Upload AdamW FineWeb-Edu checkpoint at 24,999,591,936 tokens

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- HuggingFaceFW/fineweb-edu
+tags:
+- causal-lm
+- base-model
+- bananamind2-nano
+- adamw
+- fineweb-edu
+- optimizer-comparison
+- custom-code
+- trust-remote-code
+---
+
+# BananaMind 2 Nano AdamW FineWeb-Edu Test
+
+This experimental base model uses the exact **BananaMind 2 Nano** architecture
+and tokenizer. It was trained from scratch with AdamW on only streamed
+FineWeb-Edu data for **24,999,591,936 custom-tokenizer tokens**.
+
+## Architecture
+
+| Field | Value |
+|---|---:|
+| Parameters | 9,968,128 |
+| Layers | 10 |
+| Hidden size | 256 |
+| Intermediate size | 768 |
+| Query heads | 4 |
+| KV heads | 2 |
+| Head dimension | 64 |
+| Context | 4,096 |
+| Vocabulary | 8,192 |
+| Embeddings | Tied |
+| Attention | GQA, pre-RoPE QK norm |
+| MLP | SwiGLU |
+| Position encoding | RoPE, theta 100,000 |
+
+## Training
+
+| Field | Value |
+|---|---:|
+| Dataset | `HuggingFaceFW/fineweb-edu` / `sample-100BT` |
+| Dataset revision | `87f09149ef4734204d70ed1d046ddc9ca3f2b8f9` |
+| Data access | Streaming |
+| Optimizer | AdamW |
+| Peak learning rate | 0.003 |
+| Betas | (0.9, 0.95) |
+| Global batch | 132 sequences |
+| Tokens per optimizer step | 540,672 |
+| Optimizer steps | 46,238 |
+| Warmup | 1,750 steps |
+| Schedule | Warmup-stable-decay, final 15% cosine cooldown |
+| Weight decay | 0.1, then 0.01 after 12,000,000,000 tokens |
+| Precision | bfloat16 autocast, float32 master weights |
+| Hardware | 8 x NVIDIA RTX PRO 6000 Blackwell Server Edition |
+| Seed | 1337 |
+
+The original Nano effective batch was 12 micro-batches x 11 accumulation
+steps = 132 sequences. This distributed run preserves that exact global batch.
+Ranks receive 16 or 17 sequences and scale their local mean losses so DDP's
+averaged gradient is the true 132-sequence global mean.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Banaxi-Tech/adamw-model-test"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+```
+
+This is a base model, not an instruction-tuned chat model.

config.json

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+{
+  "vocab_size": 8192,
+  "hidden_size": 256,
+  "num_hidden_layers": 10,
+  "num_attention_heads": 4,
+  "num_key_value_heads": 2,
+  "head_dim": 64,
+  "intermediate_size": 768,
+  "max_position_embeddings": 4096,
+  "rope_theta": 100000.0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": true,
+  "model_type": "bananamind2_nano",
+  "architectures": [
+    "BananaMind2NanoForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_bananamind2nano.BananaMind2NanoConfig",
+    "AutoModelForCausalLM": "modeling_bananamind2nano.BananaMind2NanoForCausalLM"
+  },
+  "torch_dtype": "float32",
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "unk_token_id": 3,
+  "use_cache": true,
+  "z_loss_coeff": 0.0,
+  "_name_or_path": "Banaxi-Tech/adamw-model-test"
+}

configuration_bananamind2nano.py

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+"""BananaMind 2 Nano configuration."""
+from transformers import PretrainedConfig
+
+
+class BananaMind2NanoConfig(PretrainedConfig):
+    model_type = "bananamind2_nano"
+
+    def __init__(
+        self,
+        vocab_size=8192,
+        hidden_size=256,
+        num_hidden_layers=10,
+        num_attention_heads=4,
+        num_key_value_heads=2,
+        head_dim=64,
+        intermediate_size=768,
+        max_position_embeddings=4096,
+        rope_theta=100000.0,
+        rms_norm_eps=1e-6,
+        tie_word_embeddings=True,
+        use_cache=True,
+        z_loss_coeff=0.0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pad_token_id=0,
+        unk_token_id=3,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.z_loss_coeff = z_loss_coeff
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            unk_token_id=unk_token_id,
+            **kwargs,
+        )

generation_config.json

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+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "use_cache": true,
+  "transformers_version": "5.7.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1bafb54fd1d65595edcc46d2d508d85dc36e15cf87a80d6717e4db749f81e33e
+size 48272696

modeling_bananamind2nano.py

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+"""BananaMind 2 Nano implementation for Hugging Face Transformers."""
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation.utils import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .configuration_bananamind2nano import BananaMind2NanoConfig
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        x_float = x.float()
+        rms = torch.rsqrt(x_float.pow(2).mean(-1, keepdim=True) + self.eps)
+        return (x_float * rms * self.weight.float()).type_as(x)
+
+
+def build_rope_inv_freq(head_dim, theta=100000.0):
+    return 1.0 / (theta ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim))
+
+
+def precompute_freqs_cis(head_dim, seq_len, theta=100000.0):
+    freqs = build_rope_inv_freq(head_dim, theta)
+    positions = torch.arange(seq_len, dtype=torch.float32)
+    freqs = torch.outer(positions, freqs)
+    return torch.polar(torch.ones_like(freqs), freqs)
+
+
+def apply_rotary_emb(q, k, freqs_cis):
+    q_complex = torch.view_as_complex(q.float().reshape(*q.shape[:-1], -1, 2))
+    k_complex = torch.view_as_complex(k.float().reshape(*k.shape[:-1], -1, 2))
+    freqs_cis = freqs_cis.unsqueeze(0).unsqueeze(0)
+    q_out = torch.view_as_real(q_complex * freqs_cis).flatten(-2)
+    k_out = torch.view_as_real(k_complex * freqs_cis).flatten(-2)
+    return q_out.type_as(q), k_out.type_as(k)
+
+
+class BananaMind2NanoAttention(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.n_head = config.num_attention_heads
+        self.n_kv_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.n_rep = self.n_head // self.n_kv_heads
+
+        self.q_proj = nn.Linear(config.hidden_size, self.n_head * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.n_head * self.head_dim, config.hidden_size, bias=False)
+        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        x,
+        freqs_cis,
+        attention_mask=None,
+        past_key_values=None,
+        use_cache=False,
+    ):
+        batch_size, seq_len, _ = x.shape
+        q = self.q_proj(x).view(
+            batch_size,
+            seq_len,
+            self.n_head,
+            self.head_dim,
+        ).transpose(1, 2)
+        k = self.k_proj(x).view(
+            batch_size,
+            seq_len,
+            self.n_kv_heads,
+            self.head_dim,
+        ).transpose(1, 2)
+        v = self.v_proj(x).view(
+            batch_size,
+            seq_len,
+            self.n_kv_heads,
+            self.head_dim,
+        ).transpose(1, 2)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        q, k = apply_rotary_emb(q, k, freqs_cis)
+
+        past_length = 0
+        if use_cache and past_key_values is not None:
+            past_length = past_key_values.get_seq_length(self.layer_idx)
+            k, v = past_key_values.update(k, v, self.layer_idx)
+
+        kv_len = k.size(-2)
+        k = k.unsqueeze(2).expand(
+            batch_size,
+            self.n_kv_heads,
+            self.n_rep,
+            kv_len,
+            self.head_dim,
+        ).reshape(batch_size, self.n_head, kv_len, self.head_dim)
+        v = v.unsqueeze(2).expand(
+            batch_size,
+            self.n_kv_heads,
+            self.n_rep,
+            kv_len,
+            self.head_dim,
+        ).reshape(batch_size, self.n_head, kv_len, self.head_dim)
+
+        attn_mask = None
+        is_causal = past_length == 0 and attention_mask is None
+        if not is_causal:
+            query_positions = past_length + torch.arange(seq_len, device=x.device)
+            key_positions = torch.arange(kv_len, device=x.device)
+            causal = key_positions.unsqueeze(0) <= query_positions.unsqueeze(1)
+            attn_mask = causal[None, None, :, :]
+            if attention_mask is not None:
+                key_padding = attention_mask.to(torch.bool)
+                if key_padding.size(-1) < kv_len:
+                    cached_padding = torch.ones(
+                        key_padding.size(0),
+                        kv_len - key_padding.size(-1),
+                        dtype=torch.bool,
+                        device=key_padding.device,
+                    )
+                    key_padding = torch.cat((cached_padding, key_padding), dim=-1)
+                else:
+                    key_padding = key_padding[:, -kv_len:]
+                attn_mask = attn_mask & key_padding[:, None, None, :]
+            is_causal = False
+
+        y = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            attn_mask=attn_mask,
+            is_causal=is_causal,
+        )
+        y = y.transpose(1, 2).contiguous().view(
+            batch_size,
+            seq_len,
+            self.n_head * self.head_dim,
+        )
+        return self.o_proj(y)
+
+
+class BananaMind2NanoSwiGLUMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w_gate = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.w_up = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.w_down = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+    def forward(self, x):
+        return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
+
+
+class BananaMind2NanoBlock(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attn = BananaMind2NanoAttention(config, layer_idx)
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = BananaMind2NanoSwiGLUMLP(config)
+
+    def forward(
+        self,
+        x,
+        freqs_cis,
+        attention_mask=None,
+        past_key_values=None,
+        use_cache=False,
+    ):
+        x = x + self.attn(
+            self.ln_1(x),
+            freqs_cis,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+        )
+        return x + self.mlp(self.ln_2(x))
+
+
+class BananaMind2NanoPreTrainedModel(PreTrainedModel):
+    config_class = BananaMind2NanoConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = False
+
+    def _init_weights(self, module):
+        std = 0.02
+        if hasattr(module, "NANOGPT_SCALE_INIT"):
+            std *= 2 * self.config.num_hidden_layers ** -0.5
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+
+class BananaMind2NanoForCausalLM(BananaMind2NanoPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "transformer.wte.weight"}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.transformer = nn.ModuleDict(
+            {
+                "wte": nn.Embedding(config.vocab_size, config.hidden_size),
+                "h": nn.ModuleList(
+                    [
+                        BananaMind2NanoBlock(config, layer_idx)
+                        for layer_idx in range(config.num_hidden_layers)
+                    ]
+                ),
+                "ln_f": RMSNorm(config.hidden_size, eps=config.rms_norm_eps),
+            }
+        )
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.transformer["wte"].weight
+        self._embd_scale = math.sqrt(config.hidden_size)
+        self._freqs_cis_cache = None
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer["wte"]
+
+    def set_input_embeddings(self, value):
+        self.transformer["wte"] = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def _get_freqs_cis(self, seq_len, device):
+        cache = self._freqs_cis_cache
+        if cache is None or cache.device != device or cache.size(0) < seq_len:
+            cache = precompute_freqs_cis(
+                self.config.head_dim,
+                seq_len,
+                self.config.rope_theta,
+            ).to(device)
+            self._freqs_cis_cache = cache
+        return cache[:seq_len]
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask=None,
+        labels=None,
+        past_key_values: Optional[Cache] = None,
+        use_cache=None,
+        **kwargs,
+    ):
+        _, seq_len = input_ids.shape
+        if use_cache is None:
+            use_cache = self.config.use_cache and labels is None
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        past_length = past_key_values.get_seq_length() if use_cache else 0
+        total_length = past_length + seq_len
+        if total_length > self.config.max_position_embeddings:
+            raise ValueError(
+                f"Sequence length {total_length} exceeds the configured maximum "
+                f"of {self.config.max_position_embeddings}"
+            )
+
+        x = self.transformer["wte"](input_ids) * self._embd_scale
+        freqs_cis = self._get_freqs_cis(total_length, input_ids.device)[past_length:]
+
+        for block in self.transformer["h"]:
+            x = block(
+                x,
+                freqs_cis,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+            )
+
+        x = self.transformer["ln_f"](x)
+        logits = self.lm_head(x)
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss = F.cross_entropy(
+                shift_logits.float().reshape(-1, shift_logits.size(-1)),
+                shift_labels.reshape(-1),
+            )
+            if self.config.z_loss_coeff:
+                loss = loss + self.config.z_loss_coeff * logits.float().pow(2).mean()
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=past_key_values if use_cache else None,
+        )

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "pad_token": "<|pad|>",
+  "bos_token": "<|bos|>",
+  "eos_token": "<|eos|>",
+  "unk_token": "<|unk|>"
+}

tokenizer_config.json

@@ -0,0 +1,8 @@
+{
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "model_max_length": 4096,
+  "pad_token": "<|pad|>",
+  "bos_token": "<|bos|>",
+  "eos_token": "<|eos|>",
+  "unk_token": "<|unk|>"
+}

training_metadata.json

@@ -0,0 +1,34 @@
+{
+  "parameters": 9968128,
+  "architecture": {
+    "vocab_size": 8192,
+    "hidden_size": 256,
+    "num_hidden_layers": 10,
+    "num_attention_heads": 4,
+    "num_key_value_heads": 2,
+    "head_dim": 64,
+    "intermediate_size": 768,
+    "max_position_embeddings": 4096,
+    "rope_theta": 100000.0,
+    "rms_norm_eps": 1e-06,
+    "tie_word_embeddings": true
+  },
+  "dataset_id": "HuggingFaceFW/fineweb-edu",
+  "dataset_config": "sample-100BT",
+  "dataset_revision": "87f09149ef4734204d70ed1d046ddc9ca3f2b8f9",
+  "tokenizer_repo": "BananaMind/BananaMind-2-Nano",
+  "tokenizer_revision": "c8564d1bd3f6177221ed7e4f63ae5f281a677a1c",
+  "optimizer": "AdamW",
+  "peak_lr": 0.003,
+  "global_batch": 132,
+  "tokens_per_step": 540672,
+  "steps": 46238,
+  "tokens_seen": 24999591936,
+  "target_tokens": 25000000000,
+  "world_size": 8,
+  "gpu_name": "NVIDIA RTX PRO 6000 Blackwell Server Edition",
+  "elapsed_seconds": 3753.9637784957886,
+  "average_tokens_per_second": 6659518.687742193,
+  "estimated_training_flops": 4640871447860871168,
+  "seed": 1337
+}