time_per_step_avg': '0.02941', 'train/epoch_time_elapsed': '64.34', 'train/estimated_remaining_minutes': '0.7153'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.4061', 'eval_runtime': '8.449', 'eval_samples_per_second': '1128', 'eval_steps_per_second': '17.64', 'epoch': '0.04044', 'train/total_time_seconds': '18.39', 'train/time_per_step_avg': '0.02941', 'train/epoch_time_elapsed': '72.79', 'train/estimated_remaining_minutes': '0.7153'}
+ 50%|███████████████████                   | 1000/2000 [02:01<01:16, 13.06it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.3975', 'grad_norm': '0.3926', 'learning_rate': '0.0003', 'epoch': '0.04179', 'train/total_time_seconds': '19.04', 'train/time_per_step_avg': '0.02992', 'train/epoch_time_elapsed': '74.39', 'train/estimated_remaining_minutes': '0.7062', 'train/global/act/norm': '2.731e+04', 'train/global/act/mean': '-0.2084', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.56', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.3661', 'train/global/grad/mean': '-9.34e-07', 'train/global/grad/std': '0.000259', 'train/global/grad/max_abs': '0.03345', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '72.82', 'train/global/param/mean': '0.001323', 'train/global/param/std': '0.05147', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.53', 'train/layer__model_layers_3/param/mean': '0.001472', 'train/layer__model_layers_3/param/std': '0.04573', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '18.52', 'train/layer__model_layers_4/param/mean': '0.001582', 'train/layer__model_layers_4/param/std': '0.04571', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '18.58', 'train/layer__model_layers_5/param/mean': '0.001577', 'train/layer__model_layers_5/param/std': '0.04583', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6062', 'train/layer_model_layers_7/act/mean': '0.03955', 'train/layer_model_layers_7/act/std': '0.7905', 'train/layer_model_layers_7/act/max_abs': '6.594', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.06668', 'train/layer_model_layers_7/grad/mean': '1.014e-07', 'train/layer_model_layers_7/grad/std': '0.0001649', 'train/layer_model_layers_7/grad/max_abs': '0.003662', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '6019', 'train/layer_model_layers_8/act/mean': '0.05698', 'train/layer_model_layers_8/act/std': '0.7842', 'train/layer_model_layers_8/act/max_abs': '6.094', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.05868', 'train/layer_model_layers_8/grad/mean': '-3.604e-07', 'train/layer_model_layers_8/grad/std': '0.0001449', 'train/layer_model_layers_8/grad/max_abs': '0.001999', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.36', 'train/layer__model_layers_0/param/mean': '0.001585', 'train/layer__model_layers_0/param/std': '0.04532', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6551', 'train/layer_model_layers_2/act/mean': '0.02133', 'train/layer_model_layers_2/act/std': '0.8559', 'train/layer_model_layers_2/act/max_abs': '5.844', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.1044', 'train/layer_model_layers_2/grad/mean': '4.625e-07', 'train/layer_model_layers_2/grad/std': '0.0002576', 'train/layer_
+{'loss': '0.3894', 'grad_norm': '0.3926', 'learning_rate': '0.0003', 'epoch': '0.04314', 'train/total_time_seconds': '19.62', 'train/time_per_step_avg': '0.02983', 'train/epoch_time_elapsed': '75.93', 'train/estimated_remaining_minutes': '0.6948'}
+{'loss': '0.3756', 'grad_norm': '0.334', 'learning_rate': '0.0003', 'epoch': '0.04449', 'train/total_time_seconds': '20.21', 'train/time_per_step_avg': '0.02987', 'train/epoch_time_elapsed': '77.48', 'train/estimated_remaining_minutes': '0.6838'}
+{'loss': '0.368', 'grad_norm': '0.3438', 'learning_rate': '0.0003', 'epoch': '0.04584', 'train/total_time_seconds': '20.8', 'train/time_per_step_avg': '0.02992', 'train/epoch_time_elapsed': '79.02', 'train/estimated_remaining_minutes': '0.6729'}
+{'loss': '0.3585', 'grad_norm': '0.3711', 'learning_rate': '0.0003', 'epoch': '0.04719', 'train/total_time_seconds': '21.38', 'train/time_per_step_avg': '0.02988', 'train/epoch_time_elapsed': '80.54', 'train/estimated_remaining_minutes': '0.6618'}
+{'loss': '0.3527', 'grad_norm': '0.4414', 'learning_rate': '0.0003', 'epoch': '0.04853', 'train/total_time_seconds': '21.97', 'train/time_per_step_avg': '0.02931', 'train/epoch_time_elapsed': '82.08', 'train/estimated_remaining_minutes': '0.6509'}
+{'loss': '0.3426', 'grad_norm': '0.4043', 'learning_rate': '0.0003', 'epoch': '0.04988', 'train/total_time_seconds': '22.56', 'train/time_per_step_avg': '0.0294', 'train/epoch_time_elapsed': '83.62', 'train/estimated_remaining_minutes': '0.6401'}
+{'loss': '0.3398', 'grad_norm': '0.3613', 'learning_rate': '0.0003', 'epoch': '0.05123', 'train/total_time_seconds': '23.16', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '85.18', 'train/estimated_remaining_minutes': '0.6299'}
+{'loss': '0.3521', 'grad_norm': '1.445', 'learning_rate': '0.0003', 'epoch': '0.05258', 'train/total_time_seconds': '23.76', 'train/time_per_step_avg': '0.0296', 'train/epoch_time_elapsed': '86.72', 'train/estimated_remaining_minutes': '0.6194'}
+{'loss': '0.3393', 'grad_norm': '0.5469', 'learning_rate': '0.0003', 'epoch': '0.05393', 'train/total_time_seconds': '24.35', 'train/time_per_step_avg': '0.02966', 'train/epoch_time_elapsed': '88.25', 'train/estimated_remaining_minutes': '0.6087'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.3342', 'eval_runtime': '8.334', 'eval_samples_per_second': '1143', 'eval_steps_per_second': '17.88', 'epoch': '0.05393', 'train/total_time_seconds': '24.35', 'train/time_per_step_avg': '0.02966', 'train/epoch_time_elapsed': '96.59', 'train/estimated_remaining_minutes': '0.6087'}
+{'loss': '0.3279', 'grad_norm': '0.4414', 'learning_rate': '0.0003', 'epoch': '0.05527', 'train/total_time_seconds': '25', 'train/time_per_step_avg': '0.03033', 'train/epoch_time_elapsed': '98.21', 'train/estimated_remaining_minutes': '0.5996', 'train/global/act/norm': '2.69e+04', 'train/global/act/mean': '-0.2008', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.69', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.5646', 'train/global/grad/mean': '-7.882e-07', 'train/global/grad/std': '0.000399', 'train/global/grad/max_abs': '0.03064', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '74.16', 'train/global/param/mean': '0.001324', 'train/global/param/std': '0.05247', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.57', 'train/layer__model_layers_3/param/mean': '0.001474', 'train/layer__model_layers_3/param/std': '0.04583', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '18.54', 'train/layer__model_layers_4/param/mean': '0.001579', 'train/layer__model_layers_4/param/std': '0.04576', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '18.61', 'train/layer__model_layers_5/param/mean': '0.001571', 'train/layer__model_layers_5/param/std': '0.0459', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '5984', 'train/layer_model_layers_7/act/mean': '0.03648', 'train/layer_model_layers_7/act/std': '0.7809', 'train/layer_model_layers_7/act/max_abs': '6.75', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.1299', 'train/layer_model_layers_7/grad/mean': '-3.97e-07', 'train/layer_model_layers_7/grad/std': '0.0003209', 'train/layer_model_layers_7/grad/max_abs': '0.007629', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '5925', 'train/layer_model_layers_8/act/mean': '0.0557', 'train/layer_model_layers_8/act/std': '0.771', 'train/layer_model_layers_8/act/max_abs': '6.344', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.1187', 'train/layer_model_layers_8/grad/mean': '1.602e-08', 'train/layer_model_layers_8/grad/std': '0.0002928', 'train/layer_model_layers_8/grad/max_abs': '0.004791', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.39', 'train/layer__model_layers_0/param/mean': '0.001593', 'train/layer__model_layers_0/param/std': '0.04537', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6635', 'train/layer_model_layers_2/act/mean': '0.02288', 'train/layer_model_layers_2/act/std': '0.8648', 'train/layer_model_layers_2/act/max_abs': '6.031', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.193', 'train/layer_model_layers_2/grad/mean': '-1.639e-07', 'train/layer_model_layers_2/grad/std': '0.0004761', 'train/layer_model_laye
+{'loss': '0.3204', 'grad_norm': '0.3633', 'learning_rate': '0.0003', 'epoch': '0.05662', 'train/total_time_seconds': '25.6', 'train/time_per_step_avg': '0.03047', 'train/epoch_time_elapsed': '99.83', 'train/estimated_remaining_minutes': '0.5893'}
+{'loss': '0.3127', 'grad_norm': '0.2949', 'learning_rate': '0.0003', 'epoch': '0.05797', 'train/total_time_seconds': '26.2', 'train/time_per_step_avg': '0.0304', 'train/epoch_time_elapsed': '101.4', 'train/estimated_remaining_minutes': '0.5789'}
+{'loss': '0.306', 'grad_norm': '0.2812', 'learning_rate': '0.0003', 'epoch': '0.05932', 'train/total_time_seconds': '26.8', 'train/time_per_step_avg': '0.03044', 'train/epoch_time_elapsed': '103', 'train/estimated_remaining_minutes': '0.5685'}
+{'loss': '0.3045', 'grad_norm': '0.3359', 'learning_rate': '0.0003', 'epoch': '0.06067', 'train/total_time_seconds': '27.39', 'train/time_per_step_avg': '0.03046', 'train/epoch_time_elapsed': '104.5', 'train/estimated_remaining_minutes': '0.558'}
+{'loss': '0.3006', 'grad_norm': '0.2852', 'learning_rate': '0.0003', 'epoch': '0.06202', 'train/total_time_seconds': '27.98', 'train/time_per_step_avg': '0.02985', 'train/epoch_time_elapsed': '106.1', 'train/estimated_remaining_minutes': '0.5475'}
+{'loss': '0.2961', 'grad_norm': '0.2832', 'learning_rate': '0.0003', 'epoch': '0.06336', 'train/total_time_seconds': '28.58', 'train/time_per_step_avg': '0.02974', 'train/epoch_time_elapsed': '107.6', 'train/estimated_remaining_minutes': '0.5371'}
+{'loss': '0.2908', 'grad_norm': '0.2715', 'learning_rate': '0.0003', 'epoch': '0.06471', 'train/total_time_seconds': '29.17', 'train/time_per_step_avg': '0.02971', 'train/epoch_time_elapsed': '109.2', 'train/estimated_remaining_minutes': '0.5268'}
+{'loss': '0.2891', 'grad_norm': '0.293', 'learning_rate': '0.0003', 'epoch': '0.06606', 'train/total_time_seconds': '29.76', 'train/time_per_step_avg': '0.02962', 'train/epoch_time_elapsed': '110.7', 'train/estimated_remaining_minutes': '0.5163'}
+{'loss': '0.2869', 'grad_norm': '0.3242', 'learning_rate': '0.0003', 'epoch': '0.06741', 'train/total_time_seconds': '30.35', 'train/time_per_step_avg': '0.02961', 'train/epoch_time_elapsed': '112.2', 'train/estimated_remaining_minutes': '0.5059'}
+{'eval_loss': '0.2864', 'eval_runtime': '8.439', 'eval_samples_per_second': '1129', 'eval_steps_per_second': '17.66', 'epoch': '0.06741', 'train/total_time_seconds': '30.35', 'train/time_per_step_avg': '0.02961', 'train/epoch_time_elapsed': '120.7', 'train/estimated_remaining_minutes': '0.5059'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 100.25it/s]
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 75%|████████████████████████████▌         | 1500/2000 [02:57<00:38, 12.95it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
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+{'loss': '0.2777', 'grad_norm': '0.2812', 'learning_rate': '0.0003', 'epoch': '0.0701', 'train/total_time_seconds': '31.61', 'train/time_per_step_avg': '0.03033', 'train/epoch_time_elapsed': '123.9', 'train/estimated_remaining_minutes': '0.4863'}
+{'loss': '0.2773', 'grad_norm': '0.3008', 'learning_rate': '0.0003', 'epoch': '0.07145', 'train/total_time_seconds': '32.21', 'train/time_per_step_avg': '0.03033', 'train/epoch_time_elapsed': '125.5', 'train/estimated_remaining_minutes': '0.476'}
+{'loss': '0.2728', 'grad_norm': '0.2969', 'learning_rate': '0.0003', 'epoch': '0.0728', 'train/total_time_seconds': '32.8', 'train/time_per_step_avg': '0.0304', 'train/epoch_time_elapsed': '127', 'train/estimated_remaining_minutes': '0.4657'}
+{'loss': '0.2716', 'grad_norm': '0.3047', 'learning_rate': '0.0003', 'epoch': '0.07415', 'train/total_time_seconds': '33.4', 'train/time_per_step_avg': '0.03045', 'train/epoch_time_elapsed': '128.5', 'train/estimated_remaining_minutes': '0.4554'}
+{'loss': '0.268', 'grad_norm': '0.3184', 'learning_rate': '0.0003', 'epoch': '0.0755', 'train/total_time_seconds': '34.02', 'train/time_per_step_avg': '0.03007', 'train/epoch_time_elapsed': '130.2', 'train/estimated_remaining_minutes': '0.4455'}
+{'loss': '0.264', 'grad_norm': '0.3242', 'learning_rate': '0.0003', 'epoch': '0.07685', 'train/total_time_seconds': '34.63', 'train/time_per_step_avg': '0.03015', 'train/epoch_time_elapsed': '131.8', 'train/estimated_remaining_minutes': '0.4354'}
+{'loss': '0.2638', 'grad_norm': '0.3047', 'learning_rate': '0.0003', 'epoch': '0.07819', 'train/total_time_seconds': '35.23', 'train/time_per_step_avg': '0.03021', 'train/epoch_time_elapsed': '133.4', 'train/estimated_remaining_minutes': '0.4252'}
+{'loss': '0.272', 'grad_norm': '3.188', 'learning_rate': '0.0003', 'epoch': '0.07954', 'train/total_time_seconds': '35.83', 'train/time_per_step_avg': '0.03025', 'train/epoch_time_elapsed': '134.9', 'train/estimated_remaining_minutes': '0.415'}
+{'loss': '0.2769', 'grad_norm': '0.957', 'learning_rate': '0.0003', 'epoch': '0.08089', 'train/total_time_seconds': '36.43', 'train/time_per_step_avg': '0.03033', 'train/epoch_time_elapsed': '136.5', 'train/estimated_remaining_minutes': '0.4048'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.2695', 'eval_runtime': '8.513', 'eval_samples_per_second': '1119', 'eval_steps_per_second': '17.5', 'epoch': '0.08089', 'train/total_time_seconds': '36.43', 'train/time_per_step_avg': '0.03033', 'train/epoch_time_elapsed': '145', 'train/estimated_remaining_minutes': '0.4048'}
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+{'loss': '0.257', 'grad_norm': '0.25', 'learning_rate': '0.0003', 'epoch': '0.08493', 'train/total_time_seconds': '38.3', 'train/time_per_step_avg': '0.03068', 'train/epoch_time_elapsed': '149.8', 'train/estimated_remaining_minutes': '0.3749'}
+{'loss': '0.252', 'grad_norm': '0.248', 'learning_rate': '0.0003', 'epoch': '0.08628', 'train/total_time_seconds': '38.89', 'train/time_per_step_avg': '0.03064', 'train/epoch_time_elapsed': '151.3', 'train/estimated_remaining_minutes': '0.3646'}
+{'loss': '0.2519', 'grad_norm': '0.2559', 'learning_rate': '0.0003', 'epoch': '0.08763', 'train/total_time_seconds': '39.49', 'train/time_per_step_avg': '0.03054', 'train/epoch_time_elapsed': '152.9', 'train/estimated_remaining_minutes': '0.3544'}
+{'loss': '0.2488', 'grad_norm': '0.25', 'learning_rate': '0.0003', 'epoch': '0.08898', 'train/total_time_seconds': '40.08', 'train/time_per_step_avg': '0.02989', 'train/epoch_time_elapsed': '154.4', 'train/estimated_remaining_minutes': '0.3441'}
+{'loss': '0.2476', 'grad_norm': '0.2471', 'learning_rate': '0.0003', 'epoch': '0.09033', 'train/total_time_seconds': '40.68', 'train/time_per_step_avg': '0.02984', 'train/epoch_time_elapsed': '156', 'train/estimated_remaining_minutes': '0.334'}
+{'loss': '0.2451', 'grad_norm': '0.249', 'learning_rate': '0.0003', 'epoch': '0.09168', 'train/total_time_seconds': '41.28', 'train/time_per_step_avg': '0.0298', 'train/epoch_time_elapsed': '157.5', 'train/estimated_remaining_minutes': '0.3237'}
+{'loss': '0.2446', 'grad_norm': '0.2441', 'learning_rate': '0.0003', 'epoch': '0.09302', 'train/total_time_seconds': '41.88', 'train/time_per_step_avg': '0.02985', 'train/epoch_time_elapsed': '159.1', 'train/estimated_remaining_minutes': '0.3136'}
+{'loss': '0.2422', 'grad_norm': '0.2441', 'learning_rate': '0.0003', 'epoch': '0.09437', 'train/total_time_seconds': '42.48', 'train/time_per_step_avg': '0.02991', 'train/epoch_time_elapsed': '160.6', 'train/estimated_remaining_minutes': '0.3034'}
+{'eval_loss': '0.2439', 'eval_runtime': '8.575', 'eval_samples_per_second': '1111', 'eval_steps_per_second': '17.38', 'epoch': '0.09437', 'train/total_time_seconds': '42.48', 'train/time_per_step_avg': '0.02991', 'train/epoch_time_elapsed': '169.2', 'train/estimated_remaining_minutes': '0.3034'}
+{'loss': '0.2416', 'grad_norm': '0.2559', 'learning_rate': '0.0003', 'epoch': '0.09572', 'train/total_time_seconds': '43.16', 'train/time_per_step_avg': '0.03078', 'train/epoch_time_elapsed': '170.9', 'train/estimated_remaining_minutes': '0.2938', 'train/global/act/norm': '2.71e+04', 'train/global/act/mean': '-0.1814', 'train/global/act/std': '0', 'train/global/act/max_abs': '11.06', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.2441', 'train/global/grad/mean': '-7.183e-07', 'train/global/grad/std': '0.0001724', 'train/global/grad/max_abs': '0.02637', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.22', 'train/global/param/mean': '0.001325', 'train/global/param/std': '0.05385', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.62', 'train/layer__model_layers_3/param/mean': '0.001481', 'train/layer__model_layers_3/param/std': '0.04594', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '18.58', 'train/layer__model_layers_4/param/mean': '0.001582', 'train/layer__model_layers_4/param/std': '0.04585', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '18.64', 'train/layer__model_layers_5/param/mean': '0.001559', 'train/layer__model_layers_5/param/std': '0.04599', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6112', 'train/layer_model_layers_7/act/mean': '0.04107', 'train/layer_model_layers_7/act/std': '0.7963', 'train/layer_model_layers_7/act/max_abs': '6.844', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.03104', 'train/layer_model_layers_7/grad/mean': '-2.288e-07', 'train/layer_model_layers_7/grad/std': '7.668e-05', 'train/layer_model_layers_7/grad/max_abs': '0.002136', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '5892', 'train/layer_model_layers_8/act/mean': '0.05636', 'train/layer_model_layers_8/act/std': '0.7669', 'train/layer_model_layers_8/act/max_abs': '6.5', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.02785', 'train/layer_model_layers_8/grad/mean': '2.236e-07', 'train/layer_model_layers_8/grad/std': '6.88e-05', 'train/layer_model_layers_8/grad/max_abs': '0.000927', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.43', 'train/layer__model_layers_0/param/mean': '0.001604', 'train/layer__model_layers_0/param/std': '0.04548', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6986', 'train/layer_model_layers_2/act/mean': '0.02908', 'train/layer_model_layers_2/act/std': '0.9104', 'train/layer_model_layers_2/act/max_abs': '6.25', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.04078', 'train/layer_model_layers_2/grad/mean': '4.985e-07', 'train/layer_model_layers_2/grad/std': '0.0001006', 'train/layer_mo
+{'loss': '0.2398', 'grad_norm': '0.252', 'learning_rate': '0.0003', 'epoch': '0.09707', 'train/total_time_seconds': '43.76', 'train/time_per_step_avg': '0.03071', 'train/epoch_time_elapsed': '172.4', 'train/estimated_remaining_minutes': '0.2836'}
+{'loss': '0.2378', 'grad_norm': '0.2598', 'learning_rate': '0.0003', 'epoch': '0.09842', 'train/total_time_seconds': '44.35', 'train/time_per_step_avg': '0.03072', 'train/epoch_time_elapsed': '174', 'train/estimated_remaining_minutes': '0.2734'}
+{'loss': '0.2357', 'grad_norm': '0.249', 'learning_rate': '0.0003', 'epoch': '0.09976', 'train/total_time_seconds': '44.94', 'train/time_per_step_avg': '0.03065', 'train/epoch_time_elapsed': '175.5', 'train/estimated_remaining_minutes': '0.2632'}
+{'loss': '0.235', 'grad_norm': '0.2402', 'learning_rate': '0.0003', 'epoch': '0.1011', 'train/total_time_seconds': '45.54', 'train/time_per_step_avg': '0.0306', 'train/epoch_time_elapsed': '177', 'train/estimated_remaining_minutes': '0.253'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 126.46it/s]
+ 80%|██████████████████████████████▍       | 1600/2000 [03:14<00:30, 13.02it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.2331', 'grad_norm': '0.2383', 'learning_rate': '0.0003', 'epoch': '0.1025', 'train/total_time_seconds': '46.15', 'train/time_per_step_avg': '0.02993', 'train/epoch_time_elapsed': '178.7', 'train/estimated_remaining_minutes': '0.2429'}
+{'loss': '0.2329', 'grad_norm': '0.2363', 'learning_rate': '0.0003', 'epoch': '0.1038', 'train/total_time_seconds': '46.75', 'train/time_per_step_avg': '0.02994', 'train/epoch_time_elapsed': '180.2', 'train/estimated_remaining_minutes': '0.2327'}
+{'loss': '0.232', 'grad_norm': '0.252', 'learning_rate': '0.0003', 'epoch': '0.1052', 'train/total_time_seconds': '47.35', 'train/time_per_step_avg': '0.03001', 'train/epoch_time_elapsed': '181.8', 'train/estimated_remaining_minutes': '0.2226'}
+{'loss': '0.2328', 'grad_norm': '0.2461', 'learning_rate': '0.0003', 'epoch': '0.1065', 'train/total_time_seconds': '47.95', 'train/time_per_step_avg': '0.03003', 'train/epoch_time_elapsed': '183.3', 'train/estimated_remaining_minutes': '0.2124'}
+{'loss': '0.2395', 'grad_norm': '1.297', 'learning_rate': '0.0003', 'epoch': '0.1079', 'train/total_time_seconds': '48.54', 'train/time_per_step_avg': '0.03005', 'train/epoch_time_elapsed': '184.9', 'train/estimated_remaining_minutes': '0.2023'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.2467', 'eval_runtime': '8.473', 'eval_samples_per_second': '1124', 'eval_steps_per_second': '17.59', 'epoch': '0.1079', 'train/total_time_seconds': '48.54', 'train/time_per_step_avg': '0.03005', 'train/epoch_time_elapsed': '193.3', 'train/estimated_remaining_minutes': '0.2023'}
+100%|██████████████████████████████████████| 2000/2000 [04:02<00:00, 12.96it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.2372', 'grad_norm': '0.5625', 'learning_rate': '0.0003', 'epoch': '0.1092', 'train/total_time_seconds': '49.21', 'train/time_per_step_avg': '0.03059', 'train/epoch_time_elapsed': '195.1', 'train/estimated_remaining_minutes': '0.1924', 'train/global/act/norm': '2.68e+04', 'train/global/act/mean': '-0.1909', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.94', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '1.299', 'train/global/grad/mean': '-1.278e-06', 'train/global/grad/std': '0.0009184', 'train/global/grad/max_abs': '0.0293', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.57', 'train/global/param/mean': '0.001328', 'train/global/param/std': '0.05402', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.62', 'train/layer__model_layers_3/param/mean': '0.001479', 'train/layer__model_layers_3/param/std': '0.04594', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '18.58', 'train/layer__model_layers_4/param/mean': '0.001581', 'train/layer__model_layers_4/param/std': '0.04585', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '18.65', 'train/layer__model_layers_5/param/mean': '0.001557', 'train/layer__model_layers_5/param/std': '0.04601', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '5954', 'train/layer_model_layers_7/act/mean': '0.03775', 'train/layer_model_layers_7/act/std': '0.7756', 'train/layer_model_layers_7/act/max_abs': '6.844', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.322', 'train/layer_model_layers_7/grad/mean': '1.048e-06', 'train/layer_model_layers_7/grad/std': '0.0007946', 'train/layer_model_layers_7/grad/max_abs': '0.01917', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '5790', 'train/layer_model_layers_8/act/mean': '0.05514', 'train/layer_model_layers_8/act/std': '0.7541', 'train/layer_model_layers_8/act/max_abs': '6.5', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.2949', 'train/layer_model_layers_8/grad/mean': '-1.855e-06', 'train/layer_model_layers_8/grad/std': '0.0007281', 'train/layer_model_layers_8/grad/max_abs': '0.01117', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.43', 'train/layer__model_layers_0/param/mean': '0.001604', 'train/layer__model_layers_0/param/std': '0.04548', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '6709', 'train/layer_model_layers_2/act/mean': '0.02841', 'train/layer_model_layers_2/act/std': '0.8741', 'train/layer_model_layers_2/act/max_abs': '5.875', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.4686', 'train/layer_model_layers_2/grad/mean': '-1.238e-06', 'train/layer_model_layers_2/grad/std': '0.001158', 'train/layer_model_lay
+{'loss': '0.2312', 'grad_norm': '0.3027', 'learning_rate': '0.0003', 'epoch': '0.1105', 'train/total_time_seconds': '49.81', 'train/time_per_step_avg': '0.03059', 'train/epoch_time_elapsed': '196.6', 'train/estimated_remaining_minutes': '0.1822'}
+{'loss': '0.2277', 'grad_norm': '0.2695', 'learning_rate': '0.0003', 'epoch': '0.1119', 'train/total_time_seconds': '50.4', 'train/time_per_step_avg': '0.03052', 'train/epoch_time_elapsed': '198.2', 'train/estimated_remaining_minutes': '0.1721'}
+{'loss': '0.227', 'grad_norm': '0.2334', 'learning_rate': '0.0003', 'epoch': '0.1132', 'train/total_time_seconds': '51', 'train/time_per_step_avg': '0.0305', 'train/epoch_time_elapsed': '199.7', 'train/estimated_remaining_minutes': '0.1619'}
+{'loss': '0.2273', 'grad_norm': '0.2275', 'learning_rate': '0.0003', 'epoch': '0.1146', 'train/total_time_seconds': '51.59', 'train/time_per_step_avg': '0.03049', 'train/epoch_time_elapsed': '201.3', 'train/estimated_remaining_minutes': '0.1517'}
+{'loss': '0.225', 'grad_norm': '0.2324', 'learning_rate': '0.0003', 'epoch': '0.1159', 'train/total_time_seconds': '52.19', 'train/time_per_step_avg': '0.02978', 'train/epoch_time_elapsed': '202.8', 'train/estimated_remaining_minutes': '0.1416'}
+{'loss': '0.2251', 'grad_norm': '0.2305', 'learning_rate': '0.0003', 'epoch': '0.1173', 'train/total_time_seconds': '52.78', 'train/time_per_step_avg': '0.02974', 'train/epoch_time_elapsed': '204.4', 'train/estimated_remaining_minutes': '0.1315'}
+{'loss': '0.2235', 'grad_norm': '0.2324', 'learning_rate': '0.0003', 'epoch': '0.1186', 'train/total_time_seconds': '53.37', 'train/time_per_step_avg': '0.02973', 'train/epoch_time_elapsed': '205.9', 'train/estimated_remaining_minutes': '0.1213'}
+{'loss': '0.2244', 'grad_norm': '0.2305', 'learning_rate': '0.0003', 'epoch': '0.12', 'train/total_time_seconds': '53.97', 'train/time_per_step_avg': '0.02971', 'train/epoch_time_elapsed': '207.4', 'train/estimated_remaining_minutes': '0.1112'}
+{'loss': '0.2244', 'grad_norm': '0.2246', 'learning_rate': '0.0003', 'epoch': '0.1213', 'train/total_time_seconds': '54.57', 'train/time_per_step_avg': '0.02977', 'train/epoch_time_elapsed': '209', 'train/estimated_remaining_minutes': '0.1011'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.2234', 'eval_runtime': '8.378', 'eval_samples_per_second': '1137', 'eval_steps_per_second': '17.78', 'epoch': '0.1213', 'train/total_time_seconds': '54.57', 'train/time_per_step_avg': '0.02977', 'train/epoch_time_elapsed': '217.4', 'train/estimated_remaining_minutes': '0.1011'}
+{'loss': '0.2234', 'grad_norm': '0.2314', 'learning_rate': '0.0003', 'epoch': '0.1227', 'train/total_time_seconds': '55.22', 'train/time_per_step_avg': '0.03026', 'train/epoch_time_elapsed': '219', 'train/estimated_remaining_minutes': '0.09102', 'train/global/act/norm': '2.715e+04', 'train/global/act/mean': '-0.1823', 'train/global/act/std': '0', 'train/global/act/max_abs': '11.12', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.2253', 'train/global/grad/mean': '-7.51e-07', 'train/global/grad/std': '0.0001591', 'train/global/grad/max_abs': '0.02466', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.77', 'train/global/param/mean': '0.001328', 'train/global/param/std': '0.05421', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.63', 'train/layer__model_layers_3/param/mean': '0.001479', 'train/layer__model_layers_3/param/std': '0.04596', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer__model_layers_4/param/norm': '18.59', 'train/layer__model_layers_4/param/mean': '0.001583', 'train/layer__model_layers_4/param/std': '0.04587', 'train/layer__model_layers_4/param/max_abs': '1', 'train/layer__model_layers_4/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_4/param/frac_near_user_limit': '0', 'train/layer__model_layers_5/param/norm': '18.66', 'train/layer__model_layers_5/param/mean': '0.001556', 'train/layer__model_layers_5/param/std': '0.04603', 'train/layer__model_layers_5/param/max_abs': '1', 'train/layer__model_layers_5/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_5/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6112', 'train/layer_model_layers_7/act/mean': '0.04053', 'train/layer_model_layers_7/act/std': '0.796', 'train/layer_model_layers_7/act/max_abs': '6.875', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.02108', 'train/layer_model_layers_7/grad/mean': '-4.173e-08', 'train/layer_model_layers_7/grad/std': '5.212e-05', 'train/layer_model_layers_7/grad/max_abs': '0.001556', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_8/act/norm': '5865', 'train/layer_model_layers_8/act/mean': '0.05609', 'train/layer_model_layers_8/act/std': '0.7636', 'train/layer_model_layers_8/act/max_abs': '6.531', 'train/layer_model_layers_8/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/act/frac_near_user_limit': '0', 'train/layer_model_layers_8/grad/norm': '0.02056', 'train/layer_model_layers_8/grad/mean': '-9.052e-08', 'train/layer_model_layers_8/grad/std': '5.076e-05', 'train/layer_model_layers_8/grad/max_abs': '0.000843', 'train/layer_model_layers_8/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_8/grad/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.45', 'train/layer__model_layers_0/param/mean': '0.001605', 'train/layer__model_layers_0/param/std': '0.04549', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '7010', 'train/layer_model_layers_2/act/mean': '0.03054', 'train/layer_model_layers_2/act/std': '0.9143', 'train/layer_model_layers_2/act/max_abs': '6.094', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.03143', 'train/layer_model_layers_2/grad/mean': '-2.366e-07', 'train/layer_model_layers_2/grad/std': '7.756e-05', 'train/layer
+{'loss': '0.2191', 'grad_norm': '0.2275', 'learning_rate': '0.0003', 'epoch': '0.124', 'train/total_time_seconds': '55.81', 'train/time_per_step_avg': '0.03023', 'train/epoch_time_elapsed': '220.6', 'train/estimated_remaining_minutes': '0.08088'}
+{'loss': '0.2187', 'grad_norm': '0.2354', 'learning_rate': '0.0003', 'epoch': '0.1254', 'train/total_time_seconds': '56.4', 'train/time_per_step_avg': '0.03028', 'train/epoch_time_elapsed': '222.1', 'train/estimated_remaining_minutes': '0.07076'}
+{'loss': '0.2183', 'grad_norm': '0.2305', 'learning_rate': '0.0003', 'epoch': '0.1267', 'train/total_time_seconds': '57.01', 'train/time_per_step_avg': '0.03048', 'train/epoch_time_elapsed': '223.7', 'train/estimated_remaining_minutes': '0.06065'}
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+{'loss': '0.2142', 'grad_norm': '0.2256', 'learning_rate': '0.0003', 'epoch': '0.1335', 'train/total_time_seconds': '59.99', 'train/time_per_step_avg': '0.02978', 'train/epoch_time_elapsed': '231.5', 'train/estimated_remaining_minutes': '0.0101'}
+{'loss': '0.2135', 'grad_norm': '0.2188', 'learning_rate': '0.0003', 'epoch': '0.1348', 'train/total_time_seconds': '60.59', 'train/time_per_step_avg': '0.02979', 'train/epoch_time_elapsed': '233.1', 'train/estimated_remaining_minutes': '0'}
+{'eval_loss': '0.2152', 'eval_runtime': '8.488', 'eval_samples_per_second': '1122', 'eval_steps_per_second': '17.55', 'epoch': '0.1348', 'train/total_time_seconds': '60.59', 'train/time_per_step_avg': '0.02979', 'train/epoch_time_elapsed': '241.6', 'train/estimated_remaining_minutes': '0'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 132.18it/s]
+100%|██████████████████████████████████████| 2000/2000 [04:02<00:00,  8.25it/s]
+{'train_runtime': '242.5', 'train_samples_per_second': '527.8', 'train_steps_per_second': '8.247', 'train_loss': '0.9127', 'epoch': '0.1348', 'train/total_time_seconds': '60.59', 'train/time_per_step_avg': '0.02979', 'train/epoch_time_elapsed': '241.6', 'train/estimated_remaining_minutes': '0'}
+100%|████████████████████████████████████████| 149/149 [00:08<00:00, 17.86it/s]
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 147.26it/s]
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 167.36it/s]
+Found 7 files to upload
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████░░░░░░░░░░░░  3 / 7
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████████████████  7 / 7 ✓
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 157.40it/s]
+Found 7 files to upload
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████████████████  7 / 7 ✓
+No files have been modified since last commit. Skipping to prevent empty commit.
diff --git a/wandb/run-20260806_203335-6n8s22c0/files/requirements.txt b/wandb/run-20260806_203335-6n8s22c0/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..123b15ebf857859624f7f4332e92341c8ef13fdf
--- /dev/null
+++ b/wandb/run-20260806_203335-6n8s22c0/files/requirements.txt
@@ -0,0 +1,149 @@
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+pure_eval==0.2.3
+Pygments==2.20.0
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+attrs==26.1.0
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+nvidia-cufile==1.15.1.6
+typer==0.27.0
+torchaudio==2.6.0+cu124
+rich==15.0.0
+nvidia-cufft-cu12==11.2.1.3
+h11==0.16.0
+dill==0.4.1
+cuda-pathfinder==1.6.0
+filelock==3.29.0
+nvidia-nvtx-cu12==12.4.127
+httpx==0.28.1
+anyio==4.14.2
+numpy==2.4.4
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+click==8.4.2
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+tokenizers==0.22.2
+markdown-it-py==4.2.0
+nvidia-cuda-runtime==13.0.96
+cuda-bindings==13.3.1
+nvidia-cuda-cupti==13.0.85
+torch==2.6.0+cu124
+multiprocess==0.70.19
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+transformers==5.15.0.dev0
+wandb==0.28.1
+nvidia-curand==10.4.0.35
+sympy==1.13.1
+nvidia-cusparse==12.6.3.3
+nvidia-cuda-nvrtc==13.0.88
+typing-inspection==0.4.2
+nvidia-cusolver==12.0.4.66
+nvidia-cufft==12.0.0.61
+nvidia-cudnn-cu13==9.20.0.48
+nvidia-cublas==13.1.1.3
+pyarrow==25.0.0
+evaluate==0.4.6
+diffusers==0.39.0
+pydantic==2.13.4
+annotated-types==0.8.0
+protobuf==7.35.1
+sentry-sdk==2.66.1
+einops==0.8.2
+packaging==26.2
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusparselt-cu12==0.6.2
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cuda-runtime-cu12==12.4.127
+torchvision==0.21.0+cu124
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cudnn-cu12==9.1.0.70
+huggingface_hub==1.26.0
+datasets==5.0.1
+safetensors==0.8.0
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+autocommand==2.2.2
+backports.tarfile==1.2.0
+importlib_metadata==8.7.1
+jaraco.text==4.0.0
+jaraco.context==6.1.0
+jaraco.functools==4.4.0
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+packaging==26.0
+platformdirs==4.4.0
+tomli==2.4.0
+wheel==0.46.3
+zipp==3.23.0
diff --git a/wandb/run-20260806_203335-6n8s22c0/files/wandb-metadata.json b/wandb/run-20260806_203335-6n8s22c0/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f870f0ffcfb37d37304b56e18c6f97d220d9d50c
--- /dev/null
+++ b/wandb/run-20260806_203335-6n8s22c0/files/wandb-metadata.json
@@ -0,0 +1,96 @@
+{
+  "os":  "Linux-5.15.0-126-generic-x86_64-with-glibc2.35",
+  "python":  "CPython 3.11.15",
+  "startedAt":  "2026-08-06T20:33:35.291538Z",
+  "args":  [
+    "--config",
+    "configs/baseline.yaml",
+    "--variants",
+    "glu-relu-9L",
+    "--push"
+  ],
+  "program":  "/mnt/data/zainulabideen/zain-exp/notebooks/Activation/sweep.py",
+  "codePath":  "sweep.py",
+  "codePathLocal":  "sweep.py",
+  "git":  {
+    "remote":  "https://github.com/deepnevro/Activation.git",
+    "commit":  "8d6f97d73f5521e4c38c6e1bda59bf8d4b489655"
+  },
+  "email":  "deepnevro@gmail.com",
+  "root":  "/mnt/data/zainulabideen/zain-exp/notebooks/Activation",
+  "host":  "deeplens-k3s-node1",
+  "executable":  "/mnt/data/zainulabideen/zain-exp/notebooks/my_env/bin/python",
+  "cpu_count":  112,
+  "cpu_count_logical":  224,
+  "gpu":  "NVIDIA H100 80GB HBM3",
+  "gpu_count":  8,
+  "disk":  {
+    "/":  {
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+      "used":  "703691034624"
+    }
+  },
+  "memory":  {
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+  },
+  "gpu_nvidia":  [
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
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+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-39c684a5-fde6-83d7-1663-0859795881ae"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-68012e5a-38b6-b643-0ca6-62fb66720bf3"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-132944c4-b689-2b5f-89a4-d730401677ab"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-2df386cc-6d26-d0e2-7a2d-a057b0d95864"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-bfa16575-1d94-1aa2-4537-2c93433f42ef"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-bc6c3e3c-9b90-09ca-c034-774961847c54"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-00a441e1-7c95-e7d6-4c35-43d6b291aea9"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-1c4d29a2-4647-6fce-d8fc-0c5ecfbbd6ea"
+    }
+  ],
+  "cudaVersion":  "12.4",
+  "writerId":  "o0ecltc5oxzt13s2rmnjk2x9n3b5dqo0"
+}
\ No newline at end of file
diff --git a/wandb/run-20260806_203335-6n8s22c0/files/wandb-summary.json b/wandb/run-20260806_203335-6n8s22c0/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..7aa93ec27502d8305423d654e7761c78ae3649b7
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..23d9b084df457da408838ed0ac4c07972e23510f
--- /dev/null
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+    value: null
+greater_is_better:
+    value: null
+head_dim:
+    value: 32
+hidden_act:
+    value: silu
+hidden_size:
+    value: 128
+hub_always_push:
+    value: false
+hub_model_id:
+    value: w-ahmad/ACTio-glu-gelu-9L
+hub_private_repo:
+    value: null
+hub_revision:
+    value: null
+hub_strategy:
+    value: every_save
+hub_token:
+    value: <HUB_TOKEN>
+id2label:
+    value:
+        "0": LABEL_0
+        "1": LABEL_1
+ignore_data_skip:
+    value: false
+include_for_metrics:
+    value: []
+include_num_input_tokens_seen:
+    value: "no"
+initializer_range:
+    value: 0.02
+intermediate_size:
+    value: 256
+is_encoder_decoder:
+    value: false
+label_names:
+    value: null
+label_smoothing_factor:
+    value: 0
+label2id:
+    value:
+        LABEL_0: 0
+        LABEL_1: 1
+learning_rate:
+    value: 0.0003
+length_column_name:
+    value: length
+liger_kernel_config:
+    value: null
+load_best_model_at_end:
+    value: false
+local_rank:
+    value: -1
+log_level:
+    value: passive
+log_level_replica:
+    value: warning
+log_on_each_node:
+    value: true
+logging_first_step:
+    value: false
+logging_nan_inf_filter:
+    value: true
+logging_steps:
+    value: 20
+logging_strategy:
+    value: steps
+lr_scheduler_kwargs:
+    value: null
+lr_scheduler_type:
+    value: constant
+max_grad_norm:
+    value: 1
+max_position_embeddings:
+    value: 512
+max_steps:
+    value: 2000
+metric_for_best_model:
+    value: null
+mlp_bias:
+    value: false
+mlp_type:
+    value: glu
+model/num_parameters:
+    value: 2001280
+model_type:
+    value: tiny_llama
+neftune_noise_alpha:
+    value: null
+num_attention_heads:
+    value: 4
+num_hidden_layers:
+    value: 9
+num_key_value_heads:
+    value: 4
+num_train_epochs:
+    value: 1
+optim:
+    value: adamw_torch
+optim_args:
+    value: null
+optim_target_modules:
+    value: null
+output_attentions:
+    value: false
+output_dir:
+    value: out/glu-gelu-9L_run
+output_hidden_states:
+    value: false
+pad_token_id:
+    value: 0
+parallelism_config:
+    value: null
+per_device_eval_batch_size:
+    value: 64
+per_device_train_batch_size:
+    value: 64
+prediction_loss_only:
+    value: false
+pretraining_tp:
+    value: 1
+problem_type:
+    value: null
+project:
+    value: huggingface
+push_to_hub:
+    value: true
+remove_unused_columns:
+    value: false
+report_to:
+    value:
+        - wandb
+restore_callback_states_from_checkpoint:
+    value: false
+resume_from_checkpoint:
+    value: null
+return_dict:
+    value: true
+rms_norm_eps:
+    value: 1e-06
+rope_parameters:
+    value:
+        rope_theta: 10000
+        rope_type: default
+run_name:
+    value: LM-glu-gelu-9L-2.0M-20260806-203757
+save_on_each_node:
+    value: false
+save_only_model:
+    value: false
+save_steps:
+    value: 500
+save_strategy:
+    value: steps
+save_total_limit:
+    value: null
+seed:
+    value: 42
+skip_memory_metrics:
+    value: true
+tf32:
+    value: null
+tie_word_embeddings:
+    value: true
+tokenizer_name:
+    value: w-ahmad/tiny-stories-tokenizer
+torch_compile:
+    value: false
+torch_compile_backend:
+    value: null
+torch_compile_mode:
+    value: null
+torch_empty_cache_steps:
+    value: null
+trackio_bucket_id:
+    value: null
+trackio_space_id:
+    value: null
+trackio_static_space_id:
+    value: null
+train_sampling_strategy:
+    value: random
+transformers_version:
+    value: 5.15.0.dev0
+use_cache:
+    value: false
+use_cpu:
+    value: false
+use_liger_kernel:
+    value: false
+vocab_size:
+    value: 4096
+warmup_steps:
+    value: 0
+weight_decay:
+    value: 0.01
diff --git a/wandb/run-20260806_203759-m4g42ash/files/output.log b/wandb/run-20260806_203759-m4g42ash/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..ad4b6f372469db83b7a163b12b8887a1b1235077
--- /dev/null
+++ b/wandb/run-20260806_203759-m4g42ash/files/output.log
@@ -0,0 +1,172 @@
+[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 25%|█████████▊                             | 500/2000 [00:57<01:58, 12.70it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '7.793', 'grad_norm': '1.258', 'learning_rate': '0.0003', 'epoch': '0.001348', 'train/total_time_seconds': '1.251', 'train/time_per_step_avg': '0.06255', 'train/epoch_time_elapsed': '2.376', 'train/estimated_remaining_minutes': '2.064', 'train/global/act/norm': '1.026e+04', 'train/global/act/mean': '0.007105', 'train/global/act/std': '0', 'train/global/act/max_abs': '8.328', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '2.198', 'train/global/grad/mean': '3.054e-07', 'train/global/grad/std': '0.001555', 'train/global/grad/max_abs': '0.1758', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '56.84', 'train/global/param/mean': '0.001193', 'train/global/param/std': '0.04018', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '17.93', 'train/layer__model_layers_0/param/mean': '0.001573', 'train/layer__model_layers_0/param/std': '0.04425', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '3158', 'train/layer_model_layers_7/act/mean': '0.004479', 'train/layer_model_layers_7/act/std': '0.4127', 'train/layer_model_layers_7/act/max_abs': '4.5', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.3784', 'train/layer_model_layers_7/grad/mean': '8.482e-07', 'train/layer_model_layers_7/grad/std': '0.000934', 'train/layer_model_layers_7/grad/max_abs': '0.02112', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '3147', 'train/layer_model_layers_1/act/mean': '-0.000155', 'train/layer_model_layers_1/act/std': '0.4112', 'train/layer_model_layers_1/act/max_abs': '4.562', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.8731', 'train/layer_model_layers_1/grad/mean': '7.075e-06', 'train/layer_model_layers_1/grad/std': '0.002155', 'train/layer_model_layers_1/grad/max_abs': '0.03687', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3141', 'train/layer_model_layers_0/act/mean': '-0.0005841', 'train/layer_model_layers_0/act/std': '0.4104', 'train/layer_model_layers_0/act/max_abs': '4.594', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.8738', 'train/layer_model_layers_0/grad/mean': '-1.131e-06', 'train/layer_model_layers_0/grad/std': '0.002157', 'train/layer_model_layers_0/grad/max_abs': '0.03735', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '17.93', 'train/layer__model_layers_2/param/mean': '0.001509', 'train/layer__model_layers_2/param/std': '0.04425', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '3147', 'train/layer_model_layers_6/act/mean': '0.012', 'train/layer_model_layers_6/act/std': '0.4107', 'train/layer_model_layers_6/act/max_abs': '4.438', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.4027', 'train/layer_model_layers_6/grad/mean': '5.374e-06', 'train/layer_model_layers_6/grad/std': '0.0009942', 'train/layer_model_layers_6/grad/max_abs
+{'loss': '7.021', 'grad_norm': '1.172', 'learning_rate': '0.0003', 'epoch': '0.002696', 'train/total_time_seconds': '1.837', 'train/time_per_step_avg': '0.04593', 'train/epoch_time_elapsed': '3.93', 'train/estimated_remaining_minutes': '1.5'}
+{'loss': '6.447', 'grad_norm': '1', 'learning_rate': '0.0003', 'epoch': '0.004044', 'train/total_time_seconds': '2.425', 'train/time_per_step_avg': '0.04041', 'train/epoch_time_elapsed': '5.465', 'train/estimated_remaining_minutes': '1.307'}
+{'loss': '5.99', 'grad_norm': '1.461', 'learning_rate': '0.0003', 'epoch': '0.005393', 'train/total_time_seconds': '3.01', 'train/time_per_step_avg': '0.03762', 'train/epoch_time_elapsed': '7.007', 'train/estimated_remaining_minutes': '1.204'}
+{'loss': '5.437', 'grad_norm': '2.328', 'learning_rate': '0.0003', 'epoch': '0.006741', 'train/total_time_seconds': '3.603', 'train/time_per_step_avg': '0.03603', 'train/epoch_time_elapsed': '8.583', 'train/estimated_remaining_minutes': '1.141'}
+{'loss': '4.911', 'grad_norm': '2.672', 'learning_rate': '0.0003', 'epoch': '0.008089', 'train/total_time_seconds': '4.188', 'train/time_per_step_avg': '0.02937', 'train/epoch_time_elapsed': '10.11', 'train/estimated_remaining_minutes': '1.093'}
+{'loss': '4.441', 'grad_norm': '3.281', 'learning_rate': '0.0003', 'epoch': '0.009437', 'train/total_time_seconds': '4.77', 'train/time_per_step_avg': '0.02933', 'train/epoch_time_elapsed': '11.63', 'train/estimated_remaining_minutes': '1.056'}
+{'loss': '4.035', 'grad_norm': '2.625', 'learning_rate': '0.0003', 'epoch': '0.01079', 'train/total_time_seconds': '5.355', 'train/time_per_step_avg': '0.02931', 'train/epoch_time_elapsed': '13.16', 'train/estimated_remaining_minutes': '1.026'}
+{'loss': '3.669', 'grad_norm': '3.766', 'learning_rate': '0.0003', 'epoch': '0.01213', 'train/total_time_seconds': '5.94', 'train/time_per_step_avg': '0.02931', 'train/epoch_time_elapsed': '14.69', 'train/estimated_remaining_minutes': '1.001'}
+{'loss': '3.315', 'grad_norm': '2.297', 'learning_rate': '0.0003', 'epoch': '0.01348', 'train/total_time_seconds': '6.528', 'train/time_per_step_avg': '0.02925', 'train/epoch_time_elapsed': '16.22', 'train/estimated_remaining_minutes': '0.9792'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '3.141', 'eval_runtime': '8.312', 'eval_samples_per_second': '1146', 'eval_steps_per_second': '17.93', 'epoch': '0.01348', 'train/total_time_seconds': '6.528', 'train/time_per_step_avg': '0.02925', 'train/epoch_time_elapsed': '24.53', 'train/estimated_remaining_minutes': '0.9792'}
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+{'loss': '2.315', 'grad_norm': '2.234', 'learning_rate': '0.0003', 'epoch': '0.01753', 'train/total_time_seconds': '8.357', 'train/time_per_step_avg': '0.03001', 'train/epoch_time_elapsed': '29.3', 'train/estimated_remaining_minutes': '0.9321'}
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+{'loss': '1.694', 'grad_norm': '1.609', 'learning_rate': '0.0003', 'epoch': '0.02022', 'train/total_time_seconds': '9.538', 'train/time_per_step_avg': '0.0301', 'train/epoch_time_elapsed': '32.39', 'train/estimated_remaining_minutes': '0.9008'}
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+{'eval_loss': '0.8561', 'eval_runtime': '8.366', 'eval_samples_per_second': '1139', 'eval_steps_per_second': '17.81', 'epoch': '0.02696', 'train/total_time_seconds': '12.53', 'train/time_per_step_avg': '0.02993', 'train/epoch_time_elapsed': '48.54', 'train/estimated_remaining_minutes': '0.8354'}
+{'loss': '0.7963', 'grad_norm': '0.6758', 'learning_rate': '0.0003', 'epoch': '0.02831', 'train/total_time_seconds': '13.18', 'train/time_per_step_avg': '0.03047', 'train/epoch_time_elapsed': '50.16', 'train/estimated_remaining_minutes': '0.8262', 'train/global/act/norm': '2.678e+04', 'train/global/act/mean': '-0.2321', 'train/global/act/std': '0', 'train/global/act/max_abs': '9.438', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.9731', 'train/global/grad/mean': '-1.893e-06', 'train/global/grad/std': '0.000688', 'train/global/grad/max_abs': '0.05176', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '68.08', 'train/global/param/mean': '0.001702', 'train/global/param/std': '0.0481', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.37', 'train/layer__model_layers_0/param/mean': '0.001548', 'train/layer__model_layers_0/param/std': '0.04535', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '5966', 'train/layer_model_layers_7/act/mean': '0.02058', 'train/layer_model_layers_7/act/std': '0.7788', 'train/layer_model_layers_7/act/max_abs': '4.406', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.2132', 'train/layer_model_layers_7/grad/mean': '-5.607e-07', 'train/layer_model_layers_7/grad/std': '0.0005258', 'train/layer_model_layers_7/grad/max_abs': '0.01019', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '3993', 'train/layer_model_layers_1/act/mean': '0.02454', 'train/layer_model_layers_1/act/std': '0.5206', 'train/layer_model_layers_1/act/max_abs': '3.891', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.4066', 'train/layer_model_layers_1/grad/mean': '-2.742e-06', 'train/layer_model_layers_1/grad/std': '0.001004', 'train/layer_model_layers_1/grad/max_abs': '0.01697', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3801', 'train/layer_model_layers_0/act/mean': '0.01181', 'train/layer_model_layers_0/act/std': '0.4956', 'train/layer_model_layers_0/act/max_abs': '4.281', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.3293', 'train/layer_model_layers_0/grad/mean': '1.946e-07', 'train/layer_model_layers_0/grad/std': '0.0008123', 'train/layer_model_layers_0/grad/max_abs': '0.0177', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.38', 'train/layer__model_layers_2/param/mean': '0.001454', 'train/layer__model_layers_2/param/std': '0.04535', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '6340', 'train/layer_model_layers_6/act/mean': '0.04304', 'train/layer_model_layers_6/act/std': '0.8263', 'train/layer_model_layers_6/act/max_abs': '5.219', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.2139', 'train/layer_model_layers_6/grad/mean': '-1.068e-06', 'train/layer_model_layers_6/grad/std': '0.0005278', 'train/layer_model_layers_6/grad/max
+{'loss': '0.7077', 'grad_norm': '0.5117', 'learning_rate': '0.0003', 'epoch': '0.02966', 'train/total_time_seconds': '13.77', 'train/time_per_step_avg': '0.03024', 'train/epoch_time_elapsed': '51.71', 'train/estimated_remaining_minutes': '0.8136'}
+{'loss': '0.6221', 'grad_norm': '0.4395', 'learning_rate': '0.0003', 'epoch': '0.03101', 'train/total_time_seconds': '14.36', 'train/time_per_step_avg': '0.03027', 'train/epoch_time_elapsed': '53.26', 'train/estimated_remaining_minutes': '0.8014'}
+{'loss': '0.5685', 'grad_norm': '0.4238', 'learning_rate': '0.0003', 'epoch': '0.03236', 'train/total_time_seconds': '14.98', 'train/time_per_step_avg': '0.03053', 'train/epoch_time_elapsed': '54.88', 'train/estimated_remaining_minutes': '0.7909'}
+{'loss': '0.5214', 'grad_norm': '0.3672', 'learning_rate': '0.0003', 'epoch': '0.0337', 'train/total_time_seconds': '15.59', 'train/time_per_step_avg': '0.03063', 'train/epoch_time_elapsed': '56.5', 'train/estimated_remaining_minutes': '0.7797'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 130.00it/s]
+ 30%|███████████▋                           | 600/2000 [01:13<01:48, 12.94it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.4872', 'grad_norm': '0.3809', 'learning_rate': '0.0003', 'epoch': '0.03505', 'train/total_time_seconds': '16.19', 'train/time_per_step_avg': '0.03012', 'train/epoch_time_elapsed': '58.11', 'train/estimated_remaining_minutes': '0.768'}
+{'loss': '0.4553', 'grad_norm': '0.3633', 'learning_rate': '0.0003', 'epoch': '0.0364', 'train/total_time_seconds': '16.78', 'train/time_per_step_avg': '0.03015', 'train/epoch_time_elapsed': '59.65', 'train/estimated_remaining_minutes': '0.7563'}
+{'loss': '0.4377', 'grad_norm': '0.3887', 'learning_rate': '0.0003', 'epoch': '0.03775', 'train/total_time_seconds': '17.38', 'train/time_per_step_avg': '0.03016', 'train/epoch_time_elapsed': '61.19', 'train/estimated_remaining_minutes': '0.7448'}
+{'loss': '0.419', 'grad_norm': '0.4004', 'learning_rate': '0.0003', 'epoch': '0.0391', 'train/total_time_seconds': '17.97', 'train/time_per_step_avg': '0.0299', 'train/epoch_time_elapsed': '62.74', 'train/estimated_remaining_minutes': '0.7335'}
+{'loss': '0.4081', 'grad_norm': '0.3926', 'learning_rate': '0.0003', 'epoch': '0.04044', 'train/total_time_seconds': '18.57', 'train/time_per_step_avg': '0.02978', 'train/epoch_time_elapsed': '64.29', 'train/estimated_remaining_minutes': '0.7223'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.4029', 'eval_runtime': '8.385', 'eval_samples_per_second': '1136', 'eval_steps_per_second': '17.77', 'epoch': '0.04044', 'train/total_time_seconds': '18.57', 'train/time_per_step_avg': '0.02978', 'train/epoch_time_elapsed': '72.68', 'train/estimated_remaining_minutes': '0.7223'}
+ 50%|███████████████████                   | 1000/2000 [02:01<01:16, 13.10it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.394', 'grad_norm': '0.4023', 'learning_rate': '0.0003', 'epoch': '0.04179', 'train/total_time_seconds': '19.22', 'train/time_per_step_avg': '0.03033', 'train/epoch_time_elapsed': '74.29', 'train/estimated_remaining_minutes': '0.7131', 'train/global/act/norm': '2.746e+04', 'train/global/act/mean': '-0.2223', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.5', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.3476', 'train/global/grad/mean': '-8.595e-07', 'train/global/grad/std': '0.0002459', 'train/global/grad/max_abs': '0.03198', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '72.94', 'train/global/param/mean': '0.001713', 'train/global/param/std': '0.05159', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.44', 'train/layer__model_layers_0/param/mean': '0.001552', 'train/layer__model_layers_0/param/std': '0.04551', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6310', 'train/layer_model_layers_7/act/mean': '0.03116', 'train/layer_model_layers_7/act/std': '0.8234', 'train/layer_model_layers_7/act/max_abs': '4.531', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.06227', 'train/layer_model_layers_7/grad/mean': '-2.631e-07', 'train/layer_model_layers_7/grad/std': '0.0001539', 'train/layer_model_layers_7/grad/max_abs': '0.001938', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '4168', 'train/layer_model_layers_1/act/mean': '0.02821', 'train/layer_model_layers_1/act/std': '0.5438', 'train/layer_model_layers_1/act/max_abs': '3.672', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.1092', 'train/layer_model_layers_1/grad/mean': '6.998e-07', 'train/layer_model_layers_1/grad/std': '0.0002697', 'train/layer_model_layers_1/grad/max_abs': '0.004547', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3869', 'train/layer_model_layers_0/act/mean': '0.01453', 'train/layer_model_layers_0/act/std': '0.5049', 'train/layer_model_layers_0/act/max_abs': '4.969', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.08383', 'train/layer_model_layers_0/grad/mean': '4.68e-07', 'train/layer_model_layers_0/grad/std': '0.000207', 'train/layer_model_layers_0/grad/max_abs': '0.00415', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.42', 'train/layer__model_layers_2/param/mean': '0.001457', 'train/layer__model_layers_2/param/std': '0.04546', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '6907', 'train/layer_model_layers_6/act/mean': '0.05505', 'train/layer_model_layers_6/act/std': '0.8993', 'train/layer_model_layers_6/act/max_abs': '5.594', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.06622', 'train/layer_model_layers_6/grad/mean': '2.311e-07', 'train/layer_model_layers_6/grad/std': '0.0001633', 'train/layer_model_layers_6/grad/
+{'loss': '0.3849', 'grad_norm': '0.4141', 'learning_rate': '0.0003', 'epoch': '0.04314', 'train/total_time_seconds': '19.82', 'train/time_per_step_avg': '0.03032', 'train/epoch_time_elapsed': '75.86', 'train/estimated_remaining_minutes': '0.7018'}
+{'loss': '0.3707', 'grad_norm': '0.377', 'learning_rate': '0.0003', 'epoch': '0.04449', 'train/total_time_seconds': '20.43', 'train/time_per_step_avg': '0.03055', 'train/epoch_time_elapsed': '77.49', 'train/estimated_remaining_minutes': '0.6914'}
+{'loss': '0.3638', 'grad_norm': '2.453', 'learning_rate': '0.0003', 'epoch': '0.04584', 'train/total_time_seconds': '21.03', 'train/time_per_step_avg': '0.03053', 'train/epoch_time_elapsed': '79.03', 'train/estimated_remaining_minutes': '0.6803'}
+{'loss': '0.3727', 'grad_norm': '1.172', 'learning_rate': '0.0003', 'epoch': '0.04719', 'train/total_time_seconds': '21.63', 'train/time_per_step_avg': '0.03053', 'train/epoch_time_elapsed': '80.57', 'train/estimated_remaining_minutes': '0.6694'}
+{'loss': '0.3607', 'grad_norm': '0.4492', 'learning_rate': '0.0003', 'epoch': '0.04853', 'train/total_time_seconds': '22.22', 'train/time_per_step_avg': '0.02994', 'train/epoch_time_elapsed': '82.1', 'train/estimated_remaining_minutes': '0.6583'}
+{'loss': '0.3455', 'grad_norm': '0.3359', 'learning_rate': '0.0003', 'epoch': '0.04988', 'train/total_time_seconds': '22.81', 'train/time_per_step_avg': '0.02997', 'train/epoch_time_elapsed': '83.68', 'train/estimated_remaining_minutes': '0.6474'}
+{'loss': '0.3414', 'grad_norm': '0.2715', 'learning_rate': '0.0003', 'epoch': '0.05123', 'train/total_time_seconds': '23.42', 'train/time_per_step_avg': '0.02982', 'train/epoch_time_elapsed': '85.29', 'train/estimated_remaining_minutes': '0.6367'}
+{'loss': '0.334', 'grad_norm': '0.2793', 'learning_rate': '0.0003', 'epoch': '0.05258', 'train/total_time_seconds': '24.01', 'train/time_per_step_avg': '0.02983', 'train/epoch_time_elapsed': '86.87', 'train/estimated_remaining_minutes': '0.6259'}
+{'loss': '0.3257', 'grad_norm': '0.2773', 'learning_rate': '0.0003', 'epoch': '0.05393', 'train/total_time_seconds': '24.63', 'train/time_per_step_avg': '0.03008', 'train/epoch_time_elapsed': '88.5', 'train/estimated_remaining_minutes': '0.6158'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.3262', 'eval_runtime': '8.4', 'eval_samples_per_second': '1134', 'eval_steps_per_second': '17.74', 'epoch': '0.05393', 'train/total_time_seconds': '24.63', 'train/time_per_step_avg': '0.03008', 'train/epoch_time_elapsed': '96.9', 'train/estimated_remaining_minutes': '0.6158'}
+{'loss': '0.3217', 'grad_norm': '0.2754', 'learning_rate': '0.0003', 'epoch': '0.05527', 'train/total_time_seconds': '25.28', 'train/time_per_step_avg': '0.03067', 'train/epoch_time_elapsed': '98.51', 'train/estimated_remaining_minutes': '0.6064', 'train/global/act/norm': '2.722e+04', 'train/global/act/mean': '-0.2102', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.69', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.286', 'train/global/grad/mean': '-8.789e-07', 'train/global/grad/std': '0.0002023', 'train/global/grad/max_abs': '0.02942', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '74.27', 'train/global/param/mean': '0.001701', 'train/global/param/std': '0.05259', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.46', 'train/layer__model_layers_0/param/mean': '0.001553', 'train/layer__model_layers_0/param/std': '0.04554', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6350', 'train/layer_model_layers_7/act/mean': '0.03303', 'train/layer_model_layers_7/act/std': '0.8279', 'train/layer_model_layers_7/act/max_abs': '4.594', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.04008', 'train/layer_model_layers_7/grad/mean': '1.136e-07', 'train/layer_model_layers_7/grad/std': '9.895e-05', 'train/layer_model_layers_7/grad/max_abs': '0.001656', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '4202', 'train/layer_model_layers_1/act/mean': '0.02912', 'train/layer_model_layers_1/act/std': '0.5472', 'train/layer_model_layers_1/act/max_abs': '3.812', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.07144', 'train/layer_model_layers_1/grad/mean': '-2.345e-07', 'train/layer_model_layers_1/grad/std': '0.0001765', 'train/layer_model_layers_1/grad/max_abs': '0.002899', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3893', 'train/layer_model_layers_0/act/mean': '0.01566', 'train/layer_model_layers_0/act/std': '0.5078', 'train/layer_model_layers_0/act/max_abs': '4.969', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.0574', 'train/layer_model_layers_0/grad/mean': '-1.233e-07', 'train/layer_model_layers_0/grad/std': '0.0001416', 'train/layer_model_layers_0/grad/max_abs': '0.003159', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.45', 'train/layer__model_layers_2/param/mean': '0.001456', 'train/layer__model_layers_2/param/std': '0.04552', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '7060', 'train/layer_model_layers_6/act/mean': '0.05703', 'train/layer_model_layers_6/act/std': '0.92', 'train/layer_model_layers_6/act/max_abs': '5.594', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.0391', 'train/layer_model_layers_6/grad/mean': '-1.78e-07', 'train/layer_model_layers_6/grad/std': '9.659e-05', 'train/layer_model_layers_6/gra
+{'loss': '0.3162', 'grad_norm': '0.291', 'learning_rate': '0.0003', 'epoch': '0.05662', 'train/total_time_seconds': '25.88', 'train/time_per_step_avg': '0.03063', 'train/epoch_time_elapsed': '100.1', 'train/estimated_remaining_minutes': '0.5956'}
+{'loss': '0.3093', 'grad_norm': '0.2617', 'learning_rate': '0.0003', 'epoch': '0.05797', 'train/total_time_seconds': '26.47', 'train/time_per_step_avg': '0.03057', 'train/epoch_time_elapsed': '101.6', 'train/estimated_remaining_minutes': '0.5849'}
+{'loss': '0.3028', 'grad_norm': '0.2773', 'learning_rate': '0.0003', 'epoch': '0.05932', 'train/total_time_seconds': '27.08', 'train/time_per_step_avg': '0.03066', 'train/epoch_time_elapsed': '103.2', 'train/estimated_remaining_minutes': '0.5744'}
+{'loss': '0.3017', 'grad_norm': '0.2871', 'learning_rate': '0.0003', 'epoch': '0.06067', 'train/total_time_seconds': '27.67', 'train/time_per_step_avg': '0.03035', 'train/epoch_time_elapsed': '104.8', 'train/estimated_remaining_minutes': '0.5636'}
+{'loss': '0.2986', 'grad_norm': '0.293', 'learning_rate': '0.0003', 'epoch': '0.06202', 'train/total_time_seconds': '28.26', 'train/time_per_step_avg': '0.02976', 'train/epoch_time_elapsed': '106.3', 'train/estimated_remaining_minutes': '0.5529'}
+{'loss': '0.2942', 'grad_norm': '0.3066', 'learning_rate': '0.0003', 'epoch': '0.06336', 'train/total_time_seconds': '28.85', 'train/time_per_step_avg': '0.02976', 'train/epoch_time_elapsed': '107.8', 'train/estimated_remaining_minutes': '0.5423'}
+{'loss': '0.2892', 'grad_norm': '0.3398', 'learning_rate': '0.0003', 'epoch': '0.06471', 'train/total_time_seconds': '29.44', 'train/time_per_step_avg': '0.02972', 'train/epoch_time_elapsed': '109.4', 'train/estimated_remaining_minutes': '0.5316'}
+{'loss': '0.2874', 'grad_norm': '0.2969', 'learning_rate': '0.0003', 'epoch': '0.06606', 'train/total_time_seconds': '30.04', 'train/time_per_step_avg': '0.0296', 'train/epoch_time_elapsed': '110.9', 'train/estimated_remaining_minutes': '0.5211'}
+{'loss': '0.2851', 'grad_norm': '0.3223', 'learning_rate': '0.0003', 'epoch': '0.06741', 'train/total_time_seconds': '30.63', 'train/time_per_step_avg': '0.02961', 'train/epoch_time_elapsed': '112.4', 'train/estimated_remaining_minutes': '0.5105'}
+{'eval_loss': '0.2846', 'eval_runtime': '8.55', 'eval_samples_per_second': '1114', 'eval_steps_per_second': '17.43', 'epoch': '0.06741', 'train/total_time_seconds': '30.63', 'train/time_per_step_avg': '0.02961', 'train/epoch_time_elapsed': '121', 'train/estimated_remaining_minutes': '0.5105'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 136.17it/s]
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 75%|████████████████████████████▌         | 1500/2000 [02:57<00:38, 13.14it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
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+{'loss': '0.2754', 'grad_norm': '0.3203', 'learning_rate': '0.0003', 'epoch': '0.07145', 'train/total_time_seconds': '32.47', 'train/time_per_step_avg': '0.03026', 'train/epoch_time_elapsed': '125.8', 'train/estimated_remaining_minutes': '0.4799'}
+{'loss': '0.2709', 'grad_norm': '0.3184', 'learning_rate': '0.0003', 'epoch': '0.0728', 'train/total_time_seconds': '33.07', 'train/time_per_step_avg': '0.0303', 'train/epoch_time_elapsed': '127.3', 'train/estimated_remaining_minutes': '0.4695'}
+{'loss': '0.2696', 'grad_norm': '0.3281', 'learning_rate': '0.0003', 'epoch': '0.07415', 'train/total_time_seconds': '33.66', 'train/time_per_step_avg': '0.03031', 'train/epoch_time_elapsed': '128.9', 'train/estimated_remaining_minutes': '0.459'}
+{'loss': '0.2661', 'grad_norm': '0.3672', 'learning_rate': '0.0003', 'epoch': '0.0755', 'train/total_time_seconds': '34.25', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '130.4', 'train/estimated_remaining_minutes': '0.4485'}
+{'loss': '0.2777', 'grad_norm': '1.828', 'learning_rate': '0.0003', 'epoch': '0.07685', 'train/total_time_seconds': '34.84', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '131.9', 'train/estimated_remaining_minutes': '0.438'}
+{'loss': '0.2816', 'grad_norm': '0.5391', 'learning_rate': '0.0003', 'epoch': '0.07819', 'train/total_time_seconds': '35.45', 'train/time_per_step_avg': '0.02979', 'train/epoch_time_elapsed': '133.6', 'train/estimated_remaining_minutes': '0.4278'}
+{'loss': '0.2674', 'grad_norm': '0.3672', 'learning_rate': '0.0003', 'epoch': '0.07954', 'train/total_time_seconds': '36.04', 'train/time_per_step_avg': '0.02973', 'train/epoch_time_elapsed': '135.1', 'train/estimated_remaining_minutes': '0.4174'}
+{'loss': '0.2629', 'grad_norm': '0.2754', 'learning_rate': '0.0003', 'epoch': '0.08089', 'train/total_time_seconds': '36.64', 'train/time_per_step_avg': '0.02981', 'train/epoch_time_elapsed': '136.6', 'train/estimated_remaining_minutes': '0.4071'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.2618', 'eval_runtime': '8.353', 'eval_samples_per_second': '1140', 'eval_steps_per_second': '17.84', 'epoch': '0.08089', 'train/total_time_seconds': '36.64', 'train/time_per_step_avg': '0.02981', 'train/epoch_time_elapsed': '145', 'train/estimated_remaining_minutes': '0.4071'}
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+{'loss': '0.2547', 'grad_norm': '0.2578', 'learning_rate': '0.0003', 'epoch': '0.08493', 'train/total_time_seconds': '38.48', 'train/time_per_step_avg': '0.03026', 'train/epoch_time_elapsed': '149.7', 'train/estimated_remaining_minutes': '0.3766'}
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+{'loss': '0.2501', 'grad_norm': '0.2598', 'learning_rate': '0.0003', 'epoch': '0.08763', 'train/total_time_seconds': '39.66', 'train/time_per_step_avg': '0.0302', 'train/epoch_time_elapsed': '152.8', 'train/estimated_remaining_minutes': '0.3559'}
+{'loss': '0.2471', 'grad_norm': '0.2471', 'learning_rate': '0.0003', 'epoch': '0.08898', 'train/total_time_seconds': '40.27', 'train/time_per_step_avg': '0.02977', 'train/epoch_time_elapsed': '154.4', 'train/estimated_remaining_minutes': '0.3457'}
+{'loss': '0.2463', 'grad_norm': '0.25', 'learning_rate': '0.0003', 'epoch': '0.09033', 'train/total_time_seconds': '40.86', 'train/time_per_step_avg': '0.02981', 'train/epoch_time_elapsed': '156', 'train/estimated_remaining_minutes': '0.3354'}
+{'loss': '0.2437', 'grad_norm': '0.3242', 'learning_rate': '0.0003', 'epoch': '0.09168', 'train/total_time_seconds': '41.46', 'train/time_per_step_avg': '0.02983', 'train/epoch_time_elapsed': '157.6', 'train/estimated_remaining_minutes': '0.3252'}
+{'loss': '0.2432', 'grad_norm': '0.2598', 'learning_rate': '0.0003', 'epoch': '0.09302', 'train/total_time_seconds': '42.05', 'train/time_per_step_avg': '0.02986', 'train/epoch_time_elapsed': '159.1', 'train/estimated_remaining_minutes': '0.3149'}
+{'loss': '0.241', 'grad_norm': '0.2656', 'learning_rate': '0.0003', 'epoch': '0.09437', 'train/total_time_seconds': '42.65', 'train/time_per_step_avg': '0.02985', 'train/epoch_time_elapsed': '160.7', 'train/estimated_remaining_minutes': '0.3046'}
+{'eval_loss': '0.2429', 'eval_runtime': '8.402', 'eval_samples_per_second': '1134', 'eval_steps_per_second': '17.73', 'epoch': '0.09437', 'train/total_time_seconds': '42.65', 'train/time_per_step_avg': '0.02985', 'train/epoch_time_elapsed': '169.1', 'train/estimated_remaining_minutes': '0.3046'}
+{'loss': '0.2406', 'grad_norm': '0.2656', 'learning_rate': '0.0003', 'epoch': '0.09572', 'train/total_time_seconds': '43.29', 'train/time_per_step_avg': '0.03026', 'train/epoch_time_elapsed': '170.7', 'train/estimated_remaining_minutes': '0.2947', 'train/global/act/norm': '2.73e+04', 'train/global/act/mean': '-0.1952', 'train/global/act/std': '0', 'train/global/act/max_abs': '11', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.2593', 'train/global/grad/mean': '-7.841e-07', 'train/global/grad/std': '0.000183', 'train/global/grad/max_abs': '0.02588', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.35', 'train/global/param/mean': '0.001704', 'train/global/param/std': '0.05398', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.49', 'train/layer__model_layers_0/param/mean': '0.001559', 'train/layer__model_layers_0/param/std': '0.04563', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6498', 'train/layer_model_layers_7/act/mean': '0.03682', 'train/layer_model_layers_7/act/std': '0.8473', 'train/layer_model_layers_7/act/max_abs': '4.625', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.03814', 'train/layer_model_layers_7/grad/mean': '4.878e-08', 'train/layer_model_layers_7/grad/std': '9.399e-05', 'train/layer_model_layers_7/grad/max_abs': '0.001671', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '4297', 'train/layer_model_layers_1/act/mean': '0.03224', 'train/layer_model_layers_1/act/std': '0.5599', 'train/layer_model_layers_1/act/max_abs': '3.516', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.05603', 'train/layer_model_layers_1/grad/mean': '-3.847e-08', 'train/layer_model_layers_1/grad/std': '0.0001384', 'train/layer_model_layers_1/grad/max_abs': '0.002686', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3941', 'train/layer_model_layers_0/act/mean': '0.01736', 'train/layer_model_layers_0/act/std': '0.5142', 'train/layer_model_layers_0/act/max_abs': '4.969', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.04523', 'train/layer_model_layers_0/grad/mean': '-9.137e-08', 'train/layer_model_layers_0/grad/std': '0.0001116', 'train/layer_model_layers_0/grad/max_abs': '0.00296', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.5', 'train/layer__model_layers_2/param/mean': '0.001461', 'train/layer__model_layers_2/param/std': '0.04565', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '7428', 'train/layer_model_layers_6/act/mean': '0.06227', 'train/layer_model_layers_6/act/std': '0.9667', 'train/layer_model_layers_6/act/max_abs': '5.75', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.03938', 'train/layer_model_layers_6/grad/mean': '-1.197e-07', 'train/layer_model_layers_6/grad/std': '9.716e-05', 'train/layer_model_layers_6/grad/
+{'loss': '0.2387', 'grad_norm': '0.2559', 'learning_rate': '0.0003', 'epoch': '0.09707', 'train/total_time_seconds': '43.88', 'train/time_per_step_avg': '0.03016', 'train/epoch_time_elapsed': '172.2', 'train/estimated_remaining_minutes': '0.2844'}
+{'loss': '0.2365', 'grad_norm': '0.2676', 'learning_rate': '0.0003', 'epoch': '0.09842', 'train/total_time_seconds': '44.47', 'train/time_per_step_avg': '0.03007', 'train/epoch_time_elapsed': '173.7', 'train/estimated_remaining_minutes': '0.2741'}
+{'loss': '0.2346', 'grad_norm': '0.2578', 'learning_rate': '0.0003', 'epoch': '0.09976', 'train/total_time_seconds': '45.06', 'train/time_per_step_avg': '0.03011', 'train/epoch_time_elapsed': '175.3', 'train/estimated_remaining_minutes': '0.2639'}
+{'loss': '0.2339', 'grad_norm': '0.2461', 'learning_rate': '0.0003', 'epoch': '0.1011', 'train/total_time_seconds': '45.65', 'train/time_per_step_avg': '0.03006', 'train/epoch_time_elapsed': '176.8', 'train/estimated_remaining_minutes': '0.2536'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 141.22it/s]
+ 80%|██████████████████████████████▍       | 1600/2000 [03:13<00:30, 13.15it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.232', 'grad_norm': '0.2451', 'learning_rate': '0.0003', 'epoch': '0.1025', 'train/total_time_seconds': '46.25', 'train/time_per_step_avg': '0.02959', 'train/epoch_time_elapsed': '178.4', 'train/estimated_remaining_minutes': '0.2434'}
+{'loss': '0.2319', 'grad_norm': '0.291', 'learning_rate': '0.0003', 'epoch': '0.1038', 'train/total_time_seconds': '46.85', 'train/time_per_step_avg': '0.02966', 'train/epoch_time_elapsed': '180', 'train/estimated_remaining_minutes': '0.2332'}
+{'loss': '0.2309', 'grad_norm': '0.3008', 'learning_rate': '0.0003', 'epoch': '0.1052', 'train/total_time_seconds': '47.44', 'train/time_per_step_avg': '0.02975', 'train/epoch_time_elapsed': '181.5', 'train/estimated_remaining_minutes': '0.223'}
+{'loss': '0.2315', 'grad_norm': '0.2637', 'learning_rate': '0.0003', 'epoch': '0.1065', 'train/total_time_seconds': '48.03', 'train/time_per_step_avg': '0.02966', 'train/epoch_time_elapsed': '183.1', 'train/estimated_remaining_minutes': '0.2128'}
+{'loss': '0.2452', 'grad_norm': '1.055', 'learning_rate': '0.0003', 'epoch': '0.1079', 'train/total_time_seconds': '48.62', 'train/time_per_step_avg': '0.02968', 'train/epoch_time_elapsed': '184.6', 'train/estimated_remaining_minutes': '0.2026'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.2447', 'eval_runtime': '8.389', 'eval_samples_per_second': '1136', 'eval_steps_per_second': '17.76', 'epoch': '0.1079', 'train/total_time_seconds': '48.62', 'train/time_per_step_avg': '0.02968', 'train/epoch_time_elapsed': '193', 'train/estimated_remaining_minutes': '0.2026'}
+100%|██████████████████████████████████████| 2000/2000 [04:01<00:00, 13.20it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.2353', 'grad_norm': '0.4004', 'learning_rate': '0.0003', 'epoch': '0.1092', 'train/total_time_seconds': '49.27', 'train/time_per_step_avg': '0.03015', 'train/epoch_time_elapsed': '194.6', 'train/estimated_remaining_minutes': '0.1926', 'train/global/act/norm': '2.687e+04', 'train/global/act/mean': '-0.2123', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.88', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '1.175', 'train/global/grad/mean': '-7.121e-07', 'train/global/grad/std': '0.0008306', 'train/global/grad/max_abs': '0.02576', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.71', 'train/global/param/mean': '0.001704', 'train/global/param/std': '0.05416', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.49', 'train/layer__model_layers_0/param/mean': '0.001561', 'train/layer__model_layers_0/param/std': '0.04564', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6137', 'train/layer_model_layers_7/act/mean': '0.03363', 'train/layer_model_layers_7/act/std': '0.7998', 'train/layer_model_layers_7/act/max_abs': '4.562', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.2828', 'train/layer_model_layers_7/grad/mean': '-1.581e-07', 'train/layer_model_layers_7/grad/std': '0.000698', 'train/layer_model_layers_7/grad/max_abs': '0.008057', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '4211', 'train/layer_model_layers_1/act/mean': '0.03115', 'train/layer_model_layers_1/act/std': '0.5484', 'train/layer_model_layers_1/act/max_abs': '3.562', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.5197', 'train/layer_model_layers_1/grad/mean': '5.253e-06', 'train/layer_model_layers_1/grad/std': '0.001282', 'train/layer_model_layers_1/grad/max_abs': '0.01929', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3906', 'train/layer_model_layers_0/act/mean': '0.01724', 'train/layer_model_layers_0/act/std': '0.5096', 'train/layer_model_layers_0/act/max_abs': '4.906', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.4255', 'train/layer_model_layers_0/grad/mean': '-1.429e-06', 'train/layer_model_layers_0/grad/std': '0.00105', 'train/layer_model_layers_0/grad/max_abs': '0.02209', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.5', 'train/layer__model_layers_2/param/mean': '0.001462', 'train/layer__model_layers_2/param/std': '0.04565', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '6881', 'train/layer_model_layers_6/act/mean': '0.05659', 'train/layer_model_layers_6/act/std': '0.895', 'train/layer_model_layers_6/act/max_abs': '5.562', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.3321', 'train/layer_model_layers_6/grad/mean': '2.649e-08', 'train/layer_model_layers_6/grad/std': '0.0008194', 'train/layer_model_layers_6/grad/max_abs
+{'loss': '0.2299', 'grad_norm': '0.2676', 'learning_rate': '0.0003', 'epoch': '0.1105', 'train/total_time_seconds': '49.87', 'train/time_per_step_avg': '0.03022', 'train/epoch_time_elapsed': '196.2', 'train/estimated_remaining_minutes': '0.1824'}
+{'loss': '0.2266', 'grad_norm': '0.2344', 'learning_rate': '0.0003', 'epoch': '0.1119', 'train/total_time_seconds': '50.46', 'train/time_per_step_avg': '0.03019', 'train/epoch_time_elapsed': '197.7', 'train/estimated_remaining_minutes': '0.1723'}
+{'loss': '0.2256', 'grad_norm': '0.2314', 'learning_rate': '0.0003', 'epoch': '0.1132', 'train/total_time_seconds': '51.05', 'train/time_per_step_avg': '0.03018', 'train/epoch_time_elapsed': '199.2', 'train/estimated_remaining_minutes': '0.1621'}
+{'loss': '0.2257', 'grad_norm': '0.2373', 'learning_rate': '0.0003', 'epoch': '0.1146', 'train/total_time_seconds': '51.64', 'train/time_per_step_avg': '0.03016', 'train/epoch_time_elapsed': '200.8', 'train/estimated_remaining_minutes': '0.1519'}
+{'loss': '0.2234', 'grad_norm': '0.2285', 'learning_rate': '0.0003', 'epoch': '0.1159', 'train/total_time_seconds': '52.23', 'train/time_per_step_avg': '0.02958', 'train/epoch_time_elapsed': '202.3', 'train/estimated_remaining_minutes': '0.1417'}
+{'loss': '0.2234', 'grad_norm': '0.2314', 'learning_rate': '0.0003', 'epoch': '0.1173', 'train/total_time_seconds': '52.82', 'train/time_per_step_avg': '0.02951', 'train/epoch_time_elapsed': '203.8', 'train/estimated_remaining_minutes': '0.1315'}
+{'loss': '0.2217', 'grad_norm': '0.2305', 'learning_rate': '0.0003', 'epoch': '0.1186', 'train/total_time_seconds': '53.41', 'train/time_per_step_avg': '0.02949', 'train/epoch_time_elapsed': '205.4', 'train/estimated_remaining_minutes': '0.1214'}
+{'loss': '0.2223', 'grad_norm': '0.2227', 'learning_rate': '0.0003', 'epoch': '0.12', 'train/total_time_seconds': '54', 'train/time_per_step_avg': '0.02949', 'train/epoch_time_elapsed': '206.9', 'train/estimated_remaining_minutes': '0.1112'}
+{'loss': '0.2224', 'grad_norm': '0.2285', 'learning_rate': '0.0003', 'epoch': '0.1213', 'train/total_time_seconds': '54.59', 'train/time_per_step_avg': '0.02953', 'train/epoch_time_elapsed': '208.4', 'train/estimated_remaining_minutes': '0.1011'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.2213', 'eval_runtime': '8.38', 'eval_samples_per_second': '1137', 'eval_steps_per_second': '17.78', 'epoch': '0.1213', 'train/total_time_seconds': '54.59', 'train/time_per_step_avg': '0.02953', 'train/epoch_time_elapsed': '216.8', 'train/estimated_remaining_minutes': '0.1011'}
+{'loss': '0.2214', 'grad_norm': '0.2285', 'learning_rate': '0.0003', 'epoch': '0.1227', 'train/total_time_seconds': '55.23', 'train/time_per_step_avg': '0.03009', 'train/epoch_time_elapsed': '218.4', 'train/estimated_remaining_minutes': '0.09105', 'train/global/act/norm': '2.733e+04', 'train/global/act/mean': '-0.1947', 'train/global/act/std': '0', 'train/global/act/max_abs': '11.06', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.2284', 'train/global/grad/mean': '-7.851e-07', 'train/global/grad/std': '0.0001613', 'train/global/grad/max_abs': '0.02441', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '76.9', 'train/global/param/mean': '0.001707', 'train/global/param/std': '0.05435', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer__model_layers_0/param/norm': '18.51', 'train/layer__model_layers_0/param/mean': '0.001561', 'train/layer__model_layers_0/param/std': '0.04565', 'train/layer__model_layers_0/param/max_abs': '1', 'train/layer__model_layers_0/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_0/param/frac_near_user_limit': '0', 'train/layer_model_layers_7/act/norm': '6503', 'train/layer_model_layers_7/act/mean': '0.03784', 'train/layer_model_layers_7/act/std': '0.8481', 'train/layer_model_layers_7/act/max_abs': '4.688', 'train/layer_model_layers_7/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/act/frac_near_user_limit': '0', 'train/layer_model_layers_7/grad/norm': '0.02747', 'train/layer_model_layers_7/grad/mean': '8.677e-08', 'train/layer_model_layers_7/grad/std': '6.792e-05', 'train/layer_model_layers_7/grad/max_abs': '0.001068', 'train/layer_model_layers_7/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_7/grad/frac_near_user_limit': '0', 'train/layer_model_layers_1/act/norm': '4294', 'train/layer_model_layers_1/act/mean': '0.03249', 'train/layer_model_layers_1/act/std': '0.5597', 'train/layer_model_layers_1/act/max_abs': '3.469', 'train/layer_model_layers_1/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/act/frac_near_user_limit': '0', 'train/layer_model_layers_1/grad/norm': '0.03765', 'train/layer_model_layers_1/grad/mean': '-1.013e-06', 'train/layer_model_layers_1/grad/std': '9.308e-05', 'train/layer_model_layers_1/grad/max_abs': '0.00145', 'train/layer_model_layers_1/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_1/grad/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3936', 'train/layer_model_layers_0/act/mean': '0.01763', 'train/layer_model_layers_0/act/std': '0.5132', 'train/layer_model_layers_0/act/max_abs': '4.844', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.0312', 'train/layer_model_layers_0/grad/mean': '-6.246e-08', 'train/layer_model_layers_0/grad/std': '7.709e-05', 'train/layer_model_layers_0/grad/max_abs': '0.001762', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer__model_layers_2/param/norm': '18.51', 'train/layer__model_layers_2/param/mean': '0.001462', 'train/layer__model_layers_2/param/std': '0.04567', 'train/layer__model_layers_2/param/max_abs': '1', 'train/layer__model_layers_2/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_2/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '7469', 'train/layer_model_layers_6/act/mean': '0.0632', 'train/layer_model_layers_6/act/std': '0.9727', 'train/layer_model_layers_6/act/max_abs': '5.875', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.03061', 'train/layer_model_layers_6/grad/mean': '2.501e-08', 'train/layer_model_layers_6/grad/std': '7.545e-05', 'train/layer_model_layers_6/gr
+{'loss': '0.2172', 'grad_norm': '0.2324', 'learning_rate': '0.0003', 'epoch': '0.124', 'train/total_time_seconds': '55.82', 'train/time_per_step_avg': '0.03004', 'train/epoch_time_elapsed': '220', 'train/estimated_remaining_minutes': '0.0809'}
+{'loss': '0.2171', 'grad_norm': '0.2354', 'learning_rate': '0.0003', 'epoch': '0.1254', 'train/total_time_seconds': '56.42', 'train/time_per_step_avg': '0.03011', 'train/epoch_time_elapsed': '221.5', 'train/estimated_remaining_minutes': '0.07078'}
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+{'loss': '0.2131', 'grad_norm': '0.2256', 'learning_rate': '0.0003', 'epoch': '0.1321', 'train/total_time_seconds': '59.37', 'train/time_per_step_avg': '0.02951', 'train/epoch_time_elapsed': '229.2', 'train/estimated_remaining_minutes': '0.02019'}
+{'loss': '0.214', 'grad_norm': '0.2227', 'learning_rate': '0.0003', 'epoch': '0.1335', 'train/total_time_seconds': '59.96', 'train/time_per_step_avg': '0.0295', 'train/epoch_time_elapsed': '230.7', 'train/estimated_remaining_minutes': '0.01009'}
+{'loss': '0.2135', 'grad_norm': '0.2207', 'learning_rate': '0.0003', 'epoch': '0.1348', 'train/total_time_seconds': '60.54', 'train/time_per_step_avg': '0.02948', 'train/epoch_time_elapsed': '232.2', 'train/estimated_remaining_minutes': '0'}
+{'eval_loss': '0.2151', 'eval_runtime': '8.412', 'eval_samples_per_second': '1133', 'eval_steps_per_second': '17.71', 'epoch': '0.1348', 'train/total_time_seconds': '60.54', 'train/time_per_step_avg': '0.02948', 'train/epoch_time_elapsed': '240.6', 'train/estimated_remaining_minutes': '0'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 136.50it/s]
+100%|██████████████████████████████████████| 2000/2000 [04:01<00:00,  8.28it/s]
+{'train_runtime': '241.6', 'train_samples_per_second': '529.8', 'train_steps_per_second': '8.279', 'train_loss': '0.9439', 'epoch': '0.1348', 'train/total_time_seconds': '60.54', 'train/time_per_step_avg': '0.02948', 'train/epoch_time_elapsed': '240.7', 'train/estimated_remaining_minutes': '0'}
+100%|████████████████████████████████████████| 149/149 [00:08<00:00, 17.96it/s]
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 140.62it/s]
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 168.45it/s]
+Found 7 files to upload
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████░░░░░░░░░░░░  3 / 7
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████░░░░░░░░░░░░  3 / 7
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████████████████  7 / 7 ✓
+[transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 159.98it/s]
+Found 7 files to upload
+  Preparing   ████████████████████  7 / 7 ✓
+  Uploading   ████████████████████  2 / 2 files ✓
+  Committing  ████████████████████  7 / 7 ✓
+No files have been modified since last commit. Skipping to prevent empty commit.
diff --git a/wandb/run-20260806_203759-m4g42ash/files/requirements.txt b/wandb/run-20260806_203759-m4g42ash/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..123b15ebf857859624f7f4332e92341c8ef13fdf
--- /dev/null
+++ b/wandb/run-20260806_203759-m4g42ash/files/requirements.txt
@@ -0,0 +1,149 @@
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+parso==0.8.7
+platformdirs==4.11.0
+psutil==7.2.2
+ptyprocess==0.7.0
+pure_eval==0.2.3
+Pygments==2.20.0
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+six==1.17.0
+tornado==6.5.7
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+fsspec==2026.4.0
+wcwidth==0.8.2
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+jupyter_core==5.9.1
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+attrs==26.1.0
+hf-xet==1.5.2
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+aiosignal==1.4.0
+xxhash==3.8.1
+aiohappyeyeballs==2.7.1
+mdurl==0.1.2
+cuda-toolkit==13.0.3.0
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+PyYAML==6.0.3
+nvidia-cufile==1.15.1.6
+typer==0.27.0
+torchaudio==2.6.0+cu124
+rich==15.0.0
+nvidia-cufft-cu12==11.2.1.3
+h11==0.16.0
+dill==0.4.1
+cuda-pathfinder==1.6.0
+filelock==3.29.0
+nvidia-nvtx-cu12==12.4.127
+httpx==0.28.1
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+yarl==1.24.5
+click==8.4.2
+triton==3.2.0
+frozenlist==1.8.0
+zipp==4.1.0
+propcache==0.5.2
+tokenizers==0.22.2
+markdown-it-py==4.2.0
+nvidia-cuda-runtime==13.0.96
+cuda-bindings==13.3.1
+nvidia-cuda-cupti==13.0.85
+torch==2.6.0+cu124
+multiprocess==0.70.19
+pillow==12.2.0
+transformers==5.15.0.dev0
+wandb==0.28.1
+nvidia-curand==10.4.0.35
+sympy==1.13.1
+nvidia-cusparse==12.6.3.3
+nvidia-cuda-nvrtc==13.0.88
+typing-inspection==0.4.2
+nvidia-cusolver==12.0.4.66
+nvidia-cufft==12.0.0.61
+nvidia-cudnn-cu13==9.20.0.48
+nvidia-cublas==13.1.1.3
+pyarrow==25.0.0
+evaluate==0.4.6
+diffusers==0.39.0
+pydantic==2.13.4
+annotated-types==0.8.0
+protobuf==7.35.1
+sentry-sdk==2.66.1
+einops==0.8.2
+packaging==26.2
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusparselt-cu12==0.6.2
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cuda-runtime-cu12==12.4.127
+torchvision==0.21.0+cu124
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cudnn-cu12==9.1.0.70
+huggingface_hub==1.26.0
+datasets==5.0.1
+safetensors==0.8.0
+accelerate==1.14.0
+pydantic_core==2.46.4
+ninja==1.13.0
+autocommand==2.2.2
+backports.tarfile==1.2.0
+importlib_metadata==8.7.1
+jaraco.text==4.0.0
+jaraco.context==6.1.0
+jaraco.functools==4.4.0
+more-itertools==10.8.0
+packaging==26.0
+platformdirs==4.4.0
+tomli==2.4.0
+wheel==0.46.3
+zipp==3.23.0
diff --git a/wandb/run-20260806_203759-m4g42ash/files/wandb-metadata.json b/wandb/run-20260806_203759-m4g42ash/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a19231c6f25ba4fef2b9649187239457223d1960
--- /dev/null
+++ b/wandb/run-20260806_203759-m4g42ash/files/wandb-metadata.json
@@ -0,0 +1,96 @@
+{
+  "os":  "Linux-5.15.0-126-generic-x86_64-with-glibc2.35",
+  "python":  "CPython 3.11.15",
+  "startedAt":  "2026-08-06T20:37:59.135434Z",
+  "args":  [
+    "--config",
+    "configs/baseline.yaml",
+    "--variants",
+    "glu-gelu-9L",
+    "--push"
+  ],
+  "program":  "/mnt/data/zainulabideen/zain-exp/notebooks/Activation/sweep.py",
+  "codePath":  "sweep.py",
+  "codePathLocal":  "sweep.py",
+  "git":  {
+    "remote":  "https://github.com/deepnevro/Activation.git",
+    "commit":  "8d6f97d73f5521e4c38c6e1bda59bf8d4b489655"
+  },
+  "email":  "deepnevro@gmail.com",
+  "root":  "/mnt/data/zainulabideen/zain-exp/notebooks/Activation",
+  "host":  "deeplens-k3s-node1",
+  "executable":  "/mnt/data/zainulabideen/zain-exp/notebooks/my_env/bin/python",
+  "cpu_count":  112,
+  "cpu_count_logical":  224,
+  "gpu":  "NVIDIA H100 80GB HBM3",
+  "gpu_count":  8,
+  "disk":  {
+    "/":  {
+      "total":  "1560765693952",
+      "used":  "703694221312"
+    }
+  },
+  "memory":  {
+    "total":  "2164089937920"
+  },
+  "gpu_nvidia":  [
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-39c684a5-fde6-83d7-1663-0859795881ae"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-68012e5a-38b6-b643-0ca6-62fb66720bf3"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-132944c4-b689-2b5f-89a4-d730401677ab"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-2df386cc-6d26-d0e2-7a2d-a057b0d95864"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-bfa16575-1d94-1aa2-4537-2c93433f42ef"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-bc6c3e3c-9b90-09ca-c034-774961847c54"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-00a441e1-7c95-e7d6-4c35-43d6b291aea9"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-1c4d29a2-4647-6fce-d8fc-0c5ecfbbd6ea"
+    }
+  ],
+  "cudaVersion":  "12.4",
+  "writerId":  "8ox7pcl4tzlbpzobhn0e14htw3g6a1bh"
+}
\ No newline at end of file
diff --git a/wandb/run-20260806_203759-m4g42ash/files/wandb-summary.json b/wandb/run-20260806_203759-m4g42ash/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..8df1434ed0f88bba5963e84a7de8ca3dc5a092d7
--- /dev/null
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diff --git a/wandb/run-20260806_203759-m4g42ash/logs/debug-internal.log b/wandb/run-20260806_203759-m4g42ash/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..41dcfe26f881bbbd422e76adc9d74e79e182b3e6
--- /dev/null
+++ b/wandb/run-20260806_203759-m4g42ash/logs/debug-internal.log
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+[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
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+ 25%|█████████▊                             | 500/2000 [00:57<01:54, 13.05it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
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+{'loss': '3.918', 'grad_norm': '2.328', 'learning_rate': '0.0003', 'epoch': '0.01079', 'train/total_time_seconds': '5.358', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '13.34', 'train/estimated_remaining_minutes': '1.027'}
+{'loss': '3.572', 'grad_norm': '2.359', 'learning_rate': '0.0003', 'epoch': '0.01213', 'train/total_time_seconds': '5.95', 'train/time_per_step_avg': '0.02964', 'train/epoch_time_elapsed': '14.88', 'train/estimated_remaining_minutes': '1.003'}
+{'loss': '3.229', 'grad_norm': '2.891', 'learning_rate': '0.0003', 'epoch': '0.01348', 'train/total_time_seconds': '6.537', 'train/time_per_step_avg': '0.02959', 'train/epoch_time_elapsed': '16.43', 'train/estimated_remaining_minutes': '0.9805'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '3.063', 'eval_runtime': '8.403', 'eval_samples_per_second': '1134', 'eval_steps_per_second': '17.73', 'epoch': '0.01348', 'train/total_time_seconds': '6.537', 'train/time_per_step_avg': '0.02959', 'train/epoch_time_elapsed': '24.83', 'train/estimated_remaining_minutes': '0.9805'}
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+{'loss': '2.574', 'grad_norm': '2.219', 'learning_rate': '0.0003', 'epoch': '0.01618', 'train/total_time_seconds': '7.775', 'train/time_per_step_avg': '0.03007', 'train/epoch_time_elapsed': '28.03', 'train/estimated_remaining_minutes': '0.9502'}
+{'loss': '2.251', 'grad_norm': '2.875', 'learning_rate': '0.0003', 'epoch': '0.01753', 'train/total_time_seconds': '8.362', 'train/time_per_step_avg': '0.03004', 'train/epoch_time_elapsed': '29.57', 'train/estimated_remaining_minutes': '0.9326'}
+{'loss': '1.948', 'grad_norm': '1.836', 'learning_rate': '0.0003', 'epoch': '0.01887', 'train/total_time_seconds': '8.949', 'train/time_per_step_avg': '0.03', 'train/epoch_time_elapsed': '31.1', 'train/estimated_remaining_minutes': '0.9163'}
+{'loss': '1.683', 'grad_norm': '2.828', 'learning_rate': '0.0003', 'epoch': '0.02022', 'train/total_time_seconds': '9.543', 'train/time_per_step_avg': '0.03006', 'train/epoch_time_elapsed': '32.66', 'train/estimated_remaining_minutes': '0.9013'}
+{'loss': '1.489', 'grad_norm': '2.031', 'learning_rate': '0.0003', 'epoch': '0.02157', 'train/total_time_seconds': '10.13', 'train/time_per_step_avg': '0.02945', 'train/epoch_time_elapsed': '34.19', 'train/estimated_remaining_minutes': '0.8865'}
+{'loss': '1.314', 'grad_norm': '1.797', 'learning_rate': '0.0003', 'epoch': '0.02292', 'train/total_time_seconds': '10.72', 'train/time_per_step_avg': '0.02945', 'train/epoch_time_elapsed': '35.73', 'train/estimated_remaining_minutes': '0.8723'}
+{'loss': '1.153', 'grad_norm': '2.516', 'learning_rate': '0.0003', 'epoch': '0.02427', 'train/total_time_seconds': '11.31', 'train/time_per_step_avg': '0.02946', 'train/epoch_time_elapsed': '37.27', 'train/estimated_remaining_minutes': '0.8586'}
+{'loss': '1.04', 'grad_norm': '1.492', 'learning_rate': '0.0003', 'epoch': '0.02562', 'train/total_time_seconds': '11.9', 'train/time_per_step_avg': '0.02951', 'train/epoch_time_elapsed': '38.85', 'train/estimated_remaining_minutes': '0.8455'}
+{'loss': '0.9419', 'grad_norm': '1.953', 'learning_rate': '0.0003', 'epoch': '0.02696', 'train/total_time_seconds': '12.49', 'train/time_per_step_avg': '0.0295', 'train/epoch_time_elapsed': '40.39', 'train/estimated_remaining_minutes': '0.8329'}
+{'eval_loss': '0.9078', 'eval_runtime': '8.327', 'eval_samples_per_second': '1144', 'eval_steps_per_second': '17.89', 'epoch': '0.02696', 'train/total_time_seconds': '12.49', 'train/time_per_step_avg': '0.0295', 'train/epoch_time_elapsed': '48.72', 'train/estimated_remaining_minutes': '0.8329'}
+{'loss': '0.8664', 'grad_norm': '1.062', 'learning_rate': '0.0003', 'epoch': '0.02831', 'train/total_time_seconds': '13.14', 'train/time_per_step_avg': '0.03006', 'train/epoch_time_elapsed': '50.33', 'train/estimated_remaining_minutes': '0.8237', 'train/global/act/norm': '2.595e+04', 'train/global/act/mean': '-0.2266', 'train/global/act/std': '0', 'train/global/act/max_abs': '9.312', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '1.438', 'train/global/grad/mean': '-8.595e-07', 'train/global/grad/std': '0.001016', 'train/global/grad/max_abs': '0.05908', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '67.28', 'train/global/param/mean': '0.0008617', 'train/global/param/std': '0.04753', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3802', 'train/layer_model_layers_0/act/mean': '-0.01041', 'train/layer_model_layers_0/act/std': '0.4964', 'train/layer_model_layers_0/act/max_abs': '4.344', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.4642', 'train/layer_model_layers_0/grad/mean': '-4.98e-07', 'train/layer_model_layers_0/grad/std': '0.001145', 'train/layer_model_layers_0/grad/max_abs': '0.02039', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '5956', 'train/layer_model_layers_5/act/mean': '0.02052', 'train/layer_model_layers_5/act/std': '0.7772', 'train/layer_model_layers_5/act/max_abs': '5.062', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.3912', 'train/layer_model_layers_5/grad/mean': '1.74e-06', 'train/layer_model_layers_5/grad/std': '0.0009653', 'train/layer_model_layers_5/grad/max_abs': '0.01373', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.56', 'train/layer__model_layers_6/param/mean': '0.001659', 'train/layer__model_layers_6/param/std': '0.04579', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '6419', 'train/layer_model_layers_6/act/mean': '0.01154', 'train/layer_model_layers_6/act/std': '0.8373', 'train/layer_model_layers_6/act/max_abs': '5.406', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.3557', 'train/layer_model_layers_6/grad/mean': '-1.964e-09', 'train/layer_model_layers_6/grad/std': '0.0008778', 'train/layer_model_layers_6/grad/max_abs': '0.01282', 'train/layer_model_layers_6/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/grad/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.3', 'train/layer__model_layers_3/param/mean': '0.001464', 'train/layer__model_layers_3/param/std': '0.04517', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5171', 'train/layer_model_layers_2/act/mean': '-0.01036', 'train/layer_model_layers_2/act/std': '0.674', 'train/layer_model_layers_2/act/max_abs': '4.906', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.519', 'train/layer_model_layers_2/grad/mean': '2.548e-06', 'train/layer_model_layers_2/grad/std': '0.001281', 'train/layer_model_layers_2/grad/max_abs
+{'loss': '0.8063', 'grad_norm': '1.602', 'learning_rate': '0.0003', 'epoch': '0.02966', 'train/total_time_seconds': '13.73', 'train/time_per_step_avg': '0.03013', 'train/epoch_time_elapsed': '51.92', 'train/estimated_remaining_minutes': '0.8115'}
+{'loss': '0.7396', 'grad_norm': '1.172', 'learning_rate': '0.0003', 'epoch': '0.03101', 'train/total_time_seconds': '14.33', 'train/time_per_step_avg': '0.0302', 'train/epoch_time_elapsed': '53.5', 'train/estimated_remaining_minutes': '0.7995'}
+{'loss': '0.6793', 'grad_norm': '0.8125', 'learning_rate': '0.0003', 'epoch': '0.03236', 'train/total_time_seconds': '14.92', 'train/time_per_step_avg': '0.03016', 'train/epoch_time_elapsed': '55.04', 'train/estimated_remaining_minutes': '0.7872'}
+{'loss': '0.6012', 'grad_norm': '0.4531', 'learning_rate': '0.0003', 'epoch': '0.0337', 'train/total_time_seconds': '15.5', 'train/time_per_step_avg': '0.03012', 'train/epoch_time_elapsed': '56.58', 'train/estimated_remaining_minutes': '0.7752'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|██████████████████████| 1/1 [00:00<00:00, 97.74it/s]
+ 30%|███████████▋                           | 600/2000 [01:13<01:48, 12.93it/s]/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+{'loss': '0.5412', 'grad_norm': '0.4238', 'learning_rate': '0.0003', 'epoch': '0.03505', 'train/total_time_seconds': '16.1', 'train/time_per_step_avg': '0.0296', 'train/epoch_time_elapsed': '58.21', 'train/estimated_remaining_minutes': '0.7636'}
+{'loss': '0.4908', 'grad_norm': '0.4473', 'learning_rate': '0.0003', 'epoch': '0.0364', 'train/total_time_seconds': '16.69', 'train/time_per_step_avg': '0.02958', 'train/epoch_time_elapsed': '59.76', 'train/estimated_remaining_minutes': '0.7521'}
+{'loss': '0.4596', 'grad_norm': '0.3965', 'learning_rate': '0.0003', 'epoch': '0.03775', 'train/total_time_seconds': '17.28', 'train/time_per_step_avg': '0.02952', 'train/epoch_time_elapsed': '61.33', 'train/estimated_remaining_minutes': '0.7406'}
+{'loss': '0.4302', 'grad_norm': '0.3906', 'learning_rate': '0.0003', 'epoch': '0.0391', 'train/total_time_seconds': '17.87', 'train/time_per_step_avg': '0.02954', 'train/epoch_time_elapsed': '62.87', 'train/estimated_remaining_minutes': '0.7292'}
+{'loss': '0.4119', 'grad_norm': '0.373', 'learning_rate': '0.0003', 'epoch': '0.04044', 'train/total_time_seconds': '18.46', 'train/time_per_step_avg': '0.02953', 'train/epoch_time_elapsed': '64.41', 'train/estimated_remaining_minutes': '0.7178'}
+  "std": tensor.std().item(),                                                  
+{'eval_loss': '0.4039', 'eval_runtime': '8.439', 'eval_samples_per_second': '1129', 'eval_steps_per_second': '17.66', 'epoch': '0.04044', 'train/total_time_seconds': '18.46', 'train/time_per_step_avg': '0.02953', 'train/epoch_time_elapsed': '72.85', 'train/estimated_remaining_minutes': '0.7178'}
+ 50%|███████████████████                   | 1000/2000 [02:01<01:17, 12.97it/s][transformers] TinyLlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+{'loss': '0.3931', 'grad_norm': '0.3438', 'learning_rate': '0.0003', 'epoch': '0.04179', 'train/total_time_seconds': '19.1', 'train/time_per_step_avg': '0.03006', 'train/epoch_time_elapsed': '74.46', 'train/estimated_remaining_minutes': '0.7087', 'train/global/act/norm': '2.701e+04', 'train/global/act/mean': '-0.2341', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.5', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.345', 'train/global/grad/mean': '-1.042e-06', 'train/global/grad/std': '0.0002439', 'train/global/grad/max_abs': '0.03345', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '72.52', 'train/global/param/mean': '0.0008311', 'train/global/param/std': '0.05126', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3896', 'train/layer_model_layers_0/act/mean': '-0.009663', 'train/layer_model_layers_0/act/std': '0.5083', 'train/layer_model_layers_0/act/max_abs': '5.656', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.08229', 'train/layer_model_layers_0/grad/mean': '-1.555e-07', 'train/layer_model_layers_0/grad/std': '0.0002033', 'train/layer_model_layers_0/grad/max_abs': '0.004944', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '6428', 'train/layer_model_layers_5/act/mean': '0.02088', 'train/layer_model_layers_5/act/std': '0.8378', 'train/layer_model_layers_5/act/max_abs': '5.438', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.08104', 'train/layer_model_layers_5/grad/mean': '-1.003e-06', 'train/layer_model_layers_5/grad/std': '0.0001999', 'train/layer_model_layers_5/grad/max_abs': '0.003799', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.61', 'train/layer__model_layers_6/param/mean': '0.001663', 'train/layer__model_layers_6/param/std': '0.04592', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '6936', 'train/layer_model_layers_6/act/mean': '0.01074', 'train/layer_model_layers_6/act/std': '0.9056', 'train/layer_model_layers_6/act/max_abs': '5.781', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.06652', 'train/layer_model_layers_6/grad/mean': '-1.542e-07', 'train/layer_model_layers_6/grad/std': '0.0001641', 'train/layer_model_layers_6/grad/max_abs': '0.002167', 'train/layer_model_layers_6/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/grad/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.33', 'train/layer__model_layers_3/param/mean': '0.001468', 'train/layer__model_layers_3/param/std': '0.04523', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5402', 'train/layer_model_layers_2/act/mean': '-0.009951', 'train/layer_model_layers_2/act/std': '0.7046', 'train/layer_model_layers_2/act/max_abs': '4.969', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.09333', 'train/layer_model_layers_2/grad/mean': '4.529e-07', 'train/layer_model_layers_2/grad/std': '0.0002303', 'train/layer_model_laye
+{'loss': '0.381', 'grad_norm': '0.3652', 'learning_rate': '0.0003', 'epoch': '0.04314', 'train/total_time_seconds': '19.7', 'train/time_per_step_avg': '0.03008', 'train/epoch_time_elapsed': '76.04', 'train/estimated_remaining_minutes': '0.6977'}
+{'loss': '0.365', 'grad_norm': '0.3652', 'learning_rate': '0.0003', 'epoch': '0.04449', 'train/total_time_seconds': '20.29', 'train/time_per_step_avg': '0.03005', 'train/epoch_time_elapsed': '77.57', 'train/estimated_remaining_minutes': '0.6864'}
+{'loss': '0.3559', 'grad_norm': '0.4414', 'learning_rate': '0.0003', 'epoch': '0.04584', 'train/total_time_seconds': '20.87', 'train/time_per_step_avg': '0.03003', 'train/epoch_time_elapsed': '79.11', 'train/estimated_remaining_minutes': '0.6753'}
+{'loss': '0.3458', 'grad_norm': '0.3418', 'learning_rate': '0.0003', 'epoch': '0.04719', 'train/total_time_seconds': '21.46', 'train/time_per_step_avg': '0.03002', 'train/epoch_time_elapsed': '80.65', 'train/estimated_remaining_minutes': '0.6643'}
+{'loss': '0.3411', 'grad_norm': '1.555', 'learning_rate': '0.0003', 'epoch': '0.04853', 'train/total_time_seconds': '22.05', 'train/time_per_step_avg': '0.02944', 'train/epoch_time_elapsed': '82.18', 'train/estimated_remaining_minutes': '0.6533'}
+{'loss': '0.3416', 'grad_norm': '1', 'learning_rate': '0.0003', 'epoch': '0.04988', 'train/total_time_seconds': '22.64', 'train/time_per_step_avg': '0.02944', 'train/epoch_time_elapsed': '83.75', 'train/estimated_remaining_minutes': '0.6426'}
+{'loss': '0.3456', 'grad_norm': '1.289', 'learning_rate': '0.0003', 'epoch': '0.05123', 'train/total_time_seconds': '23.25', 'train/time_per_step_avg': '0.0296', 'train/epoch_time_elapsed': '85.34', 'train/estimated_remaining_minutes': '0.6321'}
+{'loss': '0.3341', 'grad_norm': '0.4473', 'learning_rate': '0.0003', 'epoch': '0.05258', 'train/total_time_seconds': '23.84', 'train/time_per_step_avg': '0.02963', 'train/epoch_time_elapsed': '86.88', 'train/estimated_remaining_minutes': '0.6214'}
+{'loss': '0.3213', 'grad_norm': '0.3125', 'learning_rate': '0.0003', 'epoch': '0.05393', 'train/total_time_seconds': '24.43', 'train/time_per_step_avg': '0.02973', 'train/epoch_time_elapsed': '88.44', 'train/estimated_remaining_minutes': '0.6108'}
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+{'eval_loss': '0.3212', 'eval_runtime': '8.4', 'eval_samples_per_second': '1134', 'eval_steps_per_second': '17.74', 'epoch': '0.05393', 'train/total_time_seconds': '24.43', 'train/time_per_step_avg': '0.02973', 'train/epoch_time_elapsed': '96.84', 'train/estimated_remaining_minutes': '0.6108'}
+{'loss': '0.3162', 'grad_norm': '0.2715', 'learning_rate': '0.0003', 'epoch': '0.05527', 'train/total_time_seconds': '25.08', 'train/time_per_step_avg': '0.03032', 'train/epoch_time_elapsed': '98.45', 'train/estimated_remaining_minutes': '0.6015', 'train/global/act/norm': '2.677e+04', 'train/global/act/mean': '-0.2249', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.69', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.3042', 'train/global/grad/mean': '-8.95e-07', 'train/global/grad/std': '0.0002148', 'train/global/grad/max_abs': '0.03015', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '73.99', 'train/global/param/mean': '0.0008435', 'train/global/param/std': '0.05224', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3924', 'train/layer_model_layers_0/act/mean': '-0.01009', 'train/layer_model_layers_0/act/std': '0.5124', 'train/layer_model_layers_0/act/max_abs': '5.781', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.06474', 'train/layer_model_layers_0/grad/mean': '1.255e-09', 'train/layer_model_layers_0/grad/std': '0.0001601', 'train/layer_model_layers_0/grad/max_abs': '0.003967', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '6438', 'train/layer_model_layers_5/act/mean': '0.02096', 'train/layer_model_layers_5/act/std': '0.84', 'train/layer_model_layers_5/act/max_abs': '5.375', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.06812', 'train/layer_model_layers_5/grad/mean': '-9.167e-07', 'train/layer_model_layers_5/grad/std': '0.0001683', 'train/layer_model_layers_5/grad/max_abs': '0.002823', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.63', 'train/layer__model_layers_6/param/mean': '0.001667', 'train/layer__model_layers_6/param/std': '0.04597', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '6995', 'train/layer_model_layers_6/act/mean': '0.009783', 'train/layer_model_layers_6/act/std': '0.9127', 'train/layer_model_layers_6/act/max_abs': '5.875', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.05793', 'train/layer_model_layers_6/grad/mean': '2.534e-08', 'train/layer_model_layers_6/grad/std': '0.0001429', 'train/layer_model_layers_6/grad/max_abs': '0.002762', 'train/layer_model_layers_6/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/grad/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.34', 'train/layer__model_layers_3/param/mean': '0.001472', 'train/layer__model_layers_3/param/std': '0.04526', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5439', 'train/layer_model_layers_2/act/mean': '-0.009496', 'train/layer_model_layers_2/act/std': '0.7099', 'train/layer_model_layers_2/act/max_abs': '5.031', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.06961', 'train/layer_model_layers_2/grad/mean': '7.275e-07', 'train/layer_model_layers_2/grad/std': '0.0001719', 'train/layer_model_layers
+{'loss': '0.3103', 'grad_norm': '0.2676', 'learning_rate': '0.0003', 'epoch': '0.05662', 'train/total_time_seconds': '25.68', 'train/time_per_step_avg': '0.03041', 'train/epoch_time_elapsed': '100.1', 'train/estimated_remaining_minutes': '0.5911'}
+{'loss': '0.3027', 'grad_norm': '0.2617', 'learning_rate': '0.0003', 'epoch': '0.05797', 'train/total_time_seconds': '26.27', 'train/time_per_step_avg': '0.03025', 'train/epoch_time_elapsed': '101.6', 'train/estimated_remaining_minutes': '0.5804'}
+{'loss': '0.2957', 'grad_norm': '0.2656', 'learning_rate': '0.0003', 'epoch': '0.05932', 'train/total_time_seconds': '26.86', 'train/time_per_step_avg': '0.03025', 'train/epoch_time_elapsed': '103.1', 'train/estimated_remaining_minutes': '0.5698'}
+{'loss': '0.2944', 'grad_norm': '0.2656', 'learning_rate': '0.0003', 'epoch': '0.06067', 'train/total_time_seconds': '27.45', 'train/time_per_step_avg': '0.03015', 'train/epoch_time_elapsed': '104.7', 'train/estimated_remaining_minutes': '0.5591'}
+{'loss': '0.2906', 'grad_norm': '0.2676', 'learning_rate': '0.0003', 'epoch': '0.06202', 'train/total_time_seconds': '28.05', 'train/time_per_step_avg': '0.02973', 'train/epoch_time_elapsed': '106.2', 'train/estimated_remaining_minutes': '0.5489'}
+{'loss': '0.2863', 'grad_norm': '0.2754', 'learning_rate': '0.0003', 'epoch': '0.06336', 'train/total_time_seconds': '28.64', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '107.8', 'train/estimated_remaining_minutes': '0.5383'}
+{'loss': '0.2804', 'grad_norm': '0.2559', 'learning_rate': '0.0003', 'epoch': '0.06471', 'train/total_time_seconds': '29.23', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '109.3', 'train/estimated_remaining_minutes': '0.5277'}
+{'loss': '0.2781', 'grad_norm': '0.2891', 'learning_rate': '0.0003', 'epoch': '0.06606', 'train/total_time_seconds': '29.82', 'train/time_per_step_avg': '0.02956', 'train/epoch_time_elapsed': '110.8', 'train/estimated_remaining_minutes': '0.5172'}
+{'loss': '0.2753', 'grad_norm': '0.2812', 'learning_rate': '0.0003', 'epoch': '0.06741', 'train/total_time_seconds': '30.41', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '112.4', 'train/estimated_remaining_minutes': '0.5068'}
+{'eval_loss': '0.2748', 'eval_runtime': '8.447', 'eval_samples_per_second': '1128', 'eval_steps_per_second': '17.64', 'epoch': '0.06741', 'train/total_time_seconds': '30.41', 'train/time_per_step_avg': '0.02957', 'train/epoch_time_elapsed': '120.8', 'train/estimated_remaining_minutes': '0.5068'}
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+Writing model shards: 100%|█████████████████████| 1/1 [00:00<00:00, 149.33it/s]
+/mnt/data/zainulabideen/zain-exp/notebooks/Activation/exp.py:464: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1831.)
+  "std": tensor.std().item(),
+ 71%|██████████████████████████▊           | 1411/2000 [02:50<02:52,  3.41it/s]
+{'loss': '0.2703', 'grad_norm': '0.2676', 'learning_rate': '0.0003', 'epoch': '0.06876', 'train/total_time_seconds': '31.05', 'train/time_per_step_avg': '0.03003', 'train/epoch_time_elapsed': '122.5', 'train/estimated_remaining_minutes': '0.4973', 'train/global/act/norm': '2.682e+04', 'train/global/act/mean': '-0.2167', 'train/global/act/std': '0', 'train/global/act/max_abs': '10.94', 'train/global/act/frac_near_dtype_limit': '0', 'train/global/act/frac_near_user_limit': '0', 'train/global/grad/norm': '0.28', 'train/global/grad/mean': '-7.125e-07', 'train/global/grad/std': '0.0001977', 'train/global/grad/max_abs': '0.02832', 'train/global/grad/frac_near_dtype_limit': '0', 'train/global/grad/frac_near_user_limit': '0', 'train/global/param/norm': '74.98', 'train/global/param/mean': '0.0008527', 'train/global/param/std': '0.05306', 'train/global/param/max_abs': '1', 'train/global/param/frac_near_dtype_limit': '0', 'train/global/param/frac_near_user_limit': '0', 'train/layer_model_layers_0/act/norm': '3937', 'train/layer_model_layers_0/act/mean': '-0.009769', 'train/layer_model_layers_0/act/std': '0.5136', 'train/layer_model_layers_0/act/max_abs': '5.75', 'train/layer_model_layers_0/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/act/frac_near_user_limit': '0', 'train/layer_model_layers_0/grad/norm': '0.05513', 'train/layer_model_layers_0/grad/mean': '-1.276e-07', 'train/layer_model_layers_0/grad/std': '0.000136', 'train/layer_model_layers_0/grad/max_abs': '0.002319', 'train/layer_model_layers_0/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_0/grad/frac_near_user_limit': '0', 'train/layer_model_layers_5/act/norm': '6687', 'train/layer_model_layers_5/act/mean': '0.0218', 'train/layer_model_layers_5/act/std': '0.8724', 'train/layer_model_layers_5/act/max_abs': '5.469', 'train/layer_model_layers_5/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/act/frac_near_user_limit': '0', 'train/layer_model_layers_5/grad/norm': '0.06374', 'train/layer_model_layers_5/grad/mean': '7.187e-08', 'train/layer_model_layers_5/grad/std': '0.0001575', 'train/layer_model_layers_5/grad/max_abs': '0.001877', 'train/layer_model_layers_5/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_5/grad/frac_near_user_limit': '0', 'train/layer__model_layers_6/param/norm': '18.64', 'train/layer__model_layers_6/param/mean': '0.00167', 'train/layer__model_layers_6/param/std': '0.04598', 'train/layer__model_layers_6/param/max_abs': '1', 'train/layer__model_layers_6/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_6/param/frac_near_user_limit': '0', 'train/layer_model_layers_6/act/norm': '7252', 'train/layer_model_layers_6/act/mean': '0.01006', 'train/layer_model_layers_6/act/std': '0.9455', 'train/layer_model_layers_6/act/max_abs': '6.031', 'train/layer_model_layers_6/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/act/frac_near_user_limit': '0', 'train/layer_model_layers_6/grad/norm': '0.05066', 'train/layer_model_layers_6/grad/mean': '7.997e-08', 'train/layer_model_layers_6/grad/std': '0.0001251', 'train/layer_model_layers_6/grad/max_abs': '0.001564', 'train/layer_model_layers_6/grad/frac_near_dtype_limit': '0', 'train/layer_model_layers_6/grad/frac_near_user_limit': '0', 'train/layer__model_layers_3/param/norm': '18.35', 'train/layer__model_layers_3/param/mean': '0.001473', 'train/layer__model_layers_3/param/std': '0.04528', 'train/layer__model_layers_3/param/max_abs': '1', 'train/layer__model_layers_3/param/frac_near_dtype_limit': '0', 'train/layer__model_layers_3/param/frac_near_user_limit': '0', 'train/layer_model_layers_2/act/norm': '5551', 'train/layer_model_layers_2/act/mean': '-0.009399', 'train/layer_model_layers_2/act/std': '0.725', 'train/layer_model_layers_2/act/max_abs': '5', 'train/layer_model_layers_2/act/frac_near_dtype_limit': '0', 'train/layer_model_layers_2/act/frac_near_user_limit': '0', 'train/layer_model_layers_2/grad/norm': '0.06316', 'train/layer_model_layers_2/grad/mean': '3.644e-07', 'train/layer_model_layers_2/grad/std': '0.0001559', 'train/layer_model_layers_2/grad/
+{'loss': '0.2655', 'grad_norm': '0.2578', 'learning_rate': '0.0003', 'epoch': '0.0701', 'train/total_time_seconds': '31.66', 'train/time_per_step_avg': '0.03023', 'train/epoch_time_elapsed': '124.1', 'train/estimated_remaining_minutes': '0.4871'}
+{'loss': '0.2647', 'grad_norm': '0.3262', 'learning_rate': '0.0003', 'epoch': '0.07145', 'train/total_time_seconds': '32.26', 'train/time_per_step_avg': '0.03028', 'train/epoch_time_elapsed': '125.6', 'train/estimated_remaining_minutes': '0.4767'}
+{'loss': '0.26', 'grad_norm': '0.3379', 'learning_rate': '0.0003', 'epoch': '0.0728', 'train/total_time_seconds': '32.85', 'train/time_per_step_avg': '0.03029', 'train/epoch_time_elapsed': '127.2', 'train/estimated_remaining_minutes': '0.4663'}
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diff --git a/wandb/run-20260806_204221-wp360ttn/files/requirements.txt b/wandb/run-20260806_204221-wp360ttn/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..123b15ebf857859624f7f4332e92341c8ef13fdf
--- /dev/null
+++ b/wandb/run-20260806_204221-wp360ttn/files/requirements.txt
@@ -0,0 +1,149 @@
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diff --git a/wandb/run-20260806_204221-wp360ttn/files/wandb-metadata.json b/wandb/run-20260806_204221-wp360ttn/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..278d6024e268c71ef7183c50f929c83bdee34dd4
--- /dev/null
+++ b/wandb/run-20260806_204221-wp360ttn/files/wandb-metadata.json
@@ -0,0 +1,96 @@
+{
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+  "git":  {
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+    "commit":  "8d6f97d73f5521e4c38c6e1bda59bf8d4b489655"
+  },
+  "email":  "deepnevro@gmail.com",
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+  "gpu_nvidia":  [
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+      "architecture":  "Hopper",
+      "uuid":  "GPU-bfa16575-1d94-1aa2-4537-2c93433f42ef"
+    },
+    {
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+      "architecture":  "Hopper",
+      "uuid":  "GPU-bc6c3e3c-9b90-09ca-c034-774961847c54"
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+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-00a441e1-7c95-e7d6-4c35-43d6b291aea9"
+    },
+    {
+      "name":  "NVIDIA H100 80GB HBM3",
+      "memoryTotal":  "85520809984",
+      "cudaCores":  16896,
+      "architecture":  "Hopper",
+      "uuid":  "GPU-1c4d29a2-4647-6fce-d8fc-0c5ecfbbd6ea"
+    }
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+  "writerId":  "7bdt028ibym63b3zuklf9g2f3c2q19ys"
+}
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..2500b909c7091749eac135167f69c99ea001b107
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..5776a2f560e50179f29d335117c50629ab3ebcaf
--- /dev/null
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