Adicionando novos arquivos de modelo, incluindo subpastas

checkpoint-381/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "stabilityai/stablelm-3b-4e1t",
+  "architectures": [
+    "StableLMEpochForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig",
+    "AutoModelForCausalLM": "modeling_stablelm_epoch.StableLMEpochForCausalLM"
+  },
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "max_position_embeddings": 4096,
+  "model_type": "stablelm_epoch",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "rope_pct": 0.25,
+  "rope_theta": 10000,
+  "rotary_scaling_factor": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.1",
+  "use_cache": false,
+  "vocab_size": 50304
+}

checkpoint-381/configuration_stablelm_epoch.py

@@ -0,0 +1,110 @@
+# coding=utf-8
+# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" StableLM Epoch model configuration"""
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class StableLMEpochConfig(PretrainedConfig):
+    r"""
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50_304):
+            Vocabulary size of the StableLM model. Defines the number of different tokens that
+            can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
+        intermediate_size (`int`, *optional*, defaults to 6912):
+            Dimension of the MLP representations.
+        hidden_size (`int`, *optional*, defaults to 2560):
+            Dimension of the decoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string).
+        rope_pct (`float`, *optional*, defaults to 1.0):
+            Percentage of hidden dimensions to allocate to rotary embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 1e-5):
+            The standard deviation of the truncated_normal_initializer for initializing
+             all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-8):
+            The epsilon used by the normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions
+            (not used by all models). Only relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+    """
+    model_type = "stablelm_epoch"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50_304,
+        intermediate_size=6912,
+        hidden_size=2560,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        rope_pct=0.25,
+        rope_theta=10_000,
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        norm_eps=1.0e-5,
+        use_cache=True,
+        bos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.intermediate_size = intermediate_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.hidden_act = hidden_act
+        self.rope_pct = rope_pct
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

checkpoint-381/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.34.1"
+}

checkpoint-381/modeling_stablelm_epoch.py

@@ -0,0 +1,687 @@
+# coding=utf-8
+# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This code is based off the following work:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+""" PyTorch StableLM Epoch model. """
+from typing import Optional, Tuple, Union
+import math
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from .configuration_stablelm_epoch import StableLMEpochConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+):
+    """Make causal mask used for bi-directional self-attention."""
+    batch_size, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
+    batch_size, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_position_embeddings: int,
+        base: int = 10_000,
+        device: Optional[torch.device] = None,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
+        )
+
+    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+        # Don't do einsum, it converts fp32 to fp16 under AMP
+        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
+        # x: [batch_size, num_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x: torch.Tensor):
+    """Rotates half the hidden dims of the input."""
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MLP(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.act_fn = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Attention(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+
+        self._init_rope()
+
+    def _init_rope(self):
+        self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
+        self.rotary_emb = RotaryEmbedding(
+            self.rotary_ndims,
+            max_position_embeddings=self.config.max_position_embeddings,
+            base=self.config.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        query_rot = query_states[..., : self.rotary_ndims]
+        query_pass = query_states[..., self.rotary_ndims :]
+        key_rot = key_states[..., : self.rotary_ndims]
+        key_pass = key_states[..., self.rotary_ndims :]
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # [batch_size, num_heads, seq_len, head_dim]
+        query_states = torch.cat((query_states, query_pass), dim=-1)
+        key_states = torch.cat((key_states, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            # Reuse k, v, self_attention
+            key_states = torch.cat((past_key_value[0], key_states), dim=2)
+            value_states = torch.cat((past_key_value[1], value_states), dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # Upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        # Merge heads
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        # Final linear projection
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.self_attn = Attention(config)
+        self.mlp = MLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.FloatTensor],
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class StableLMEpochPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and a simple interface
+    for downloading and loading pretrained models.
+    """
+
+    config_class = StableLMEpochConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
+        if isinstance(module, StableLMEpochModel):
+            module.gradient_checkpointing = value
+
+
+class StableLMEpochModel(StableLMEpochPreTrainedModel):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
+        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_shape: torch.Size,
+        inputs_embeds: torch.Tensor,
+        past_key_values_length: int,
+    ):
+        # Create causal mask
+        # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # Embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # Decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # Add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__(config)
+
+        self.model = StableLMEpochModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        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 set_output_embeddings(self, new_embeddings: nn.Module):
+        self.lm_head = new_embeddings
+
+    def get_decoder(self):
+        return self.model
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states).float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        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=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        # Trim decoder_input_ids if past is used
+        if past_key_values and past_key_values[0] is not None:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # Create position_ids on the fly for batch generation
+            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)
+
+        # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+StableLMEpochConfig.register_for_auto_class()
+StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")

checkpoint-381/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:371856f931233dd717fba719b46c33cca387985553211cf056b2a91b45ea877a
+size 12213648093

checkpoint-381/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0533e722b0fe05fceeec40e132e52c4e538eb8f172958a4c46d28c0456fc0a75
+size 6106785781

checkpoint-381/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2bad8ffde323ea4d873d4ea6f601f2ad8074201b415660915f2d39a92b3907b
+size 14575

checkpoint-381/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fcb0cd5ac25ae442cf685ac09869370725a6819fe2382a5d0923e2611beea8d1
+size 627

checkpoint-381/trainer_state.json

@@ -0,0 +1,2305 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 1.0178660878276842,
+  "eval_steps": 500,
+  "global_step": 381,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.0,
+      "learning_rate": 2e-08,
+      "loss": 1.1427,
+      "step": 1
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 4e-08,
+      "loss": 1.1555,
+      "step": 2
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 6.000000000000001e-08,
+      "loss": 1.1633,
+      "step": 3
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 8e-08,
+      "loss": 1.1405,
+      "step": 4
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 1e-07,
+      "loss": 1.1607,
+      "step": 5
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.2000000000000002e-07,
+      "loss": 1.1587,
+      "step": 6
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.4e-07,
+      "loss": 1.1463,
+      "step": 7
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.6e-07,
+      "loss": 1.1764,
+      "step": 8
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.8e-07,
+      "loss": 1.1509,
+      "step": 9
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2e-07,
+      "loss": 1.1516,
+      "step": 10
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2.2e-07,
+      "loss": 1.1589,
+      "step": 11
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2.4000000000000003e-07,
+      "loss": 1.1416,
+      "step": 12
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2.6e-07,
+      "loss": 1.1474,
+      "step": 13
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 2.8e-07,
+      "loss": 1.1785,
+      "step": 14
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 3e-07,
+      "loss": 1.1259,
+      "step": 15
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 3.2e-07,
+      "loss": 1.1578,
+      "step": 16
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 3.4e-07,
+      "loss": 1.1406,
+      "step": 17
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 3.6e-07,
+      "loss": 1.1533,
+      "step": 18
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 3.7999999999999996e-07,
+      "loss": 1.1593,
+      "step": 19
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 4e-07,
+      "loss": 1.1401,
+      "step": 20
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.2e-07,
+      "loss": 1.1595,
+      "step": 21
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.4e-07,
+      "loss": 1.1482,
+      "step": 22
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.6e-07,
+      "loss": 1.1682,
+      "step": 23
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.800000000000001e-07,
+      "loss": 1.1453,
+      "step": 24
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5e-07,
+      "loss": 1.1329,
+      "step": 25
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5.2e-07,
+      "loss": 1.1488,
+      "step": 26
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5.399999999999999e-07,
+      "loss": 1.1528,
+      "step": 27
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5.6e-07,
+      "loss": 1.1463,
+      "step": 28
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 5.8e-07,
+      "loss": 1.1445,
+      "step": 29
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 6e-07,
+      "loss": 1.1578,
+      "step": 30
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 6.2e-07,
+      "loss": 1.1523,
+      "step": 31
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 6.4e-07,
+      "loss": 1.1547,
+      "step": 32
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 6.6e-07,
+      "loss": 1.1309,
+      "step": 33
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 6.8e-07,
+      "loss": 1.1249,
+      "step": 34
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 7.000000000000001e-07,
+      "loss": 1.1774,
+      "step": 35
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.2e-07,
+      "loss": 1.124,
+      "step": 36
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.4e-07,
+      "loss": 1.1485,
+      "step": 37
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.599999999999999e-07,
+      "loss": 1.1442,
+      "step": 38
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.8e-07,
+      "loss": 1.1106,
+      "step": 39
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8e-07,
+      "loss": 1.1494,
+      "step": 40
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8.2e-07,
+      "loss": 1.1084,
+      "step": 41
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8.4e-07,
+      "loss": 1.1244,
+      "step": 42
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8.6e-07,
+      "loss": 1.1482,
+      "step": 43
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 8.8e-07,
+      "loss": 1.1164,
+      "step": 44
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 8.999999999999999e-07,
+      "loss": 1.1382,
+      "step": 45
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 9.2e-07,
+      "loss": 1.131,
+      "step": 46
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 9.4e-07,
+      "loss": 1.1241,
+      "step": 47
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 9.600000000000001e-07,
+      "loss": 1.1159,
+      "step": 48
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 9.800000000000001e-07,
+      "loss": 1.1291,
+      "step": 49
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 1e-06,
+      "loss": 1.1361,
+      "step": 50
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.02e-06,
+      "loss": 1.126,
+      "step": 51
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.04e-06,
+      "loss": 1.1184,
+      "step": 52
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.06e-06,
+      "loss": 1.1193,
+      "step": 53
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.0799999999999998e-06,
+      "loss": 1.1187,
+      "step": 54
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.1e-06,
+      "loss": 1.1145,
+      "step": 55
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.12e-06,
+      "loss": 1.1019,
+      "step": 56
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.14e-06,
+      "loss": 1.1157,
+      "step": 57
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.16e-06,
+      "loss": 1.1024,
+      "step": 58
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 1.18e-06,
+      "loss": 1.098,
+      "step": 59
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 1.2e-06,
+      "loss": 1.0946,
+      "step": 60
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 1.22e-06,
+      "loss": 1.0818,
+      "step": 61
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.24e-06,
+      "loss": 1.121,
+      "step": 62
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.26e-06,
+      "loss": 1.09,
+      "step": 63
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.28e-06,
+      "loss": 1.0958,
+      "step": 64
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.3e-06,
+      "loss": 1.0888,
+      "step": 65
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.32e-06,
+      "loss": 1.0793,
+      "step": 66
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.3399999999999999e-06,
+      "loss": 1.0929,
+      "step": 67
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.36e-06,
+      "loss": 1.0685,
+      "step": 68
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.38e-06,
+      "loss": 1.0888,
+      "step": 69
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 1.4000000000000001e-06,
+      "loss": 1.0688,
+      "step": 70
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 1.42e-06,
+      "loss": 1.0627,
+      "step": 71
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 1.44e-06,
+      "loss": 1.0627,
+      "step": 72
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.46e-06,
+      "loss": 1.0858,
+      "step": 73
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.48e-06,
+      "loss": 1.0517,
+      "step": 74
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.4999999999999998e-06,
+      "loss": 1.0626,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.5199999999999998e-06,
+      "loss": 1.0708,
+      "step": 76
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.54e-06,
+      "loss": 1.0391,
+      "step": 77
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.56e-06,
+      "loss": 1.0436,
+      "step": 78
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.58e-06,
+      "loss": 1.0396,
+      "step": 79
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.6e-06,
+      "loss": 1.0334,
+      "step": 80
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.62e-06,
+      "loss": 1.0291,
+      "step": 81
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.64e-06,
+      "loss": 1.0421,
+      "step": 82
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.66e-06,
+      "loss": 1.0493,
+      "step": 83
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.68e-06,
+      "loss": 1.0293,
+      "step": 84
+    },
+    {
+      "epoch": 0.23,
+      "learning_rate": 1.7e-06,
+      "loss": 1.0766,
+      "step": 85
+    },
+    {
+      "epoch": 0.23,
+      "learning_rate": 1.72e-06,
+      "loss": 1.058,
+      "step": 86
+    },
+    {
+      "epoch": 0.23,
+      "learning_rate": 1.74e-06,
+      "loss": 1.0244,
+      "step": 87
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.76e-06,
+      "loss": 1.0399,
+      "step": 88
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.78e-06,
+      "loss": 1.0272,
+      "step": 89
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.7999999999999997e-06,
+      "loss": 1.029,
+      "step": 90
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.82e-06,
+      "loss": 1.0477,
+      "step": 91
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.84e-06,
+      "loss": 1.0166,
+      "step": 92
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.8600000000000002e-06,
+      "loss": 1.0309,
+      "step": 93
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.88e-06,
+      "loss": 1.0135,
+      "step": 94
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.8999999999999998e-06,
+      "loss": 1.0226,
+      "step": 95
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.9200000000000003e-06,
+      "loss": 1.0402,
+      "step": 96
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.94e-06,
+      "loss": 1.0252,
+      "step": 97
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.9600000000000003e-06,
+      "loss": 1.0064,
+      "step": 98
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.98e-06,
+      "loss": 0.9967,
+      "step": 99
+    },
+    {
+      "epoch": 0.27,
+      "learning_rate": 2e-06,
+      "loss": 1.0094,
+      "step": 100
+    },
+    {
+      "epoch": 0.27,
+      "learning_rate": 2.02e-06,
+      "loss": 0.9938,
+      "step": 101
+    },
+    {
+      "epoch": 0.27,
+      "learning_rate": 2.04e-06,
+      "loss": 1.0199,
+      "step": 102
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.0599999999999998e-06,
+      "loss": 1.005,
+      "step": 103
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.08e-06,
+      "loss": 1.0016,
+      "step": 104
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.1e-06,
+      "loss": 1.0169,
+      "step": 105
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.12e-06,
+      "loss": 0.978,
+      "step": 106
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.14e-06,
+      "loss": 0.9787,
+      "step": 107
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.1599999999999996e-06,
+      "loss": 0.9992,
+      "step": 108
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.18e-06,
+      "loss": 0.985,
+      "step": 109
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.2e-06,
+      "loss": 0.9832,
+      "step": 110
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.22e-06,
+      "loss": 0.9922,
+      "step": 111
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.24e-06,
+      "loss": 0.9882,
+      "step": 112
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.26e-06,
+      "loss": 0.979,
+      "step": 113
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.28e-06,
+      "loss": 0.9866,
+      "step": 114
+    },
+    {
+      "epoch": 0.31,
+      "learning_rate": 2.3e-06,
+      "loss": 0.97,
+      "step": 115
+    },
+    {
+      "epoch": 0.31,
+      "learning_rate": 2.32e-06,
+      "loss": 1.0042,
+      "step": 116
+    },
+    {
+      "epoch": 0.31,
+      "learning_rate": 2.34e-06,
+      "loss": 0.9778,
+      "step": 117
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.36e-06,
+      "loss": 0.9906,
+      "step": 118
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.38e-06,
+      "loss": 0.9547,
+      "step": 119
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.4e-06,
+      "loss": 0.9792,
+      "step": 120
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.4199999999999997e-06,
+      "loss": 0.964,
+      "step": 121
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.44e-06,
+      "loss": 0.9797,
+      "step": 122
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.4599999999999997e-06,
+      "loss": 0.9576,
+      "step": 123
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.48e-06,
+      "loss": 0.9795,
+      "step": 124
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.5e-06,
+      "loss": 0.9619,
+      "step": 125
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.52e-06,
+      "loss": 0.9756,
+      "step": 126
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.5400000000000002e-06,
+      "loss": 0.9678,
+      "step": 127
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.56e-06,
+      "loss": 0.973,
+      "step": 128
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.58e-06,
+      "loss": 0.9581,
+      "step": 129
+    },
+    {
+      "epoch": 0.35,
+      "learning_rate": 2.6e-06,
+      "loss": 0.9559,
+      "step": 130
+    },
+    {
+      "epoch": 0.35,
+      "learning_rate": 2.62e-06,
+      "loss": 0.9623,
+      "step": 131
+    },
+    {
+      "epoch": 0.35,
+      "learning_rate": 2.64e-06,
+      "loss": 0.9608,
+      "step": 132
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.66e-06,
+      "loss": 0.9599,
+      "step": 133
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.6799999999999998e-06,
+      "loss": 0.9481,
+      "step": 134
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.7e-06,
+      "loss": 0.951,
+      "step": 135
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.72e-06,
+      "loss": 0.9553,
+      "step": 136
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.7399999999999996e-06,
+      "loss": 0.9471,
+      "step": 137
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.76e-06,
+      "loss": 0.9228,
+      "step": 138
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.78e-06,
+      "loss": 0.9411,
+      "step": 139
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.8000000000000003e-06,
+      "loss": 0.9541,
+      "step": 140
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.82e-06,
+      "loss": 0.9318,
+      "step": 141
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.84e-06,
+      "loss": 0.9113,
+      "step": 142
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.86e-06,
+      "loss": 0.9316,
+      "step": 143
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.88e-06,
+      "loss": 0.9326,
+      "step": 144
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.9e-06,
+      "loss": 0.9471,
+      "step": 145
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.92e-06,
+      "loss": 0.9234,
+      "step": 146
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.94e-06,
+      "loss": 0.9384,
+      "step": 147
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 2.96e-06,
+      "loss": 0.9317,
+      "step": 148
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 2.98e-06,
+      "loss": 0.9157,
+      "step": 149
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 2.9999999999999997e-06,
+      "loss": 0.9411,
+      "step": 150
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 3.02e-06,
+      "loss": 0.9566,
+      "step": 151
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.0399999999999997e-06,
+      "loss": 0.9443,
+      "step": 152
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.06e-06,
+      "loss": 0.9215,
+      "step": 153
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.08e-06,
+      "loss": 0.9129,
+      "step": 154
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.1e-06,
+      "loss": 0.9175,
+      "step": 155
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.12e-06,
+      "loss": 0.9309,
+      "step": 156
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.14e-06,
+      "loss": 0.9185,
+      "step": 157
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.16e-06,
+      "loss": 0.9196,
+      "step": 158
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.18e-06,
+      "loss": 0.9038,
+      "step": 159
+    },
+    {
+      "epoch": 0.43,
+      "learning_rate": 3.2e-06,
+      "loss": 0.9205,
+      "step": 160
+    },
+    {
+      "epoch": 0.43,
+      "learning_rate": 3.22e-06,
+      "loss": 0.9308,
+      "step": 161
+    },
+    {
+      "epoch": 0.43,
+      "learning_rate": 3.24e-06,
+      "loss": 0.939,
+      "step": 162
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.2599999999999997e-06,
+      "loss": 0.9211,
+      "step": 163
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.28e-06,
+      "loss": 0.9214,
+      "step": 164
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.2999999999999997e-06,
+      "loss": 0.9243,
+      "step": 165
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.32e-06,
+      "loss": 0.9098,
+      "step": 166
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.3399999999999998e-06,
+      "loss": 0.9223,
+      "step": 167
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.36e-06,
+      "loss": 0.9213,
+      "step": 168
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.3800000000000002e-06,
+      "loss": 0.9059,
+      "step": 169
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.4e-06,
+      "loss": 0.8938,
+      "step": 170
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.42e-06,
+      "loss": 0.9139,
+      "step": 171
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.44e-06,
+      "loss": 0.898,
+      "step": 172
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.46e-06,
+      "loss": 0.9103,
+      "step": 173
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.48e-06,
+      "loss": 0.9042,
+      "step": 174
+    },
+    {
+      "epoch": 0.47,
+      "learning_rate": 3.5e-06,
+      "loss": 0.8833,
+      "step": 175
+    },
+    {
+      "epoch": 0.47,
+      "learning_rate": 3.52e-06,
+      "loss": 0.8933,
+      "step": 176
+    },
+    {
+      "epoch": 0.47,
+      "learning_rate": 3.5399999999999996e-06,
+      "loss": 0.9022,
+      "step": 177
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.56e-06,
+      "loss": 0.9141,
+      "step": 178
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.58e-06,
+      "loss": 0.9089,
+      "step": 179
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.5999999999999994e-06,
+      "loss": 0.9007,
+      "step": 180
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.6199999999999996e-06,
+      "loss": 0.8985,
+      "step": 181
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.64e-06,
+      "loss": 0.8977,
+      "step": 182
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.66e-06,
+      "loss": 0.8996,
+      "step": 183
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.68e-06,
+      "loss": 0.8999,
+      "step": 184
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.7e-06,
+      "loss": 0.9141,
+      "step": 185
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.7200000000000004e-06,
+      "loss": 0.8918,
+      "step": 186
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.7399999999999998e-06,
+      "loss": 0.9008,
+      "step": 187
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.76e-06,
+      "loss": 0.9021,
+      "step": 188
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.7800000000000002e-06,
+      "loss": 0.8951,
+      "step": 189
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 3.7999999999999996e-06,
+      "loss": 0.9089,
+      "step": 190
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 3.82e-06,
+      "loss": 0.8966,
+      "step": 191
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 3.8400000000000005e-06,
+      "loss": 0.8937,
+      "step": 192
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.8599999999999995e-06,
+      "loss": 0.8969,
+      "step": 193
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.88e-06,
+      "loss": 0.8719,
+      "step": 194
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.9e-06,
+      "loss": 0.884,
+      "step": 195
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.920000000000001e-06,
+      "loss": 0.8809,
+      "step": 196
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 3.9399999999999995e-06,
+      "loss": 0.8711,
+      "step": 197
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 3.96e-06,
+      "loss": 0.877,
+      "step": 198
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 3.98e-06,
+      "loss": 0.8892,
+      "step": 199
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 4e-06,
+      "loss": 0.8781,
+      "step": 200
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.02e-06,
+      "loss": 0.8697,
+      "step": 201
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.04e-06,
+      "loss": 0.8745,
+      "step": 202
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.059999999999999e-06,
+      "loss": 0.8738,
+      "step": 203
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.08e-06,
+      "loss": 0.8804,
+      "step": 204
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 4.1000000000000006e-06,
+      "loss": 0.8843,
+      "step": 205
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 4.1199999999999995e-06,
+      "loss": 0.8736,
+      "step": 206
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 4.14e-06,
+      "loss": 0.8689,
+      "step": 207
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.16e-06,
+      "loss": 0.8614,
+      "step": 208
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.180000000000001e-06,
+      "loss": 0.866,
+      "step": 209
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.2e-06,
+      "loss": 0.8598,
+      "step": 210
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.22e-06,
+      "loss": 0.878,
+      "step": 211
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.24e-06,
+      "loss": 0.8624,
+      "step": 212
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.26e-06,
+      "loss": 0.8788,
+      "step": 213
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.28e-06,
+      "loss": 0.8713,
+      "step": 214
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.3e-06,
+      "loss": 0.8682,
+      "step": 215
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 4.319999999999999e-06,
+      "loss": 0.8827,
+      "step": 216
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 4.34e-06,
+      "loss": 0.8754,
+      "step": 217
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 4.36e-06,
+      "loss": 0.8867,
+      "step": 218
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.3799999999999996e-06,
+      "loss": 0.8707,
+      "step": 219
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.4e-06,
+      "loss": 0.8655,
+      "step": 220
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.42e-06,
+      "loss": 0.8503,
+      "step": 221
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.44e-06,
+      "loss": 0.8674,
+      "step": 222
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.46e-06,
+      "loss": 0.8507,
+      "step": 223
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.48e-06,
+      "loss": 0.8668,
+      "step": 224
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.5e-06,
+      "loss": 0.8548,
+      "step": 225
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.52e-06,
+      "loss": 0.8756,
+      "step": 226
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.54e-06,
+      "loss": 0.8424,
+      "step": 227
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.56e-06,
+      "loss": 0.8501,
+      "step": 228
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.579999999999999e-06,
+      "loss": 0.8633,
+      "step": 229
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.6e-06,
+      "loss": 0.859,
+      "step": 230
+    },
+    {
+      "epoch": 0.62,
+      "learning_rate": 4.62e-06,
+      "loss": 0.8497,
+      "step": 231
+    },
+    {
+      "epoch": 0.62,
+      "learning_rate": 4.64e-06,
+      "loss": 0.8491,
+      "step": 232
+    },
+    {
+      "epoch": 0.62,
+      "learning_rate": 4.6599999999999994e-06,
+      "loss": 0.8596,
+      "step": 233
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.68e-06,
+      "loss": 0.8636,
+      "step": 234
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.7e-06,
+      "loss": 0.8677,
+      "step": 235
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.72e-06,
+      "loss": 0.8605,
+      "step": 236
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.74e-06,
+      "loss": 0.8493,
+      "step": 237
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.76e-06,
+      "loss": 0.8367,
+      "step": 238
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.78e-06,
+      "loss": 0.8443,
+      "step": 239
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.8e-06,
+      "loss": 0.8464,
+      "step": 240
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.8200000000000004e-06,
+      "loss": 0.8588,
+      "step": 241
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.839999999999999e-06,
+      "loss": 0.8502,
+      "step": 242
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.86e-06,
+      "loss": 0.849,
+      "step": 243
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.88e-06,
+      "loss": 0.8576,
+      "step": 244
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.9e-06,
+      "loss": 0.8418,
+      "step": 245
+    },
+    {
+      "epoch": 0.66,
+      "learning_rate": 4.9199999999999995e-06,
+      "loss": 0.8717,
+      "step": 246
+    },
+    {
+      "epoch": 0.66,
+      "learning_rate": 4.94e-06,
+      "loss": 0.8529,
+      "step": 247
+    },
+    {
+      "epoch": 0.66,
+      "learning_rate": 4.96e-06,
+      "loss": 0.8328,
+      "step": 248
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 4.98e-06,
+      "loss": 0.85,
+      "step": 249
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 5e-06,
+      "loss": 0.8376,
+      "step": 250
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 5.02e-06,
+      "loss": 0.8431,
+      "step": 251
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 5.04e-06,
+      "loss": 0.8446,
+      "step": 252
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.06e-06,
+      "loss": 0.8424,
+      "step": 253
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.0800000000000005e-06,
+      "loss": 0.8377,
+      "step": 254
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.0999999999999995e-06,
+      "loss": 0.8453,
+      "step": 255
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.12e-06,
+      "loss": 0.8591,
+      "step": 256
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.14e-06,
+      "loss": 0.8536,
+      "step": 257
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.16e-06,
+      "loss": 0.8399,
+      "step": 258
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.1799999999999995e-06,
+      "loss": 0.8259,
+      "step": 259
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.2e-06,
+      "loss": 0.8399,
+      "step": 260
+    },
+    {
+      "epoch": 0.7,
+      "learning_rate": 5.219999999999999e-06,
+      "loss": 0.8443,
+      "step": 261
+    },
+    {
+      "epoch": 0.7,
+      "learning_rate": 5.24e-06,
+      "loss": 0.8331,
+      "step": 262
+    },
+    {
+      "epoch": 0.7,
+      "learning_rate": 5.2600000000000005e-06,
+      "loss": 0.8268,
+      "step": 263
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.28e-06,
+      "loss": 0.8338,
+      "step": 264
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.3e-06,
+      "loss": 0.8287,
+      "step": 265
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.32e-06,
+      "loss": 0.8273,
+      "step": 266
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.3400000000000005e-06,
+      "loss": 0.8471,
+      "step": 267
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.3599999999999995e-06,
+      "loss": 0.8364,
+      "step": 268
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.38e-06,
+      "loss": 0.8167,
+      "step": 269
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.4e-06,
+      "loss": 0.8433,
+      "step": 270
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.42e-06,
+      "loss": 0.8163,
+      "step": 271
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.44e-06,
+      "loss": 0.8186,
+      "step": 272
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.46e-06,
+      "loss": 0.8491,
+      "step": 273
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.479999999999999e-06,
+      "loss": 0.8222,
+      "step": 274
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.5e-06,
+      "loss": 0.8362,
+      "step": 275
+    },
+    {
+      "epoch": 0.74,
+      "learning_rate": 5.52e-06,
+      "loss": 0.8375,
+      "step": 276
+    },
+    {
+      "epoch": 0.74,
+      "learning_rate": 5.54e-06,
+      "loss": 0.7934,
+      "step": 277
+    },
+    {
+      "epoch": 0.74,
+      "learning_rate": 5.56e-06,
+      "loss": 0.8096,
+      "step": 278
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.58e-06,
+      "loss": 0.8185,
+      "step": 279
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.600000000000001e-06,
+      "loss": 0.8126,
+      "step": 280
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.6199999999999996e-06,
+      "loss": 0.8297,
+      "step": 281
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.64e-06,
+      "loss": 0.8196,
+      "step": 282
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.66e-06,
+      "loss": 0.8251,
+      "step": 283
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.68e-06,
+      "loss": 0.8291,
+      "step": 284
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.7e-06,
+      "loss": 0.8067,
+      "step": 285
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.72e-06,
+      "loss": 0.8171,
+      "step": 286
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.739999999999999e-06,
+      "loss": 0.8091,
+      "step": 287
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.76e-06,
+      "loss": 0.8149,
+      "step": 288
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.78e-06,
+      "loss": 0.8393,
+      "step": 289
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.8e-06,
+      "loss": 0.8157,
+      "step": 290
+    },
+    {
+      "epoch": 0.78,
+      "learning_rate": 5.819999999999999e-06,
+      "loss": 0.8043,
+      "step": 291
+    },
+    {
+      "epoch": 0.78,
+      "learning_rate": 5.84e-06,
+      "loss": 0.8166,
+      "step": 292
+    },
+    {
+      "epoch": 0.78,
+      "learning_rate": 5.860000000000001e-06,
+      "loss": 0.8022,
+      "step": 293
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.88e-06,
+      "loss": 0.7949,
+      "step": 294
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.9e-06,
+      "loss": 0.8102,
+      "step": 295
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.92e-06,
+      "loss": 0.81,
+      "step": 296
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.94e-06,
+      "loss": 0.8104,
+      "step": 297
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 5.96e-06,
+      "loss": 0.8089,
+      "step": 298
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 5.98e-06,
+      "loss": 0.824,
+      "step": 299
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 5.999999999999999e-06,
+      "loss": 0.8145,
+      "step": 300
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 6.02e-06,
+      "loss": 0.8085,
+      "step": 301
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.04e-06,
+      "loss": 0.8183,
+      "step": 302
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.0600000000000004e-06,
+      "loss": 0.8114,
+      "step": 303
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.079999999999999e-06,
+      "loss": 0.8112,
+      "step": 304
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.1e-06,
+      "loss": 0.8308,
+      "step": 305
+    },
+    {
+      "epoch": 0.82,
+      "learning_rate": 6.12e-06,
+      "loss": 0.8303,
+      "step": 306
+    },
+    {
+      "epoch": 0.82,
+      "learning_rate": 6.14e-06,
+      "loss": 0.8287,
+      "step": 307
+    },
+    {
+      "epoch": 0.82,
+      "learning_rate": 6.16e-06,
+      "loss": 0.7957,
+      "step": 308
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.18e-06,
+      "loss": 0.817,
+      "step": 309
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.2e-06,
+      "loss": 0.8209,
+      "step": 310
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.22e-06,
+      "loss": 0.8019,
+      "step": 311
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.24e-06,
+      "loss": 0.8099,
+      "step": 312
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.259999999999999e-06,
+      "loss": 0.8275,
+      "step": 313
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.28e-06,
+      "loss": 0.814,
+      "step": 314
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.3e-06,
+      "loss": 0.7992,
+      "step": 315
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.32e-06,
+      "loss": 0.7964,
+      "step": 316
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.3399999999999994e-06,
+      "loss": 0.8013,
+      "step": 317
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.36e-06,
+      "loss": 0.8023,
+      "step": 318
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.38e-06,
+      "loss": 0.8074,
+      "step": 319
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.4e-06,
+      "loss": 0.8154,
+      "step": 320
+    },
+    {
+      "epoch": 0.86,
+      "learning_rate": 6.42e-06,
+      "loss": 0.7984,
+      "step": 321
+    },
+    {
+      "epoch": 0.86,
+      "learning_rate": 6.44e-06,
+      "loss": 0.7987,
+      "step": 322
+    },
+    {
+      "epoch": 0.86,
+      "learning_rate": 6.46e-06,
+      "loss": 0.8007,
+      "step": 323
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.48e-06,
+      "loss": 0.7999,
+      "step": 324
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.5000000000000004e-06,
+      "loss": 0.8107,
+      "step": 325
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.519999999999999e-06,
+      "loss": 0.7928,
+      "step": 326
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.54e-06,
+      "loss": 0.8033,
+      "step": 327
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.56e-06,
+      "loss": 0.802,
+      "step": 328
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.58e-06,
+      "loss": 0.8056,
+      "step": 329
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.5999999999999995e-06,
+      "loss": 0.7857,
+      "step": 330
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.62e-06,
+      "loss": 0.8046,
+      "step": 331
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.64e-06,
+      "loss": 0.7865,
+      "step": 332
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.66e-06,
+      "loss": 0.814,
+      "step": 333
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.6799999999999996e-06,
+      "loss": 0.7934,
+      "step": 334
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.7e-06,
+      "loss": 0.7893,
+      "step": 335
+    },
+    {
+      "epoch": 0.9,
+      "learning_rate": 6.72e-06,
+      "loss": 0.7984,
+      "step": 336
+    },
+    {
+      "epoch": 0.9,
+      "learning_rate": 6.74e-06,
+      "loss": 0.7932,
+      "step": 337
+    },
+    {
+      "epoch": 0.9,
+      "learning_rate": 6.7600000000000005e-06,
+      "loss": 0.7886,
+      "step": 338
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.7799999999999995e-06,
+      "loss": 0.7925,
+      "step": 339
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.8e-06,
+      "loss": 0.7872,
+      "step": 340
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.82e-06,
+      "loss": 0.8191,
+      "step": 341
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.84e-06,
+      "loss": 0.7994,
+      "step": 342
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.8599999999999995e-06,
+      "loss": 0.8014,
+      "step": 343
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.88e-06,
+      "loss": 0.7998,
+      "step": 344
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.9e-06,
+      "loss": 0.7809,
+      "step": 345
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.92e-06,
+      "loss": 0.7845,
+      "step": 346
+    },
+    {
+      "epoch": 0.93,
+      "learning_rate": 6.94e-06,
+      "loss": 0.7795,
+      "step": 347
+    },
+    {
+      "epoch": 0.93,
+      "learning_rate": 6.96e-06,
+      "loss": 0.7959,
+      "step": 348
+    },
+    {
+      "epoch": 0.93,
+      "learning_rate": 6.979999999999999e-06,
+      "loss": 0.7848,
+      "step": 349
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 7e-06,
+      "loss": 0.7915,
+      "step": 350
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 6.999986848712049e-06,
+      "loss": 0.7891,
+      "step": 351
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 6.999947394947031e-06,
+      "loss": 0.7838,
+      "step": 352
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 6.999881639001441e-06,
+      "loss": 0.8101,
+      "step": 353
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999789581369437e-06,
+      "loss": 0.8062,
+      "step": 354
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999671222742834e-06,
+      "loss": 0.7923,
+      "step": 355
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999526564011098e-06,
+      "loss": 0.8033,
+      "step": 356
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999355606261343e-06,
+      "loss": 0.7905,
+      "step": 357
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.999158350778321e-06,
+      "loss": 0.7833,
+      "step": 358
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.998934799044413e-06,
+      "loss": 0.7916,
+      "step": 359
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.9986849527396114e-06,
+      "loss": 0.7848,
+      "step": 360
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.998408813741518e-06,
+      "loss": 0.7888,
+      "step": 361
+    },
+    {
+      "epoch": 0.97,
+      "learning_rate": 6.9981063841253256e-06,
+      "loss": 0.7971,
+      "step": 362
+    },
+    {
+      "epoch": 0.97,
+      "learning_rate": 6.997777666163796e-06,
+      "loss": 0.7947,
+      "step": 363
+    },
+    {
+      "epoch": 0.97,
+      "learning_rate": 6.997422662327253e-06,
+      "loss": 0.7943,
+      "step": 364
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.9970413752835595e-06,
+      "loss": 0.7864,
+      "step": 365
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.996633807898094e-06,
+      "loss": 0.8132,
+      "step": 366
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.996199963233736e-06,
+      "loss": 0.7814,
+      "step": 367
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.995739844550836e-06,
+      "loss": 0.8083,
+      "step": 368
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.995253455307197e-06,
+      "loss": 0.7831,
+      "step": 369
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.994740799158044e-06,
+      "loss": 0.7775,
+      "step": 370
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.994201879955999e-06,
+      "loss": 0.7969,
+      "step": 371
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.993636701751052e-06,
+      "loss": 0.7726,
+      "step": 372
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.993045268790529e-06,
+      "loss": 0.783,
+      "step": 373
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.9924275855190615e-06,
+      "loss": 0.7802,
+      "step": 374
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.991783656578554e-06,
+      "loss": 0.7885,
+      "step": 375
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.991113486808145e-06,
+      "loss": 0.7916,
+      "step": 376
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.990417081244175e-06,
+      "loss": 0.7961,
+      "step": 377
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.989694445120147e-06,
+      "loss": 0.7912,
+      "step": 378
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.9889455838666875e-06,
+      "loss": 0.7711,
+      "step": 379
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.988170503111504e-06,
+      "loss": 0.7695,
+      "step": 380
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.987369208679347e-06,
+      "loss": 0.7693,
+      "step": 381
+    }
+  ],
+  "logging_steps": 1,
+  "max_steps": 1496,
+  "num_train_epochs": 4,
+  "save_steps": 500,
+  "total_flos": 6.35040259173974e+18,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-381/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46e4ff946a52f7ad865e9875e81fb905b4052faefbb3e2ea02b382193a53532b
+size 4539

checkpoint-763/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "stabilityai/stablelm-3b-4e1t",
+  "architectures": [
+    "StableLMEpochForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig",
+    "AutoModelForCausalLM": "modeling_stablelm_epoch.StableLMEpochForCausalLM"
+  },
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "max_position_embeddings": 4096,
+  "model_type": "stablelm_epoch",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "rope_pct": 0.25,
+  "rope_theta": 10000,
+  "rotary_scaling_factor": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.1",
+  "use_cache": false,
+  "vocab_size": 50304
+}

checkpoint-763/configuration_stablelm_epoch.py

@@ -0,0 +1,110 @@
+# coding=utf-8
+# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" StableLM Epoch model configuration"""
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class StableLMEpochConfig(PretrainedConfig):
+    r"""
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50_304):
+            Vocabulary size of the StableLM model. Defines the number of different tokens that
+            can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
+        intermediate_size (`int`, *optional*, defaults to 6912):
+            Dimension of the MLP representations.
+        hidden_size (`int`, *optional*, defaults to 2560):
+            Dimension of the decoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string).
+        rope_pct (`float`, *optional*, defaults to 1.0):
+            Percentage of hidden dimensions to allocate to rotary embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 1e-5):
+            The standard deviation of the truncated_normal_initializer for initializing
+             all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-8):
+            The epsilon used by the normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions
+            (not used by all models). Only relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+    """
+    model_type = "stablelm_epoch"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50_304,
+        intermediate_size=6912,
+        hidden_size=2560,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        rope_pct=0.25,
+        rope_theta=10_000,
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        norm_eps=1.0e-5,
+        use_cache=True,
+        bos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.intermediate_size = intermediate_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.hidden_act = hidden_act
+        self.rope_pct = rope_pct
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

checkpoint-763/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.34.1"
+}

checkpoint-763/modeling_stablelm_epoch.py

@@ -0,0 +1,687 @@
+# coding=utf-8
+# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This code is based off the following work:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+""" PyTorch StableLM Epoch model. """
+from typing import Optional, Tuple, Union
+import math
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from .configuration_stablelm_epoch import StableLMEpochConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+):
+    """Make causal mask used for bi-directional self-attention."""
+    batch_size, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
+    batch_size, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_position_embeddings: int,
+        base: int = 10_000,
+        device: Optional[torch.device] = None,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
+        )
+
+    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+        # Don't do einsum, it converts fp32 to fp16 under AMP
+        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
+        # x: [batch_size, num_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x: torch.Tensor):
+    """Rotates half the hidden dims of the input."""
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MLP(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.act_fn = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Attention(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+
+        self._init_rope()
+
+    def _init_rope(self):
+        self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
+        self.rotary_emb = RotaryEmbedding(
+            self.rotary_ndims,
+            max_position_embeddings=self.config.max_position_embeddings,
+            base=self.config.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        query_rot = query_states[..., : self.rotary_ndims]
+        query_pass = query_states[..., self.rotary_ndims :]
+        key_rot = key_states[..., : self.rotary_ndims]
+        key_pass = key_states[..., self.rotary_ndims :]
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # [batch_size, num_heads, seq_len, head_dim]
+        query_states = torch.cat((query_states, query_pass), dim=-1)
+        key_states = torch.cat((key_states, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            # Reuse k, v, self_attention
+            key_states = torch.cat((past_key_value[0], key_states), dim=2)
+            value_states = torch.cat((past_key_value[1], value_states), dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # Upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        # Merge heads
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        # Final linear projection
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.self_attn = Attention(config)
+        self.mlp = MLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.FloatTensor],
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class StableLMEpochPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and a simple interface
+    for downloading and loading pretrained models.
+    """
+
+    config_class = StableLMEpochConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
+        if isinstance(module, StableLMEpochModel):
+            module.gradient_checkpointing = value
+
+
+class StableLMEpochModel(StableLMEpochPreTrainedModel):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
+        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_shape: torch.Size,
+        inputs_embeds: torch.Tensor,
+        past_key_values_length: int,
+    ):
+        # Create causal mask
+        # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # Embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # Decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # Add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__(config)
+
+        self.model = StableLMEpochModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        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 set_output_embeddings(self, new_embeddings: nn.Module):
+        self.lm_head = new_embeddings
+
+    def get_decoder(self):
+        return self.model
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states).float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        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=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        # Trim decoder_input_ids if past is used
+        if past_key_values and past_key_values[0] is not None:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # Create position_ids on the fly for batch generation
+            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)
+
+        # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+StableLMEpochConfig.register_for_auto_class()
+StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")

checkpoint-763/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ec9eeb238895d7117b73340b1d702cf05fb3c7cf1b17f0a191c6162bc12e226
+size 12213648093

checkpoint-763/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:828c2f2c0047a5da1df719f8a4b3a107cd5576ab0e26b3428c6ad2dc85173766
+size 6106785781

checkpoint-763/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6869750f95a25c4e970298a33adf90e2d7ab52680bf3317239bff1b10103235
+size 14575

checkpoint-763/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e131988c613ca08947220b6fc96c0cca14f03e41478d45f3eaa22fd86b322ee3
+size 627

checkpoint-763/trainer_state.json

@@ -0,0 +1,4597 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 2.0182835197862747,
+  "eval_steps": 500,
+  "global_step": 763,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.0,
+      "learning_rate": 2e-08,
+      "loss": 1.1427,
+      "step": 1
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 4e-08,
+      "loss": 1.1555,
+      "step": 2
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 6.000000000000001e-08,
+      "loss": 1.1633,
+      "step": 3
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 8e-08,
+      "loss": 1.1405,
+      "step": 4
+    },
+    {
+      "epoch": 0.01,
+      "learning_rate": 1e-07,
+      "loss": 1.1607,
+      "step": 5
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.2000000000000002e-07,
+      "loss": 1.1587,
+      "step": 6
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.4e-07,
+      "loss": 1.1463,
+      "step": 7
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.6e-07,
+      "loss": 1.1764,
+      "step": 8
+    },
+    {
+      "epoch": 0.02,
+      "learning_rate": 1.8e-07,
+      "loss": 1.1509,
+      "step": 9
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2e-07,
+      "loss": 1.1516,
+      "step": 10
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2.2e-07,
+      "loss": 1.1589,
+      "step": 11
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2.4000000000000003e-07,
+      "loss": 1.1416,
+      "step": 12
+    },
+    {
+      "epoch": 0.03,
+      "learning_rate": 2.6e-07,
+      "loss": 1.1474,
+      "step": 13
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 2.8e-07,
+      "loss": 1.1785,
+      "step": 14
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 3e-07,
+      "loss": 1.1259,
+      "step": 15
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 3.2e-07,
+      "loss": 1.1578,
+      "step": 16
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 3.4e-07,
+      "loss": 1.1406,
+      "step": 17
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 3.6e-07,
+      "loss": 1.1533,
+      "step": 18
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 3.7999999999999996e-07,
+      "loss": 1.1593,
+      "step": 19
+    },
+    {
+      "epoch": 0.05,
+      "learning_rate": 4e-07,
+      "loss": 1.1401,
+      "step": 20
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.2e-07,
+      "loss": 1.1595,
+      "step": 21
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.4e-07,
+      "loss": 1.1482,
+      "step": 22
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.6e-07,
+      "loss": 1.1682,
+      "step": 23
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 4.800000000000001e-07,
+      "loss": 1.1453,
+      "step": 24
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5e-07,
+      "loss": 1.1329,
+      "step": 25
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5.2e-07,
+      "loss": 1.1488,
+      "step": 26
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5.399999999999999e-07,
+      "loss": 1.1528,
+      "step": 27
+    },
+    {
+      "epoch": 0.07,
+      "learning_rate": 5.6e-07,
+      "loss": 1.1463,
+      "step": 28
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 5.8e-07,
+      "loss": 1.1445,
+      "step": 29
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 6e-07,
+      "loss": 1.1578,
+      "step": 30
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 6.2e-07,
+      "loss": 1.1523,
+      "step": 31
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 6.4e-07,
+      "loss": 1.1547,
+      "step": 32
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 6.6e-07,
+      "loss": 1.1309,
+      "step": 33
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 6.8e-07,
+      "loss": 1.1249,
+      "step": 34
+    },
+    {
+      "epoch": 0.09,
+      "learning_rate": 7.000000000000001e-07,
+      "loss": 1.1774,
+      "step": 35
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.2e-07,
+      "loss": 1.124,
+      "step": 36
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.4e-07,
+      "loss": 1.1485,
+      "step": 37
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.599999999999999e-07,
+      "loss": 1.1442,
+      "step": 38
+    },
+    {
+      "epoch": 0.1,
+      "learning_rate": 7.8e-07,
+      "loss": 1.1106,
+      "step": 39
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8e-07,
+      "loss": 1.1494,
+      "step": 40
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8.2e-07,
+      "loss": 1.1084,
+      "step": 41
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8.4e-07,
+      "loss": 1.1244,
+      "step": 42
+    },
+    {
+      "epoch": 0.11,
+      "learning_rate": 8.6e-07,
+      "loss": 1.1482,
+      "step": 43
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 8.8e-07,
+      "loss": 1.1164,
+      "step": 44
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 8.999999999999999e-07,
+      "loss": 1.1382,
+      "step": 45
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 9.2e-07,
+      "loss": 1.131,
+      "step": 46
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 9.4e-07,
+      "loss": 1.1241,
+      "step": 47
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 9.600000000000001e-07,
+      "loss": 1.1159,
+      "step": 48
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 9.800000000000001e-07,
+      "loss": 1.1291,
+      "step": 49
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 1e-06,
+      "loss": 1.1361,
+      "step": 50
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.02e-06,
+      "loss": 1.126,
+      "step": 51
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.04e-06,
+      "loss": 1.1184,
+      "step": 52
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.06e-06,
+      "loss": 1.1193,
+      "step": 53
+    },
+    {
+      "epoch": 0.14,
+      "learning_rate": 1.0799999999999998e-06,
+      "loss": 1.1187,
+      "step": 54
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.1e-06,
+      "loss": 1.1145,
+      "step": 55
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.12e-06,
+      "loss": 1.1019,
+      "step": 56
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.14e-06,
+      "loss": 1.1157,
+      "step": 57
+    },
+    {
+      "epoch": 0.15,
+      "learning_rate": 1.16e-06,
+      "loss": 1.1024,
+      "step": 58
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 1.18e-06,
+      "loss": 1.098,
+      "step": 59
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 1.2e-06,
+      "loss": 1.0946,
+      "step": 60
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 1.22e-06,
+      "loss": 1.0818,
+      "step": 61
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.24e-06,
+      "loss": 1.121,
+      "step": 62
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.26e-06,
+      "loss": 1.09,
+      "step": 63
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.28e-06,
+      "loss": 1.0958,
+      "step": 64
+    },
+    {
+      "epoch": 0.17,
+      "learning_rate": 1.3e-06,
+      "loss": 1.0888,
+      "step": 65
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.32e-06,
+      "loss": 1.0793,
+      "step": 66
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.3399999999999999e-06,
+      "loss": 1.0929,
+      "step": 67
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.36e-06,
+      "loss": 1.0685,
+      "step": 68
+    },
+    {
+      "epoch": 0.18,
+      "learning_rate": 1.38e-06,
+      "loss": 1.0888,
+      "step": 69
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 1.4000000000000001e-06,
+      "loss": 1.0688,
+      "step": 70
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 1.42e-06,
+      "loss": 1.0627,
+      "step": 71
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 1.44e-06,
+      "loss": 1.0627,
+      "step": 72
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.46e-06,
+      "loss": 1.0858,
+      "step": 73
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.48e-06,
+      "loss": 1.0517,
+      "step": 74
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.4999999999999998e-06,
+      "loss": 1.0626,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 1.5199999999999998e-06,
+      "loss": 1.0708,
+      "step": 76
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.54e-06,
+      "loss": 1.0391,
+      "step": 77
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.56e-06,
+      "loss": 1.0436,
+      "step": 78
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.58e-06,
+      "loss": 1.0396,
+      "step": 79
+    },
+    {
+      "epoch": 0.21,
+      "learning_rate": 1.6e-06,
+      "loss": 1.0334,
+      "step": 80
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.62e-06,
+      "loss": 1.0291,
+      "step": 81
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.64e-06,
+      "loss": 1.0421,
+      "step": 82
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.66e-06,
+      "loss": 1.0493,
+      "step": 83
+    },
+    {
+      "epoch": 0.22,
+      "learning_rate": 1.68e-06,
+      "loss": 1.0293,
+      "step": 84
+    },
+    {
+      "epoch": 0.23,
+      "learning_rate": 1.7e-06,
+      "loss": 1.0766,
+      "step": 85
+    },
+    {
+      "epoch": 0.23,
+      "learning_rate": 1.72e-06,
+      "loss": 1.058,
+      "step": 86
+    },
+    {
+      "epoch": 0.23,
+      "learning_rate": 1.74e-06,
+      "loss": 1.0244,
+      "step": 87
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.76e-06,
+      "loss": 1.0399,
+      "step": 88
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.78e-06,
+      "loss": 1.0272,
+      "step": 89
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.7999999999999997e-06,
+      "loss": 1.029,
+      "step": 90
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 1.82e-06,
+      "loss": 1.0477,
+      "step": 91
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.84e-06,
+      "loss": 1.0166,
+      "step": 92
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.8600000000000002e-06,
+      "loss": 1.0309,
+      "step": 93
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.88e-06,
+      "loss": 1.0135,
+      "step": 94
+    },
+    {
+      "epoch": 0.25,
+      "learning_rate": 1.8999999999999998e-06,
+      "loss": 1.0226,
+      "step": 95
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.9200000000000003e-06,
+      "loss": 1.0402,
+      "step": 96
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.94e-06,
+      "loss": 1.0252,
+      "step": 97
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.9600000000000003e-06,
+      "loss": 1.0064,
+      "step": 98
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 1.98e-06,
+      "loss": 0.9967,
+      "step": 99
+    },
+    {
+      "epoch": 0.27,
+      "learning_rate": 2e-06,
+      "loss": 1.0094,
+      "step": 100
+    },
+    {
+      "epoch": 0.27,
+      "learning_rate": 2.02e-06,
+      "loss": 0.9938,
+      "step": 101
+    },
+    {
+      "epoch": 0.27,
+      "learning_rate": 2.04e-06,
+      "loss": 1.0199,
+      "step": 102
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.0599999999999998e-06,
+      "loss": 1.005,
+      "step": 103
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.08e-06,
+      "loss": 1.0016,
+      "step": 104
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.1e-06,
+      "loss": 1.0169,
+      "step": 105
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 2.12e-06,
+      "loss": 0.978,
+      "step": 106
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.14e-06,
+      "loss": 0.9787,
+      "step": 107
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.1599999999999996e-06,
+      "loss": 0.9992,
+      "step": 108
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.18e-06,
+      "loss": 0.985,
+      "step": 109
+    },
+    {
+      "epoch": 0.29,
+      "learning_rate": 2.2e-06,
+      "loss": 0.9832,
+      "step": 110
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.22e-06,
+      "loss": 0.9922,
+      "step": 111
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.24e-06,
+      "loss": 0.9882,
+      "step": 112
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.26e-06,
+      "loss": 0.979,
+      "step": 113
+    },
+    {
+      "epoch": 0.3,
+      "learning_rate": 2.28e-06,
+      "loss": 0.9866,
+      "step": 114
+    },
+    {
+      "epoch": 0.31,
+      "learning_rate": 2.3e-06,
+      "loss": 0.97,
+      "step": 115
+    },
+    {
+      "epoch": 0.31,
+      "learning_rate": 2.32e-06,
+      "loss": 1.0042,
+      "step": 116
+    },
+    {
+      "epoch": 0.31,
+      "learning_rate": 2.34e-06,
+      "loss": 0.9778,
+      "step": 117
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.36e-06,
+      "loss": 0.9906,
+      "step": 118
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.38e-06,
+      "loss": 0.9547,
+      "step": 119
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.4e-06,
+      "loss": 0.9792,
+      "step": 120
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.4199999999999997e-06,
+      "loss": 0.964,
+      "step": 121
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.44e-06,
+      "loss": 0.9797,
+      "step": 122
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.4599999999999997e-06,
+      "loss": 0.9576,
+      "step": 123
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.48e-06,
+      "loss": 0.9795,
+      "step": 124
+    },
+    {
+      "epoch": 0.33,
+      "learning_rate": 2.5e-06,
+      "loss": 0.9619,
+      "step": 125
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.52e-06,
+      "loss": 0.9756,
+      "step": 126
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.5400000000000002e-06,
+      "loss": 0.9678,
+      "step": 127
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.56e-06,
+      "loss": 0.973,
+      "step": 128
+    },
+    {
+      "epoch": 0.34,
+      "learning_rate": 2.58e-06,
+      "loss": 0.9581,
+      "step": 129
+    },
+    {
+      "epoch": 0.35,
+      "learning_rate": 2.6e-06,
+      "loss": 0.9559,
+      "step": 130
+    },
+    {
+      "epoch": 0.35,
+      "learning_rate": 2.62e-06,
+      "loss": 0.9623,
+      "step": 131
+    },
+    {
+      "epoch": 0.35,
+      "learning_rate": 2.64e-06,
+      "loss": 0.9608,
+      "step": 132
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.66e-06,
+      "loss": 0.9599,
+      "step": 133
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.6799999999999998e-06,
+      "loss": 0.9481,
+      "step": 134
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.7e-06,
+      "loss": 0.951,
+      "step": 135
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 2.72e-06,
+      "loss": 0.9553,
+      "step": 136
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.7399999999999996e-06,
+      "loss": 0.9471,
+      "step": 137
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.76e-06,
+      "loss": 0.9228,
+      "step": 138
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.78e-06,
+      "loss": 0.9411,
+      "step": 139
+    },
+    {
+      "epoch": 0.37,
+      "learning_rate": 2.8000000000000003e-06,
+      "loss": 0.9541,
+      "step": 140
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.82e-06,
+      "loss": 0.9318,
+      "step": 141
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.84e-06,
+      "loss": 0.9113,
+      "step": 142
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.86e-06,
+      "loss": 0.9316,
+      "step": 143
+    },
+    {
+      "epoch": 0.38,
+      "learning_rate": 2.88e-06,
+      "loss": 0.9326,
+      "step": 144
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.9e-06,
+      "loss": 0.9471,
+      "step": 145
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.92e-06,
+      "loss": 0.9234,
+      "step": 146
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.94e-06,
+      "loss": 0.9384,
+      "step": 147
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 2.96e-06,
+      "loss": 0.9317,
+      "step": 148
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 2.98e-06,
+      "loss": 0.9157,
+      "step": 149
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 2.9999999999999997e-06,
+      "loss": 0.9411,
+      "step": 150
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 3.02e-06,
+      "loss": 0.9566,
+      "step": 151
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.0399999999999997e-06,
+      "loss": 0.9443,
+      "step": 152
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.06e-06,
+      "loss": 0.9215,
+      "step": 153
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.08e-06,
+      "loss": 0.9129,
+      "step": 154
+    },
+    {
+      "epoch": 0.41,
+      "learning_rate": 3.1e-06,
+      "loss": 0.9175,
+      "step": 155
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.12e-06,
+      "loss": 0.9309,
+      "step": 156
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.14e-06,
+      "loss": 0.9185,
+      "step": 157
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.16e-06,
+      "loss": 0.9196,
+      "step": 158
+    },
+    {
+      "epoch": 0.42,
+      "learning_rate": 3.18e-06,
+      "loss": 0.9038,
+      "step": 159
+    },
+    {
+      "epoch": 0.43,
+      "learning_rate": 3.2e-06,
+      "loss": 0.9205,
+      "step": 160
+    },
+    {
+      "epoch": 0.43,
+      "learning_rate": 3.22e-06,
+      "loss": 0.9308,
+      "step": 161
+    },
+    {
+      "epoch": 0.43,
+      "learning_rate": 3.24e-06,
+      "loss": 0.939,
+      "step": 162
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.2599999999999997e-06,
+      "loss": 0.9211,
+      "step": 163
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.28e-06,
+      "loss": 0.9214,
+      "step": 164
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.2999999999999997e-06,
+      "loss": 0.9243,
+      "step": 165
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 3.32e-06,
+      "loss": 0.9098,
+      "step": 166
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.3399999999999998e-06,
+      "loss": 0.9223,
+      "step": 167
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.36e-06,
+      "loss": 0.9213,
+      "step": 168
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.3800000000000002e-06,
+      "loss": 0.9059,
+      "step": 169
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 3.4e-06,
+      "loss": 0.8938,
+      "step": 170
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.42e-06,
+      "loss": 0.9139,
+      "step": 171
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.44e-06,
+      "loss": 0.898,
+      "step": 172
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.46e-06,
+      "loss": 0.9103,
+      "step": 173
+    },
+    {
+      "epoch": 0.46,
+      "learning_rate": 3.48e-06,
+      "loss": 0.9042,
+      "step": 174
+    },
+    {
+      "epoch": 0.47,
+      "learning_rate": 3.5e-06,
+      "loss": 0.8833,
+      "step": 175
+    },
+    {
+      "epoch": 0.47,
+      "learning_rate": 3.52e-06,
+      "loss": 0.8933,
+      "step": 176
+    },
+    {
+      "epoch": 0.47,
+      "learning_rate": 3.5399999999999996e-06,
+      "loss": 0.9022,
+      "step": 177
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.56e-06,
+      "loss": 0.9141,
+      "step": 178
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.58e-06,
+      "loss": 0.9089,
+      "step": 179
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.5999999999999994e-06,
+      "loss": 0.9007,
+      "step": 180
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 3.6199999999999996e-06,
+      "loss": 0.8985,
+      "step": 181
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.64e-06,
+      "loss": 0.8977,
+      "step": 182
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.66e-06,
+      "loss": 0.8996,
+      "step": 183
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.68e-06,
+      "loss": 0.8999,
+      "step": 184
+    },
+    {
+      "epoch": 0.49,
+      "learning_rate": 3.7e-06,
+      "loss": 0.9141,
+      "step": 185
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.7200000000000004e-06,
+      "loss": 0.8918,
+      "step": 186
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.7399999999999998e-06,
+      "loss": 0.9008,
+      "step": 187
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.76e-06,
+      "loss": 0.9021,
+      "step": 188
+    },
+    {
+      "epoch": 0.5,
+      "learning_rate": 3.7800000000000002e-06,
+      "loss": 0.8951,
+      "step": 189
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 3.7999999999999996e-06,
+      "loss": 0.9089,
+      "step": 190
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 3.82e-06,
+      "loss": 0.8966,
+      "step": 191
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 3.8400000000000005e-06,
+      "loss": 0.8937,
+      "step": 192
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.8599999999999995e-06,
+      "loss": 0.8969,
+      "step": 193
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.88e-06,
+      "loss": 0.8719,
+      "step": 194
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.9e-06,
+      "loss": 0.884,
+      "step": 195
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 3.920000000000001e-06,
+      "loss": 0.8809,
+      "step": 196
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 3.9399999999999995e-06,
+      "loss": 0.8711,
+      "step": 197
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 3.96e-06,
+      "loss": 0.877,
+      "step": 198
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 3.98e-06,
+      "loss": 0.8892,
+      "step": 199
+    },
+    {
+      "epoch": 0.53,
+      "learning_rate": 4e-06,
+      "loss": 0.8781,
+      "step": 200
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.02e-06,
+      "loss": 0.8697,
+      "step": 201
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.04e-06,
+      "loss": 0.8745,
+      "step": 202
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.059999999999999e-06,
+      "loss": 0.8738,
+      "step": 203
+    },
+    {
+      "epoch": 0.54,
+      "learning_rate": 4.08e-06,
+      "loss": 0.8804,
+      "step": 204
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 4.1000000000000006e-06,
+      "loss": 0.8843,
+      "step": 205
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 4.1199999999999995e-06,
+      "loss": 0.8736,
+      "step": 206
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 4.14e-06,
+      "loss": 0.8689,
+      "step": 207
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.16e-06,
+      "loss": 0.8614,
+      "step": 208
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.180000000000001e-06,
+      "loss": 0.866,
+      "step": 209
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.2e-06,
+      "loss": 0.8598,
+      "step": 210
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 4.22e-06,
+      "loss": 0.878,
+      "step": 211
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.24e-06,
+      "loss": 0.8624,
+      "step": 212
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.26e-06,
+      "loss": 0.8788,
+      "step": 213
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.28e-06,
+      "loss": 0.8713,
+      "step": 214
+    },
+    {
+      "epoch": 0.57,
+      "learning_rate": 4.3e-06,
+      "loss": 0.8682,
+      "step": 215
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 4.319999999999999e-06,
+      "loss": 0.8827,
+      "step": 216
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 4.34e-06,
+      "loss": 0.8754,
+      "step": 217
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 4.36e-06,
+      "loss": 0.8867,
+      "step": 218
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.3799999999999996e-06,
+      "loss": 0.8707,
+      "step": 219
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.4e-06,
+      "loss": 0.8655,
+      "step": 220
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.42e-06,
+      "loss": 0.8503,
+      "step": 221
+    },
+    {
+      "epoch": 0.59,
+      "learning_rate": 4.44e-06,
+      "loss": 0.8674,
+      "step": 222
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.46e-06,
+      "loss": 0.8507,
+      "step": 223
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.48e-06,
+      "loss": 0.8668,
+      "step": 224
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.5e-06,
+      "loss": 0.8548,
+      "step": 225
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 4.52e-06,
+      "loss": 0.8756,
+      "step": 226
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.54e-06,
+      "loss": 0.8424,
+      "step": 227
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.56e-06,
+      "loss": 0.8501,
+      "step": 228
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.579999999999999e-06,
+      "loss": 0.8633,
+      "step": 229
+    },
+    {
+      "epoch": 0.61,
+      "learning_rate": 4.6e-06,
+      "loss": 0.859,
+      "step": 230
+    },
+    {
+      "epoch": 0.62,
+      "learning_rate": 4.62e-06,
+      "loss": 0.8497,
+      "step": 231
+    },
+    {
+      "epoch": 0.62,
+      "learning_rate": 4.64e-06,
+      "loss": 0.8491,
+      "step": 232
+    },
+    {
+      "epoch": 0.62,
+      "learning_rate": 4.6599999999999994e-06,
+      "loss": 0.8596,
+      "step": 233
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.68e-06,
+      "loss": 0.8636,
+      "step": 234
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.7e-06,
+      "loss": 0.8677,
+      "step": 235
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.72e-06,
+      "loss": 0.8605,
+      "step": 236
+    },
+    {
+      "epoch": 0.63,
+      "learning_rate": 4.74e-06,
+      "loss": 0.8493,
+      "step": 237
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.76e-06,
+      "loss": 0.8367,
+      "step": 238
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.78e-06,
+      "loss": 0.8443,
+      "step": 239
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.8e-06,
+      "loss": 0.8464,
+      "step": 240
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 4.8200000000000004e-06,
+      "loss": 0.8588,
+      "step": 241
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.839999999999999e-06,
+      "loss": 0.8502,
+      "step": 242
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.86e-06,
+      "loss": 0.849,
+      "step": 243
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.88e-06,
+      "loss": 0.8576,
+      "step": 244
+    },
+    {
+      "epoch": 0.65,
+      "learning_rate": 4.9e-06,
+      "loss": 0.8418,
+      "step": 245
+    },
+    {
+      "epoch": 0.66,
+      "learning_rate": 4.9199999999999995e-06,
+      "loss": 0.8717,
+      "step": 246
+    },
+    {
+      "epoch": 0.66,
+      "learning_rate": 4.94e-06,
+      "loss": 0.8529,
+      "step": 247
+    },
+    {
+      "epoch": 0.66,
+      "learning_rate": 4.96e-06,
+      "loss": 0.8328,
+      "step": 248
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 4.98e-06,
+      "loss": 0.85,
+      "step": 249
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 5e-06,
+      "loss": 0.8376,
+      "step": 250
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 5.02e-06,
+      "loss": 0.8431,
+      "step": 251
+    },
+    {
+      "epoch": 0.67,
+      "learning_rate": 5.04e-06,
+      "loss": 0.8446,
+      "step": 252
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.06e-06,
+      "loss": 0.8424,
+      "step": 253
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.0800000000000005e-06,
+      "loss": 0.8377,
+      "step": 254
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.0999999999999995e-06,
+      "loss": 0.8453,
+      "step": 255
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 5.12e-06,
+      "loss": 0.8591,
+      "step": 256
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.14e-06,
+      "loss": 0.8536,
+      "step": 257
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.16e-06,
+      "loss": 0.8399,
+      "step": 258
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.1799999999999995e-06,
+      "loss": 0.8259,
+      "step": 259
+    },
+    {
+      "epoch": 0.69,
+      "learning_rate": 5.2e-06,
+      "loss": 0.8399,
+      "step": 260
+    },
+    {
+      "epoch": 0.7,
+      "learning_rate": 5.219999999999999e-06,
+      "loss": 0.8443,
+      "step": 261
+    },
+    {
+      "epoch": 0.7,
+      "learning_rate": 5.24e-06,
+      "loss": 0.8331,
+      "step": 262
+    },
+    {
+      "epoch": 0.7,
+      "learning_rate": 5.2600000000000005e-06,
+      "loss": 0.8268,
+      "step": 263
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.28e-06,
+      "loss": 0.8338,
+      "step": 264
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.3e-06,
+      "loss": 0.8287,
+      "step": 265
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.32e-06,
+      "loss": 0.8273,
+      "step": 266
+    },
+    {
+      "epoch": 0.71,
+      "learning_rate": 5.3400000000000005e-06,
+      "loss": 0.8471,
+      "step": 267
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.3599999999999995e-06,
+      "loss": 0.8364,
+      "step": 268
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.38e-06,
+      "loss": 0.8167,
+      "step": 269
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.4e-06,
+      "loss": 0.8433,
+      "step": 270
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 5.42e-06,
+      "loss": 0.8163,
+      "step": 271
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.44e-06,
+      "loss": 0.8186,
+      "step": 272
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.46e-06,
+      "loss": 0.8491,
+      "step": 273
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.479999999999999e-06,
+      "loss": 0.8222,
+      "step": 274
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 5.5e-06,
+      "loss": 0.8362,
+      "step": 275
+    },
+    {
+      "epoch": 0.74,
+      "learning_rate": 5.52e-06,
+      "loss": 0.8375,
+      "step": 276
+    },
+    {
+      "epoch": 0.74,
+      "learning_rate": 5.54e-06,
+      "loss": 0.7934,
+      "step": 277
+    },
+    {
+      "epoch": 0.74,
+      "learning_rate": 5.56e-06,
+      "loss": 0.8096,
+      "step": 278
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.58e-06,
+      "loss": 0.8185,
+      "step": 279
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.600000000000001e-06,
+      "loss": 0.8126,
+      "step": 280
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.6199999999999996e-06,
+      "loss": 0.8297,
+      "step": 281
+    },
+    {
+      "epoch": 0.75,
+      "learning_rate": 5.64e-06,
+      "loss": 0.8196,
+      "step": 282
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.66e-06,
+      "loss": 0.8251,
+      "step": 283
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.68e-06,
+      "loss": 0.8291,
+      "step": 284
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.7e-06,
+      "loss": 0.8067,
+      "step": 285
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 5.72e-06,
+      "loss": 0.8171,
+      "step": 286
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.739999999999999e-06,
+      "loss": 0.8091,
+      "step": 287
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.76e-06,
+      "loss": 0.8149,
+      "step": 288
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.78e-06,
+      "loss": 0.8393,
+      "step": 289
+    },
+    {
+      "epoch": 0.77,
+      "learning_rate": 5.8e-06,
+      "loss": 0.8157,
+      "step": 290
+    },
+    {
+      "epoch": 0.78,
+      "learning_rate": 5.819999999999999e-06,
+      "loss": 0.8043,
+      "step": 291
+    },
+    {
+      "epoch": 0.78,
+      "learning_rate": 5.84e-06,
+      "loss": 0.8166,
+      "step": 292
+    },
+    {
+      "epoch": 0.78,
+      "learning_rate": 5.860000000000001e-06,
+      "loss": 0.8022,
+      "step": 293
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.88e-06,
+      "loss": 0.7949,
+      "step": 294
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.9e-06,
+      "loss": 0.8102,
+      "step": 295
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.92e-06,
+      "loss": 0.81,
+      "step": 296
+    },
+    {
+      "epoch": 0.79,
+      "learning_rate": 5.94e-06,
+      "loss": 0.8104,
+      "step": 297
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 5.96e-06,
+      "loss": 0.8089,
+      "step": 298
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 5.98e-06,
+      "loss": 0.824,
+      "step": 299
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 5.999999999999999e-06,
+      "loss": 0.8145,
+      "step": 300
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 6.02e-06,
+      "loss": 0.8085,
+      "step": 301
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.04e-06,
+      "loss": 0.8183,
+      "step": 302
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.0600000000000004e-06,
+      "loss": 0.8114,
+      "step": 303
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.079999999999999e-06,
+      "loss": 0.8112,
+      "step": 304
+    },
+    {
+      "epoch": 0.81,
+      "learning_rate": 6.1e-06,
+      "loss": 0.8308,
+      "step": 305
+    },
+    {
+      "epoch": 0.82,
+      "learning_rate": 6.12e-06,
+      "loss": 0.8303,
+      "step": 306
+    },
+    {
+      "epoch": 0.82,
+      "learning_rate": 6.14e-06,
+      "loss": 0.8287,
+      "step": 307
+    },
+    {
+      "epoch": 0.82,
+      "learning_rate": 6.16e-06,
+      "loss": 0.7957,
+      "step": 308
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.18e-06,
+      "loss": 0.817,
+      "step": 309
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.2e-06,
+      "loss": 0.8209,
+      "step": 310
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.22e-06,
+      "loss": 0.8019,
+      "step": 311
+    },
+    {
+      "epoch": 0.83,
+      "learning_rate": 6.24e-06,
+      "loss": 0.8099,
+      "step": 312
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.259999999999999e-06,
+      "loss": 0.8275,
+      "step": 313
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.28e-06,
+      "loss": 0.814,
+      "step": 314
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.3e-06,
+      "loss": 0.7992,
+      "step": 315
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 6.32e-06,
+      "loss": 0.7964,
+      "step": 316
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.3399999999999994e-06,
+      "loss": 0.8013,
+      "step": 317
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.36e-06,
+      "loss": 0.8023,
+      "step": 318
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.38e-06,
+      "loss": 0.8074,
+      "step": 319
+    },
+    {
+      "epoch": 0.85,
+      "learning_rate": 6.4e-06,
+      "loss": 0.8154,
+      "step": 320
+    },
+    {
+      "epoch": 0.86,
+      "learning_rate": 6.42e-06,
+      "loss": 0.7984,
+      "step": 321
+    },
+    {
+      "epoch": 0.86,
+      "learning_rate": 6.44e-06,
+      "loss": 0.7987,
+      "step": 322
+    },
+    {
+      "epoch": 0.86,
+      "learning_rate": 6.46e-06,
+      "loss": 0.8007,
+      "step": 323
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.48e-06,
+      "loss": 0.7999,
+      "step": 324
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.5000000000000004e-06,
+      "loss": 0.8107,
+      "step": 325
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.519999999999999e-06,
+      "loss": 0.7928,
+      "step": 326
+    },
+    {
+      "epoch": 0.87,
+      "learning_rate": 6.54e-06,
+      "loss": 0.8033,
+      "step": 327
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.56e-06,
+      "loss": 0.802,
+      "step": 328
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.58e-06,
+      "loss": 0.8056,
+      "step": 329
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.5999999999999995e-06,
+      "loss": 0.7857,
+      "step": 330
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 6.62e-06,
+      "loss": 0.8046,
+      "step": 331
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.64e-06,
+      "loss": 0.7865,
+      "step": 332
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.66e-06,
+      "loss": 0.814,
+      "step": 333
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.6799999999999996e-06,
+      "loss": 0.7934,
+      "step": 334
+    },
+    {
+      "epoch": 0.89,
+      "learning_rate": 6.7e-06,
+      "loss": 0.7893,
+      "step": 335
+    },
+    {
+      "epoch": 0.9,
+      "learning_rate": 6.72e-06,
+      "loss": 0.7984,
+      "step": 336
+    },
+    {
+      "epoch": 0.9,
+      "learning_rate": 6.74e-06,
+      "loss": 0.7932,
+      "step": 337
+    },
+    {
+      "epoch": 0.9,
+      "learning_rate": 6.7600000000000005e-06,
+      "loss": 0.7886,
+      "step": 338
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.7799999999999995e-06,
+      "loss": 0.7925,
+      "step": 339
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.8e-06,
+      "loss": 0.7872,
+      "step": 340
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.82e-06,
+      "loss": 0.8191,
+      "step": 341
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 6.84e-06,
+      "loss": 0.7994,
+      "step": 342
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.8599999999999995e-06,
+      "loss": 0.8014,
+      "step": 343
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.88e-06,
+      "loss": 0.7998,
+      "step": 344
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.9e-06,
+      "loss": 0.7809,
+      "step": 345
+    },
+    {
+      "epoch": 0.92,
+      "learning_rate": 6.92e-06,
+      "loss": 0.7845,
+      "step": 346
+    },
+    {
+      "epoch": 0.93,
+      "learning_rate": 6.94e-06,
+      "loss": 0.7795,
+      "step": 347
+    },
+    {
+      "epoch": 0.93,
+      "learning_rate": 6.96e-06,
+      "loss": 0.7959,
+      "step": 348
+    },
+    {
+      "epoch": 0.93,
+      "learning_rate": 6.979999999999999e-06,
+      "loss": 0.7848,
+      "step": 349
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 7e-06,
+      "loss": 0.7915,
+      "step": 350
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 6.999986848712049e-06,
+      "loss": 0.7891,
+      "step": 351
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 6.999947394947031e-06,
+      "loss": 0.7838,
+      "step": 352
+    },
+    {
+      "epoch": 0.94,
+      "learning_rate": 6.999881639001441e-06,
+      "loss": 0.8101,
+      "step": 353
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999789581369437e-06,
+      "loss": 0.8062,
+      "step": 354
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999671222742834e-06,
+      "loss": 0.7923,
+      "step": 355
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999526564011098e-06,
+      "loss": 0.8033,
+      "step": 356
+    },
+    {
+      "epoch": 0.95,
+      "learning_rate": 6.999355606261343e-06,
+      "loss": 0.7905,
+      "step": 357
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.999158350778321e-06,
+      "loss": 0.7833,
+      "step": 358
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.998934799044413e-06,
+      "loss": 0.7916,
+      "step": 359
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.9986849527396114e-06,
+      "loss": 0.7848,
+      "step": 360
+    },
+    {
+      "epoch": 0.96,
+      "learning_rate": 6.998408813741518e-06,
+      "loss": 0.7888,
+      "step": 361
+    },
+    {
+      "epoch": 0.97,
+      "learning_rate": 6.9981063841253256e-06,
+      "loss": 0.7971,
+      "step": 362
+    },
+    {
+      "epoch": 0.97,
+      "learning_rate": 6.997777666163796e-06,
+      "loss": 0.7947,
+      "step": 363
+    },
+    {
+      "epoch": 0.97,
+      "learning_rate": 6.997422662327253e-06,
+      "loss": 0.7943,
+      "step": 364
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.9970413752835595e-06,
+      "loss": 0.7864,
+      "step": 365
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.996633807898094e-06,
+      "loss": 0.8132,
+      "step": 366
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.996199963233736e-06,
+      "loss": 0.7814,
+      "step": 367
+    },
+    {
+      "epoch": 0.98,
+      "learning_rate": 6.995739844550836e-06,
+      "loss": 0.8083,
+      "step": 368
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.995253455307197e-06,
+      "loss": 0.7831,
+      "step": 369
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.994740799158044e-06,
+      "loss": 0.7775,
+      "step": 370
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.994201879955999e-06,
+      "loss": 0.7969,
+      "step": 371
+    },
+    {
+      "epoch": 0.99,
+      "learning_rate": 6.993636701751052e-06,
+      "loss": 0.7726,
+      "step": 372
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.993045268790529e-06,
+      "loss": 0.783,
+      "step": 373
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.9924275855190615e-06,
+      "loss": 0.7802,
+      "step": 374
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.991783656578554e-06,
+      "loss": 0.7885,
+      "step": 375
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.991113486808145e-06,
+      "loss": 0.7916,
+      "step": 376
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.990417081244175e-06,
+      "loss": 0.7961,
+      "step": 377
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.989694445120147e-06,
+      "loss": 0.7912,
+      "step": 378
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.9889455838666875e-06,
+      "loss": 0.7711,
+      "step": 379
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.988170503111504e-06,
+      "loss": 0.7695,
+      "step": 380
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.987369208679347e-06,
+      "loss": 0.7693,
+      "step": 381
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.986541706591961e-06,
+      "loss": 0.7944,
+      "step": 382
+    },
+    {
+      "epoch": 1.0,
+      "learning_rate": 6.985688003068041e-06,
+      "loss": 0.7792,
+      "step": 383
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.984808104523189e-06,
+      "loss": 0.7852,
+      "step": 384
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.983902017569859e-06,
+      "loss": 0.7866,
+      "step": 385
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.982969749017318e-06,
+      "loss": 0.7708,
+      "step": 386
+    },
+    {
+      "epoch": 1.01,
+      "learning_rate": 6.982011305871581e-06,
+      "loss": 0.7979,
+      "step": 387
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.981026695335371e-06,
+      "loss": 0.7729,
+      "step": 388
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.980015924808057e-06,
+      "loss": 0.7846,
+      "step": 389
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.978979001885602e-06,
+      "loss": 0.8077,
+      "step": 390
+    },
+    {
+      "epoch": 1.02,
+      "learning_rate": 6.9779159343605024e-06,
+      "loss": 0.7805,
+      "step": 391
+    },
+    {
+      "epoch": 1.03,
+      "learning_rate": 6.9768267302217355e-06,
+      "loss": 0.7984,
+      "step": 392
+    },
+    {
+      "epoch": 1.03,
+      "learning_rate": 6.975711397654693e-06,
+      "loss": 0.775,
+      "step": 393
+    },
+    {
+      "epoch": 1.03,
+      "learning_rate": 6.974569945041124e-06,
+      "loss": 0.7771,
+      "step": 394
+    },
+    {
+      "epoch": 1.04,
+      "learning_rate": 6.97340238095907e-06,
+      "loss": 0.7963,
+      "step": 395
+    },
+    {
+      "epoch": 1.04,
+      "learning_rate": 6.972208714182799e-06,
+      "loss": 0.7831,
+      "step": 396
+    },
+    {
+      "epoch": 1.04,
+      "learning_rate": 6.970988953682744e-06,
+      "loss": 0.7892,
+      "step": 397
+    },
+    {
+      "epoch": 1.04,
+      "learning_rate": 6.96974310862543e-06,
+      "loss": 0.7852,
+      "step": 398
+    },
+    {
+      "epoch": 1.05,
+      "learning_rate": 6.9684711883734115e-06,
+      "loss": 0.7965,
+      "step": 399
+    },
+    {
+      "epoch": 1.05,
+      "learning_rate": 6.9671732024851965e-06,
+      "loss": 0.766,
+      "step": 400
+    },
+    {
+      "epoch": 1.05,
+      "learning_rate": 6.965849160715176e-06,
+      "loss": 0.7918,
+      "step": 401
+    },
+    {
+      "epoch": 1.05,
+      "learning_rate": 6.964499073013553e-06,
+      "loss": 0.767,
+      "step": 402
+    },
+    {
+      "epoch": 1.06,
+      "learning_rate": 6.963122949526267e-06,
+      "loss": 0.7807,
+      "step": 403
+    },
+    {
+      "epoch": 1.06,
+      "learning_rate": 6.961720800594914e-06,
+      "loss": 0.7976,
+      "step": 404
+    },
+    {
+      "epoch": 1.06,
+      "learning_rate": 6.960292636756675e-06,
+      "loss": 0.7732,
+      "step": 405
+    },
+    {
+      "epoch": 1.06,
+      "learning_rate": 6.9588384687442315e-06,
+      "loss": 0.7791,
+      "step": 406
+    },
+    {
+      "epoch": 1.07,
+      "learning_rate": 6.957358307485689e-06,
+      "loss": 0.7776,
+      "step": 407
+    },
+    {
+      "epoch": 1.07,
+      "learning_rate": 6.9558521641044894e-06,
+      "loss": 0.7885,
+      "step": 408
+    },
+    {
+      "epoch": 1.07,
+      "learning_rate": 6.954320049919333e-06,
+      "loss": 0.7852,
+      "step": 409
+    },
+    {
+      "epoch": 1.08,
+      "learning_rate": 6.952761976444094e-06,
+      "loss": 0.7919,
+      "step": 410
+    },
+    {
+      "epoch": 1.08,
+      "learning_rate": 6.9511779553877245e-06,
+      "loss": 0.7863,
+      "step": 411
+    },
+    {
+      "epoch": 1.08,
+      "learning_rate": 6.949567998654181e-06,
+      "loss": 0.7738,
+      "step": 412
+    },
+    {
+      "epoch": 1.08,
+      "learning_rate": 6.947932118342319e-06,
+      "loss": 0.7629,
+      "step": 413
+    },
+    {
+      "epoch": 1.09,
+      "learning_rate": 6.946270326745818e-06,
+      "loss": 0.7802,
+      "step": 414
+    },
+    {
+      "epoch": 1.09,
+      "learning_rate": 6.944582636353076e-06,
+      "loss": 0.7803,
+      "step": 415
+    },
+    {
+      "epoch": 1.09,
+      "learning_rate": 6.942869059847123e-06,
+      "loss": 0.8036,
+      "step": 416
+    },
+    {
+      "epoch": 1.09,
+      "learning_rate": 6.941129610105525e-06,
+      "loss": 0.772,
+      "step": 417
+    },
+    {
+      "epoch": 1.1,
+      "learning_rate": 6.939364300200283e-06,
+      "loss": 0.7958,
+      "step": 418
+    },
+    {
+      "epoch": 1.1,
+      "learning_rate": 6.93757314339774e-06,
+      "loss": 0.7837,
+      "step": 419
+    },
+    {
+      "epoch": 1.1,
+      "learning_rate": 6.935756153158478e-06,
+      "loss": 0.7632,
+      "step": 420
+    },
+    {
+      "epoch": 1.1,
+      "learning_rate": 6.933913343137219e-06,
+      "loss": 0.7921,
+      "step": 421
+    },
+    {
+      "epoch": 1.11,
+      "learning_rate": 6.9320447271827186e-06,
+      "loss": 0.793,
+      "step": 422
+    },
+    {
+      "epoch": 1.11,
+      "learning_rate": 6.930150319337667e-06,
+      "loss": 0.789,
+      "step": 423
+    },
+    {
+      "epoch": 1.11,
+      "learning_rate": 6.92823013383858e-06,
+      "loss": 0.7785,
+      "step": 424
+    },
+    {
+      "epoch": 1.12,
+      "learning_rate": 6.9262841851156935e-06,
+      "loss": 0.7719,
+      "step": 425
+    },
+    {
+      "epoch": 1.12,
+      "learning_rate": 6.924312487792855e-06,
+      "loss": 0.791,
+      "step": 426
+    },
+    {
+      "epoch": 1.12,
+      "learning_rate": 6.922315056687412e-06,
+      "loss": 0.795,
+      "step": 427
+    },
+    {
+      "epoch": 1.12,
+      "learning_rate": 6.920291906810102e-06,
+      "loss": 0.7837,
+      "step": 428
+    },
+    {
+      "epoch": 1.13,
+      "learning_rate": 6.918243053364941e-06,
+      "loss": 0.8042,
+      "step": 429
+    },
+    {
+      "epoch": 1.13,
+      "learning_rate": 6.916168511749106e-06,
+      "loss": 0.78,
+      "step": 430
+    },
+    {
+      "epoch": 1.13,
+      "learning_rate": 6.914068297552825e-06,
+      "loss": 0.7781,
+      "step": 431
+    },
+    {
+      "epoch": 1.13,
+      "learning_rate": 6.9119424265592495e-06,
+      "loss": 0.7679,
+      "step": 432
+    },
+    {
+      "epoch": 1.14,
+      "learning_rate": 6.909790914744349e-06,
+      "loss": 0.761,
+      "step": 433
+    },
+    {
+      "epoch": 1.14,
+      "learning_rate": 6.907613778276779e-06,
+      "loss": 0.7773,
+      "step": 434
+    },
+    {
+      "epoch": 1.14,
+      "learning_rate": 6.90541103351777e-06,
+      "loss": 0.7817,
+      "step": 435
+    },
+    {
+      "epoch": 1.14,
+      "learning_rate": 6.903182697020994e-06,
+      "loss": 0.7634,
+      "step": 436
+    },
+    {
+      "epoch": 1.15,
+      "learning_rate": 6.90092878553245e-06,
+      "loss": 0.7739,
+      "step": 437
+    },
+    {
+      "epoch": 1.15,
+      "learning_rate": 6.898649315990332e-06,
+      "loss": 0.7679,
+      "step": 438
+    },
+    {
+      "epoch": 1.15,
+      "learning_rate": 6.896344305524902e-06,
+      "loss": 0.7755,
+      "step": 439
+    },
+    {
+      "epoch": 1.16,
+      "learning_rate": 6.894013771458362e-06,
+      "loss": 0.7964,
+      "step": 440
+    },
+    {
+      "epoch": 1.16,
+      "learning_rate": 6.891657731304729e-06,
+      "loss": 0.7756,
+      "step": 441
+    },
+    {
+      "epoch": 1.16,
+      "learning_rate": 6.889276202769695e-06,
+      "loss": 0.7938,
+      "step": 442
+    },
+    {
+      "epoch": 1.16,
+      "learning_rate": 6.886869203750498e-06,
+      "loss": 0.7752,
+      "step": 443
+    },
+    {
+      "epoch": 1.17,
+      "learning_rate": 6.884436752335787e-06,
+      "loss": 0.7724,
+      "step": 444
+    },
+    {
+      "epoch": 1.17,
+      "learning_rate": 6.881978866805488e-06,
+      "loss": 0.7867,
+      "step": 445
+    },
+    {
+      "epoch": 1.17,
+      "learning_rate": 6.879495565630666e-06,
+      "loss": 0.7843,
+      "step": 446
+    },
+    {
+      "epoch": 1.17,
+      "learning_rate": 6.87698686747338e-06,
+      "loss": 0.7912,
+      "step": 447
+    },
+    {
+      "epoch": 1.18,
+      "learning_rate": 6.8744527911865535e-06,
+      "loss": 0.7851,
+      "step": 448
+    },
+    {
+      "epoch": 1.18,
+      "learning_rate": 6.871893355813823e-06,
+      "loss": 0.7682,
+      "step": 449
+    },
+    {
+      "epoch": 1.18,
+      "learning_rate": 6.869308580589402e-06,
+      "loss": 0.7538,
+      "step": 450
+    },
+    {
+      "epoch": 1.18,
+      "learning_rate": 6.866698484937932e-06,
+      "loss": 0.7906,
+      "step": 451
+    },
+    {
+      "epoch": 1.19,
+      "learning_rate": 6.864063088474338e-06,
+      "loss": 0.7771,
+      "step": 452
+    },
+    {
+      "epoch": 1.19,
+      "learning_rate": 6.861402411003682e-06,
+      "loss": 0.7749,
+      "step": 453
+    },
+    {
+      "epoch": 1.19,
+      "learning_rate": 6.858716472521012e-06,
+      "loss": 0.77,
+      "step": 454
+    },
+    {
+      "epoch": 1.2,
+      "learning_rate": 6.856005293211217e-06,
+      "loss": 0.7723,
+      "step": 455
+    },
+    {
+      "epoch": 1.2,
+      "learning_rate": 6.853268893448865e-06,
+      "loss": 0.7714,
+      "step": 456
+    },
+    {
+      "epoch": 1.2,
+      "learning_rate": 6.850507293798059e-06,
+      "loss": 0.7736,
+      "step": 457
+    },
+    {
+      "epoch": 1.2,
+      "learning_rate": 6.847720515012284e-06,
+      "loss": 0.7748,
+      "step": 458
+    },
+    {
+      "epoch": 1.21,
+      "learning_rate": 6.8449085780342395e-06,
+      "loss": 0.7886,
+      "step": 459
+    },
+    {
+      "epoch": 1.21,
+      "learning_rate": 6.842071503995695e-06,
+      "loss": 0.7771,
+      "step": 460
+    },
+    {
+      "epoch": 1.21,
+      "learning_rate": 6.839209314217322e-06,
+      "loss": 0.8013,
+      "step": 461
+    },
+    {
+      "epoch": 1.21,
+      "learning_rate": 6.83632203020854e-06,
+      "loss": 0.7785,
+      "step": 462
+    },
+    {
+      "epoch": 1.22,
+      "learning_rate": 6.8334096736673505e-06,
+      "loss": 0.778,
+      "step": 463
+    },
+    {
+      "epoch": 1.22,
+      "learning_rate": 6.830472266480176e-06,
+      "loss": 0.7811,
+      "step": 464
+    },
+    {
+      "epoch": 1.22,
+      "learning_rate": 6.827509830721694e-06,
+      "loss": 0.7668,
+      "step": 465
+    },
+    {
+      "epoch": 1.22,
+      "learning_rate": 6.824522388654676e-06,
+      "loss": 0.7761,
+      "step": 466
+    },
+    {
+      "epoch": 1.23,
+      "learning_rate": 6.821509962729811e-06,
+      "loss": 0.7784,
+      "step": 467
+    },
+    {
+      "epoch": 1.23,
+      "learning_rate": 6.818472575585549e-06,
+      "loss": 0.7844,
+      "step": 468
+    },
+    {
+      "epoch": 1.23,
+      "learning_rate": 6.8154102500479155e-06,
+      "loss": 0.7892,
+      "step": 469
+    },
+    {
+      "epoch": 1.24,
+      "learning_rate": 6.812323009130357e-06,
+      "loss": 0.7758,
+      "step": 470
+    },
+    {
+      "epoch": 1.24,
+      "learning_rate": 6.809210876033554e-06,
+      "loss": 0.7931,
+      "step": 471
+    },
+    {
+      "epoch": 1.24,
+      "learning_rate": 6.806073874145257e-06,
+      "loss": 0.771,
+      "step": 472
+    },
+    {
+      "epoch": 1.24,
+      "learning_rate": 6.802912027040099e-06,
+      "loss": 0.7969,
+      "step": 473
+    },
+    {
+      "epoch": 1.25,
+      "learning_rate": 6.799725358479433e-06,
+      "loss": 0.7696,
+      "step": 474
+    },
+    {
+      "epoch": 1.25,
+      "learning_rate": 6.79651389241114e-06,
+      "loss": 0.7863,
+      "step": 475
+    },
+    {
+      "epoch": 1.25,
+      "learning_rate": 6.793277652969458e-06,
+      "loss": 0.778,
+      "step": 476
+    },
+    {
+      "epoch": 1.25,
+      "learning_rate": 6.790016664474797e-06,
+      "loss": 0.768,
+      "step": 477
+    },
+    {
+      "epoch": 1.26,
+      "learning_rate": 6.786730951433557e-06,
+      "loss": 0.7675,
+      "step": 478
+    },
+    {
+      "epoch": 1.26,
+      "learning_rate": 6.78342053853794e-06,
+      "loss": 0.7711,
+      "step": 479
+    },
+    {
+      "epoch": 1.26,
+      "learning_rate": 6.780085450665775e-06,
+      "loss": 0.7677,
+      "step": 480
+    },
+    {
+      "epoch": 1.26,
+      "learning_rate": 6.776725712880315e-06,
+      "loss": 0.7544,
+      "step": 481
+    },
+    {
+      "epoch": 1.27,
+      "learning_rate": 6.773341350430065e-06,
+      "loss": 0.7687,
+      "step": 482
+    },
+    {
+      "epoch": 1.27,
+      "learning_rate": 6.769932388748583e-06,
+      "loss": 0.7768,
+      "step": 483
+    },
+    {
+      "epoch": 1.27,
+      "learning_rate": 6.766498853454287e-06,
+      "loss": 0.7783,
+      "step": 484
+    },
+    {
+      "epoch": 1.28,
+      "learning_rate": 6.763040770350272e-06,
+      "loss": 0.7832,
+      "step": 485
+    },
+    {
+      "epoch": 1.28,
+      "learning_rate": 6.759558165424105e-06,
+      "loss": 0.763,
+      "step": 486
+    },
+    {
+      "epoch": 1.28,
+      "learning_rate": 6.75605106484764e-06,
+      "loss": 0.7656,
+      "step": 487
+    },
+    {
+      "epoch": 1.28,
+      "learning_rate": 6.752519494976812e-06,
+      "loss": 0.7685,
+      "step": 488
+    },
+    {
+      "epoch": 1.29,
+      "learning_rate": 6.748963482351447e-06,
+      "loss": 0.7724,
+      "step": 489
+    },
+    {
+      "epoch": 1.29,
+      "learning_rate": 6.745383053695056e-06,
+      "loss": 0.7693,
+      "step": 490
+    },
+    {
+      "epoch": 1.29,
+      "learning_rate": 6.741778235914637e-06,
+      "loss": 0.7815,
+      "step": 491
+    },
+    {
+      "epoch": 1.29,
+      "learning_rate": 6.738149056100475e-06,
+      "loss": 0.7702,
+      "step": 492
+    },
+    {
+      "epoch": 1.3,
+      "learning_rate": 6.734495541525934e-06,
+      "loss": 0.7744,
+      "step": 493
+    },
+    {
+      "epoch": 1.3,
+      "learning_rate": 6.730817719647257e-06,
+      "loss": 0.766,
+      "step": 494
+    },
+    {
+      "epoch": 1.3,
+      "learning_rate": 6.727115618103354e-06,
+      "loss": 0.7582,
+      "step": 495
+    },
+    {
+      "epoch": 1.3,
+      "learning_rate": 6.7233892647156e-06,
+      "loss": 0.7821,
+      "step": 496
+    },
+    {
+      "epoch": 1.31,
+      "learning_rate": 6.719638687487618e-06,
+      "loss": 0.7831,
+      "step": 497
+    },
+    {
+      "epoch": 1.31,
+      "learning_rate": 6.71586391460508e-06,
+      "loss": 0.7501,
+      "step": 498
+    },
+    {
+      "epoch": 1.31,
+      "learning_rate": 6.712064974435485e-06,
+      "loss": 0.783,
+      "step": 499
+    },
+    {
+      "epoch": 1.32,
+      "learning_rate": 6.708241895527952e-06,
+      "loss": 0.7857,
+      "step": 500
+    },
+    {
+      "epoch": 1.32,
+      "learning_rate": 6.704394706613e-06,
+      "loss": 0.7757,
+      "step": 501
+    },
+    {
+      "epoch": 1.32,
+      "learning_rate": 6.700523436602338e-06,
+      "loss": 0.7651,
+      "step": 502
+    },
+    {
+      "epoch": 1.32,
+      "learning_rate": 6.696628114588645e-06,
+      "loss": 0.7759,
+      "step": 503
+    },
+    {
+      "epoch": 1.33,
+      "learning_rate": 6.69270876984535e-06,
+      "loss": 0.7801,
+      "step": 504
+    },
+    {
+      "epoch": 1.33,
+      "learning_rate": 6.688765431826413e-06,
+      "loss": 0.7662,
+      "step": 505
+    },
+    {
+      "epoch": 1.33,
+      "learning_rate": 6.6847981301661046e-06,
+      "loss": 0.7566,
+      "step": 506
+    },
+    {
+      "epoch": 1.33,
+      "learning_rate": 6.680806894678784e-06,
+      "loss": 0.7733,
+      "step": 507
+    },
+    {
+      "epoch": 1.34,
+      "learning_rate": 6.676791755358671e-06,
+      "loss": 0.754,
+      "step": 508
+    },
+    {
+      "epoch": 1.34,
+      "learning_rate": 6.6727527423796255e-06,
+      "loss": 0.7871,
+      "step": 509
+    },
+    {
+      "epoch": 1.34,
+      "learning_rate": 6.668689886094918e-06,
+      "loss": 0.7614,
+      "step": 510
+    },
+    {
+      "epoch": 1.35,
+      "learning_rate": 6.664603217037001e-06,
+      "loss": 0.7691,
+      "step": 511
+    },
+    {
+      "epoch": 1.35,
+      "learning_rate": 6.66049276591728e-06,
+      "loss": 0.7669,
+      "step": 512
+    },
+    {
+      "epoch": 1.35,
+      "learning_rate": 6.656358563625887e-06,
+      "loss": 0.7487,
+      "step": 513
+    },
+    {
+      "epoch": 1.35,
+      "learning_rate": 6.6522006412314404e-06,
+      "loss": 0.7689,
+      "step": 514
+    },
+    {
+      "epoch": 1.36,
+      "learning_rate": 6.648019029980816e-06,
+      "loss": 0.7628,
+      "step": 515
+    },
+    {
+      "epoch": 1.36,
+      "learning_rate": 6.643813761298915e-06,
+      "loss": 0.7426,
+      "step": 516
+    },
+    {
+      "epoch": 1.36,
+      "learning_rate": 6.6395848667884215e-06,
+      "loss": 0.7693,
+      "step": 517
+    },
+    {
+      "epoch": 1.36,
+      "learning_rate": 6.635332378229571e-06,
+      "loss": 0.7734,
+      "step": 518
+    },
+    {
+      "epoch": 1.37,
+      "learning_rate": 6.631056327579905e-06,
+      "loss": 0.7633,
+      "step": 519
+    },
+    {
+      "epoch": 1.37,
+      "learning_rate": 6.626756746974038e-06,
+      "loss": 0.7947,
+      "step": 520
+    },
+    {
+      "epoch": 1.37,
+      "learning_rate": 6.622433668723412e-06,
+      "loss": 0.7693,
+      "step": 521
+    },
+    {
+      "epoch": 1.37,
+      "learning_rate": 6.6180871253160525e-06,
+      "loss": 0.7843,
+      "step": 522
+    },
+    {
+      "epoch": 1.38,
+      "learning_rate": 6.613717149416329e-06,
+      "loss": 0.7569,
+      "step": 523
+    },
+    {
+      "epoch": 1.38,
+      "learning_rate": 6.609323773864704e-06,
+      "loss": 0.7652,
+      "step": 524
+    },
+    {
+      "epoch": 1.38,
+      "learning_rate": 6.60490703167749e-06,
+      "loss": 0.7715,
+      "step": 525
+    },
+    {
+      "epoch": 1.39,
+      "learning_rate": 6.6004669560466e-06,
+      "loss": 0.778,
+      "step": 526
+    },
+    {
+      "epoch": 1.39,
+      "learning_rate": 6.596003580339301e-06,
+      "loss": 0.7888,
+      "step": 527
+    },
+    {
+      "epoch": 1.39,
+      "learning_rate": 6.5915169380979565e-06,
+      "loss": 0.7567,
+      "step": 528
+    },
+    {
+      "epoch": 1.39,
+      "learning_rate": 6.58700706303978e-06,
+      "loss": 0.768,
+      "step": 529
+    },
+    {
+      "epoch": 1.4,
+      "learning_rate": 6.58247398905658e-06,
+      "loss": 0.7729,
+      "step": 530
+    },
+    {
+      "epoch": 1.4,
+      "learning_rate": 6.577917750214508e-06,
+      "loss": 0.767,
+      "step": 531
+    },
+    {
+      "epoch": 1.4,
+      "learning_rate": 6.573338380753795e-06,
+      "loss": 0.7601,
+      "step": 532
+    },
+    {
+      "epoch": 1.4,
+      "learning_rate": 6.568735915088504e-06,
+      "loss": 0.7714,
+      "step": 533
+    },
+    {
+      "epoch": 1.41,
+      "learning_rate": 6.564110387806263e-06,
+      "loss": 0.7579,
+      "step": 534
+    },
+    {
+      "epoch": 1.41,
+      "learning_rate": 6.5594618336680095e-06,
+      "loss": 0.7693,
+      "step": 535
+    },
+    {
+      "epoch": 1.41,
+      "learning_rate": 6.55479028760773e-06,
+      "loss": 0.7818,
+      "step": 536
+    },
+    {
+      "epoch": 1.41,
+      "learning_rate": 6.550095784732193e-06,
+      "loss": 0.7641,
+      "step": 537
+    },
+    {
+      "epoch": 1.42,
+      "learning_rate": 6.54537836032069e-06,
+      "loss": 0.7658,
+      "step": 538
+    },
+    {
+      "epoch": 1.42,
+      "learning_rate": 6.5406380498247695e-06,
+      "loss": 0.7655,
+      "step": 539
+    },
+    {
+      "epoch": 1.42,
+      "learning_rate": 6.535874888867964e-06,
+      "loss": 0.7664,
+      "step": 540
+    },
+    {
+      "epoch": 1.43,
+      "learning_rate": 6.531088913245536e-06,
+      "loss": 0.7706,
+      "step": 541
+    },
+    {
+      "epoch": 1.43,
+      "learning_rate": 6.526280158924192e-06,
+      "loss": 0.771,
+      "step": 542
+    },
+    {
+      "epoch": 1.43,
+      "learning_rate": 6.521448662041825e-06,
+      "loss": 0.7582,
+      "step": 543
+    },
+    {
+      "epoch": 1.43,
+      "learning_rate": 6.516594458907243e-06,
+      "loss": 0.7743,
+      "step": 544
+    },
+    {
+      "epoch": 1.44,
+      "learning_rate": 6.511717585999884e-06,
+      "loss": 0.7657,
+      "step": 545
+    },
+    {
+      "epoch": 1.44,
+      "learning_rate": 6.5068180799695556e-06,
+      "loss": 0.7699,
+      "step": 546
+    },
+    {
+      "epoch": 1.44,
+      "learning_rate": 6.501895977636151e-06,
+      "loss": 0.7675,
+      "step": 547
+    },
+    {
+      "epoch": 1.44,
+      "learning_rate": 6.4969513159893755e-06,
+      "loss": 0.7887,
+      "step": 548
+    },
+    {
+      "epoch": 1.45,
+      "learning_rate": 6.49198413218847e-06,
+      "loss": 0.7647,
+      "step": 549
+    },
+    {
+      "epoch": 1.45,
+      "learning_rate": 6.4869944635619266e-06,
+      "loss": 0.7672,
+      "step": 550
+    },
+    {
+      "epoch": 1.45,
+      "learning_rate": 6.4819823476072154e-06,
+      "loss": 0.7506,
+      "step": 551
+    },
+    {
+      "epoch": 1.45,
+      "learning_rate": 6.476947821990495e-06,
+      "loss": 0.7772,
+      "step": 552
+    },
+    {
+      "epoch": 1.46,
+      "learning_rate": 6.471890924546335e-06,
+      "loss": 0.7484,
+      "step": 553
+    },
+    {
+      "epoch": 1.46,
+      "learning_rate": 6.466811693277429e-06,
+      "loss": 0.7668,
+      "step": 554
+    },
+    {
+      "epoch": 1.46,
+      "learning_rate": 6.4617101663543115e-06,
+      "loss": 0.7762,
+      "step": 555
+    },
+    {
+      "epoch": 1.47,
+      "learning_rate": 6.456586382115066e-06,
+      "loss": 0.7797,
+      "step": 556
+    },
+    {
+      "epoch": 1.47,
+      "learning_rate": 6.4514403790650435e-06,
+      "loss": 0.7401,
+      "step": 557
+    },
+    {
+      "epoch": 1.47,
+      "learning_rate": 6.4462721958765695e-06,
+      "loss": 0.7589,
+      "step": 558
+    },
+    {
+      "epoch": 1.47,
+      "learning_rate": 6.441081871388649e-06,
+      "loss": 0.7648,
+      "step": 559
+    },
+    {
+      "epoch": 1.48,
+      "learning_rate": 6.435869444606687e-06,
+      "loss": 0.7714,
+      "step": 560
+    },
+    {
+      "epoch": 1.48,
+      "learning_rate": 6.430634954702182e-06,
+      "loss": 0.7544,
+      "step": 561
+    },
+    {
+      "epoch": 1.48,
+      "learning_rate": 6.42537844101244e-06,
+      "loss": 0.7706,
+      "step": 562
+    },
+    {
+      "epoch": 1.48,
+      "learning_rate": 6.420099943040274e-06,
+      "loss": 0.7833,
+      "step": 563
+    },
+    {
+      "epoch": 1.49,
+      "learning_rate": 6.414799500453712e-06,
+      "loss": 0.7769,
+      "step": 564
+    },
+    {
+      "epoch": 1.49,
+      "learning_rate": 6.409477153085693e-06,
+      "loss": 0.7537,
+      "step": 565
+    },
+    {
+      "epoch": 1.49,
+      "learning_rate": 6.404132940933776e-06,
+      "loss": 0.773,
+      "step": 566
+    },
+    {
+      "epoch": 1.49,
+      "learning_rate": 6.398766904159829e-06,
+      "loss": 0.7794,
+      "step": 567
+    },
+    {
+      "epoch": 1.5,
+      "learning_rate": 6.393379083089736e-06,
+      "loss": 0.7687,
+      "step": 568
+    },
+    {
+      "epoch": 1.5,
+      "learning_rate": 6.387969518213087e-06,
+      "loss": 0.7838,
+      "step": 569
+    },
+    {
+      "epoch": 1.5,
+      "learning_rate": 6.382538250182883e-06,
+      "loss": 0.7789,
+      "step": 570
+    },
+    {
+      "epoch": 1.51,
+      "learning_rate": 6.377085319815218e-06,
+      "loss": 0.7616,
+      "step": 571
+    },
+    {
+      "epoch": 1.51,
+      "learning_rate": 6.371610768088981e-06,
+      "loss": 0.772,
+      "step": 572
+    },
+    {
+      "epoch": 1.51,
+      "learning_rate": 6.366114636145552e-06,
+      "loss": 0.7507,
+      "step": 573
+    },
+    {
+      "epoch": 1.51,
+      "learning_rate": 6.360596965288476e-06,
+      "loss": 0.7573,
+      "step": 574
+    },
+    {
+      "epoch": 1.52,
+      "learning_rate": 6.3550577969831725e-06,
+      "loss": 0.7706,
+      "step": 575
+    },
+    {
+      "epoch": 1.52,
+      "learning_rate": 6.3494971728566115e-06,
+      "loss": 0.7783,
+      "step": 576
+    },
+    {
+      "epoch": 1.52,
+      "learning_rate": 6.343915134697002e-06,
+      "loss": 0.7667,
+      "step": 577
+    },
+    {
+      "epoch": 1.52,
+      "learning_rate": 6.338311724453484e-06,
+      "loss": 0.7681,
+      "step": 578
+    },
+    {
+      "epoch": 1.53,
+      "learning_rate": 6.332686984235804e-06,
+      "loss": 0.7756,
+      "step": 579
+    },
+    {
+      "epoch": 1.53,
+      "learning_rate": 6.327040956314011e-06,
+      "loss": 0.7616,
+      "step": 580
+    },
+    {
+      "epoch": 1.53,
+      "learning_rate": 6.321373683118123e-06,
+      "loss": 0.7762,
+      "step": 581
+    },
+    {
+      "epoch": 1.53,
+      "learning_rate": 6.315685207237823e-06,
+      "loss": 0.773,
+      "step": 582
+    },
+    {
+      "epoch": 1.54,
+      "learning_rate": 6.309975571422131e-06,
+      "loss": 0.7471,
+      "step": 583
+    },
+    {
+      "epoch": 1.54,
+      "learning_rate": 6.304244818579083e-06,
+      "loss": 0.7537,
+      "step": 584
+    },
+    {
+      "epoch": 1.54,
+      "learning_rate": 6.298492991775411e-06,
+      "loss": 0.7712,
+      "step": 585
+    },
+    {
+      "epoch": 1.55,
+      "learning_rate": 6.292720134236218e-06,
+      "loss": 0.7735,
+      "step": 586
+    },
+    {
+      "epoch": 1.55,
+      "learning_rate": 6.286926289344656e-06,
+      "loss": 0.7456,
+      "step": 587
+    },
+    {
+      "epoch": 1.55,
+      "learning_rate": 6.2811115006415916e-06,
+      "loss": 0.7729,
+      "step": 588
+    },
+    {
+      "epoch": 1.55,
+      "learning_rate": 6.275275811825288e-06,
+      "loss": 0.7844,
+      "step": 589
+    },
+    {
+      "epoch": 1.56,
+      "learning_rate": 6.269419266751077e-06,
+      "loss": 0.7748,
+      "step": 590
+    },
+    {
+      "epoch": 1.56,
+      "learning_rate": 6.263541909431018e-06,
+      "loss": 0.7601,
+      "step": 591
+    },
+    {
+      "epoch": 1.56,
+      "learning_rate": 6.257643784033581e-06,
+      "loss": 0.7783,
+      "step": 592
+    },
+    {
+      "epoch": 1.56,
+      "learning_rate": 6.251724934883305e-06,
+      "loss": 0.7934,
+      "step": 593
+    },
+    {
+      "epoch": 1.57,
+      "learning_rate": 6.245785406460471e-06,
+      "loss": 0.7638,
+      "step": 594
+    },
+    {
+      "epoch": 1.57,
+      "learning_rate": 6.239825243400762e-06,
+      "loss": 0.7636,
+      "step": 595
+    },
+    {
+      "epoch": 1.57,
+      "learning_rate": 6.233844490494934e-06,
+      "loss": 0.764,
+      "step": 596
+    },
+    {
+      "epoch": 1.57,
+      "learning_rate": 6.227843192688475e-06,
+      "loss": 0.7677,
+      "step": 597
+    },
+    {
+      "epoch": 1.58,
+      "learning_rate": 6.221821395081267e-06,
+      "loss": 0.7657,
+      "step": 598
+    },
+    {
+      "epoch": 1.58,
+      "learning_rate": 6.21577914292725e-06,
+      "loss": 0.7745,
+      "step": 599
+    },
+    {
+      "epoch": 1.58,
+      "learning_rate": 6.209716481634081e-06,
+      "loss": 0.7658,
+      "step": 600
+    },
+    {
+      "epoch": 1.59,
+      "learning_rate": 6.20363345676279e-06,
+      "loss": 0.7726,
+      "step": 601
+    },
+    {
+      "epoch": 1.59,
+      "learning_rate": 6.1975301140274415e-06,
+      "loss": 0.7627,
+      "step": 602
+    },
+    {
+      "epoch": 1.59,
+      "learning_rate": 6.1914064992947875e-06,
+      "loss": 0.7601,
+      "step": 603
+    },
+    {
+      "epoch": 1.59,
+      "learning_rate": 6.1852626585839265e-06,
+      "loss": 0.758,
+      "step": 604
+    },
+    {
+      "epoch": 1.6,
+      "learning_rate": 6.179098638065954e-06,
+      "loss": 0.7636,
+      "step": 605
+    },
+    {
+      "epoch": 1.6,
+      "learning_rate": 6.172914484063619e-06,
+      "loss": 0.7678,
+      "step": 606
+    },
+    {
+      "epoch": 1.6,
+      "learning_rate": 6.166710243050972e-06,
+      "loss": 0.7489,
+      "step": 607
+    },
+    {
+      "epoch": 1.6,
+      "learning_rate": 6.160485961653018e-06,
+      "loss": 0.7629,
+      "step": 608
+    },
+    {
+      "epoch": 1.61,
+      "learning_rate": 6.154241686645368e-06,
+      "loss": 0.7684,
+      "step": 609
+    },
+    {
+      "epoch": 1.61,
+      "learning_rate": 6.147977464953884e-06,
+      "loss": 0.7567,
+      "step": 610
+    },
+    {
+      "epoch": 1.61,
+      "learning_rate": 6.141693343654327e-06,
+      "loss": 0.7699,
+      "step": 611
+    },
+    {
+      "epoch": 1.61,
+      "learning_rate": 6.135389369972006e-06,
+      "loss": 0.7728,
+      "step": 612
+    },
+    {
+      "epoch": 1.62,
+      "learning_rate": 6.129065591281419e-06,
+      "loss": 0.7455,
+      "step": 613
+    },
+    {
+      "epoch": 1.62,
+      "learning_rate": 6.1227220551059e-06,
+      "loss": 0.7448,
+      "step": 614
+    },
+    {
+      "epoch": 1.62,
+      "learning_rate": 6.116358809117262e-06,
+      "loss": 0.7585,
+      "step": 615
+    },
+    {
+      "epoch": 1.63,
+      "learning_rate": 6.109975901135435e-06,
+      "loss": 0.7339,
+      "step": 616
+    },
+    {
+      "epoch": 1.63,
+      "learning_rate": 6.1035733791281125e-06,
+      "loss": 0.7715,
+      "step": 617
+    },
+    {
+      "epoch": 1.63,
+      "learning_rate": 6.097151291210385e-06,
+      "loss": 0.7648,
+      "step": 618
+    },
+    {
+      "epoch": 1.63,
+      "learning_rate": 6.090709685644382e-06,
+      "loss": 0.7566,
+      "step": 619
+    },
+    {
+      "epoch": 1.64,
+      "learning_rate": 6.084248610838911e-06,
+      "loss": 0.753,
+      "step": 620
+    },
+    {
+      "epoch": 1.64,
+      "learning_rate": 6.077768115349088e-06,
+      "loss": 0.7709,
+      "step": 621
+    },
+    {
+      "epoch": 1.64,
+      "learning_rate": 6.071268247875976e-06,
+      "loss": 0.756,
+      "step": 622
+    },
+    {
+      "epoch": 1.64,
+      "learning_rate": 6.064749057266223e-06,
+      "loss": 0.7812,
+      "step": 623
+    },
+    {
+      "epoch": 1.65,
+      "learning_rate": 6.058210592511687e-06,
+      "loss": 0.7728,
+      "step": 624
+    },
+    {
+      "epoch": 1.65,
+      "learning_rate": 6.05165290274907e-06,
+      "loss": 0.7775,
+      "step": 625
+    },
+    {
+      "epoch": 1.65,
+      "learning_rate": 6.045076037259554e-06,
+      "loss": 0.7655,
+      "step": 626
+    },
+    {
+      "epoch": 1.65,
+      "learning_rate": 6.038480045468426e-06,
+      "loss": 0.7668,
+      "step": 627
+    },
+    {
+      "epoch": 1.66,
+      "learning_rate": 6.03186497694471e-06,
+      "loss": 0.7593,
+      "step": 628
+    },
+    {
+      "epoch": 1.66,
+      "learning_rate": 6.025230881400785e-06,
+      "loss": 0.7666,
+      "step": 629
+    },
+    {
+      "epoch": 1.66,
+      "learning_rate": 6.0185778086920245e-06,
+      "loss": 0.7841,
+      "step": 630
+    },
+    {
+      "epoch": 1.67,
+      "learning_rate": 6.011905808816416e-06,
+      "loss": 0.7709,
+      "step": 631
+    },
+    {
+      "epoch": 1.67,
+      "learning_rate": 6.0052149319141795e-06,
+      "loss": 0.7763,
+      "step": 632
+    },
+    {
+      "epoch": 1.67,
+      "learning_rate": 5.998505228267403e-06,
+      "loss": 0.7759,
+      "step": 633
+    },
+    {
+      "epoch": 1.67,
+      "learning_rate": 5.991776748299656e-06,
+      "loss": 0.7607,
+      "step": 634
+    },
+    {
+      "epoch": 1.68,
+      "learning_rate": 5.985029542575608e-06,
+      "loss": 0.7639,
+      "step": 635
+    },
+    {
+      "epoch": 1.68,
+      "learning_rate": 5.978263661800657e-06,
+      "loss": 0.7745,
+      "step": 636
+    },
+    {
+      "epoch": 1.68,
+      "learning_rate": 5.971479156820547e-06,
+      "loss": 0.7635,
+      "step": 637
+    },
+    {
+      "epoch": 1.68,
+      "learning_rate": 5.964676078620975e-06,
+      "loss": 0.7504,
+      "step": 638
+    },
+    {
+      "epoch": 1.69,
+      "learning_rate": 5.957854478327225e-06,
+      "loss": 0.7548,
+      "step": 639
+    },
+    {
+      "epoch": 1.69,
+      "learning_rate": 5.95101440720377e-06,
+      "loss": 0.7677,
+      "step": 640
+    },
+    {
+      "epoch": 1.69,
+      "learning_rate": 5.944155916653893e-06,
+      "loss": 0.7543,
+      "step": 641
+    },
+    {
+      "epoch": 1.7,
+      "learning_rate": 5.9372790582192985e-06,
+      "loss": 0.7651,
+      "step": 642
+    },
+    {
+      "epoch": 1.7,
+      "learning_rate": 5.930383883579727e-06,
+      "loss": 0.7578,
+      "step": 643
+    },
+    {
+      "epoch": 1.7,
+      "learning_rate": 5.923470444552567e-06,
+      "loss": 0.7588,
+      "step": 644
+    },
+    {
+      "epoch": 1.7,
+      "learning_rate": 5.91653879309246e-06,
+      "loss": 0.755,
+      "step": 645
+    },
+    {
+      "epoch": 1.71,
+      "learning_rate": 5.909588981290919e-06,
+      "loss": 0.7546,
+      "step": 646
+    },
+    {
+      "epoch": 1.71,
+      "learning_rate": 5.90262106137593e-06,
+      "loss": 0.752,
+      "step": 647
+    },
+    {
+      "epoch": 1.71,
+      "learning_rate": 5.895635085711562e-06,
+      "loss": 0.7806,
+      "step": 648
+    },
+    {
+      "epoch": 1.71,
+      "learning_rate": 5.8886311067975745e-06,
+      "loss": 0.7765,
+      "step": 649
+    },
+    {
+      "epoch": 1.72,
+      "learning_rate": 5.8816091772690205e-06,
+      "loss": 0.7637,
+      "step": 650
+    },
+    {
+      "epoch": 1.72,
+      "learning_rate": 5.87456934989585e-06,
+      "loss": 0.7792,
+      "step": 651
+    },
+    {
+      "epoch": 1.72,
+      "learning_rate": 5.867511677582522e-06,
+      "loss": 0.7492,
+      "step": 652
+    },
+    {
+      "epoch": 1.72,
+      "learning_rate": 5.860436213367597e-06,
+      "loss": 0.7509,
+      "step": 653
+    },
+    {
+      "epoch": 1.73,
+      "learning_rate": 5.85334301042334e-06,
+      "loss": 0.7514,
+      "step": 654
+    },
+    {
+      "epoch": 1.73,
+      "learning_rate": 5.846232122055325e-06,
+      "loss": 0.7695,
+      "step": 655
+    },
+    {
+      "epoch": 1.73,
+      "learning_rate": 5.839103601702033e-06,
+      "loss": 0.7693,
+      "step": 656
+    },
+    {
+      "epoch": 1.74,
+      "learning_rate": 5.83195750293445e-06,
+      "loss": 0.7466,
+      "step": 657
+    },
+    {
+      "epoch": 1.74,
+      "learning_rate": 5.824793879455662e-06,
+      "loss": 0.7652,
+      "step": 658
+    },
+    {
+      "epoch": 1.74,
+      "learning_rate": 5.817612785100454e-06,
+      "loss": 0.7558,
+      "step": 659
+    },
+    {
+      "epoch": 1.74,
+      "learning_rate": 5.8104142738349076e-06,
+      "loss": 0.7638,
+      "step": 660
+    },
+    {
+      "epoch": 1.75,
+      "learning_rate": 5.803198399755989e-06,
+      "loss": 0.7717,
+      "step": 661
+    },
+    {
+      "epoch": 1.75,
+      "learning_rate": 5.795965217091152e-06,
+      "loss": 0.7551,
+      "step": 662
+    },
+    {
+      "epoch": 1.75,
+      "learning_rate": 5.788714780197918e-06,
+      "loss": 0.7692,
+      "step": 663
+    },
+    {
+      "epoch": 1.75,
+      "learning_rate": 5.7814471435634775e-06,
+      "loss": 0.7454,
+      "step": 664
+    },
+    {
+      "epoch": 1.76,
+      "learning_rate": 5.774162361804279e-06,
+      "loss": 0.7591,
+      "step": 665
+    },
+    {
+      "epoch": 1.76,
+      "learning_rate": 5.7668604896656145e-06,
+      "loss": 0.7511,
+      "step": 666
+    },
+    {
+      "epoch": 1.76,
+      "learning_rate": 5.7595415820212105e-06,
+      "loss": 0.757,
+      "step": 667
+    },
+    {
+      "epoch": 1.76,
+      "learning_rate": 5.752205693872819e-06,
+      "loss": 0.7593,
+      "step": 668
+    },
+    {
+      "epoch": 1.77,
+      "learning_rate": 5.7448528803497975e-06,
+      "loss": 0.7667,
+      "step": 669
+    },
+    {
+      "epoch": 1.77,
+      "learning_rate": 5.737483196708698e-06,
+      "loss": 0.766,
+      "step": 670
+    },
+    {
+      "epoch": 1.77,
+      "learning_rate": 5.730096698332854e-06,
+      "loss": 0.7675,
+      "step": 671
+    },
+    {
+      "epoch": 1.78,
+      "learning_rate": 5.722693440731961e-06,
+      "loss": 0.7549,
+      "step": 672
+    },
+    {
+      "epoch": 1.78,
+      "learning_rate": 5.715273479541661e-06,
+      "loss": 0.7377,
+      "step": 673
+    },
+    {
+      "epoch": 1.78,
+      "learning_rate": 5.707836870523121e-06,
+      "loss": 0.748,
+      "step": 674
+    },
+    {
+      "epoch": 1.78,
+      "learning_rate": 5.700383669562621e-06,
+      "loss": 0.7765,
+      "step": 675
+    },
+    {
+      "epoch": 1.79,
+      "learning_rate": 5.692913932671127e-06,
+      "loss": 0.7583,
+      "step": 676
+    },
+    {
+      "epoch": 1.79,
+      "learning_rate": 5.685427715983874e-06,
+      "loss": 0.7747,
+      "step": 677
+    },
+    {
+      "epoch": 1.79,
+      "learning_rate": 5.677925075759944e-06,
+      "loss": 0.7733,
+      "step": 678
+    },
+    {
+      "epoch": 1.79,
+      "learning_rate": 5.670406068381839e-06,
+      "loss": 0.7632,
+      "step": 679
+    },
+    {
+      "epoch": 1.8,
+      "learning_rate": 5.662870750355062e-06,
+      "loss": 0.7824,
+      "step": 680
+    },
+    {
+      "epoch": 1.8,
+      "learning_rate": 5.655319178307693e-06,
+      "loss": 0.7623,
+      "step": 681
+    },
+    {
+      "epoch": 1.8,
+      "learning_rate": 5.647751408989961e-06,
+      "loss": 0.7601,
+      "step": 682
+    },
+    {
+      "epoch": 1.8,
+      "learning_rate": 5.640167499273813e-06,
+      "loss": 0.7556,
+      "step": 683
+    },
+    {
+      "epoch": 1.81,
+      "learning_rate": 5.632567506152498e-06,
+      "loss": 0.7711,
+      "step": 684
+    },
+    {
+      "epoch": 1.81,
+      "learning_rate": 5.624951486740128e-06,
+      "loss": 0.7567,
+      "step": 685
+    },
+    {
+      "epoch": 1.81,
+      "learning_rate": 5.617319498271254e-06,
+      "loss": 0.7585,
+      "step": 686
+    },
+    {
+      "epoch": 1.82,
+      "learning_rate": 5.609671598100434e-06,
+      "loss": 0.7467,
+      "step": 687
+    },
+    {
+      "epoch": 1.82,
+      "learning_rate": 5.6020078437018046e-06,
+      "loss": 0.7504,
+      "step": 688
+    },
+    {
+      "epoch": 1.82,
+      "learning_rate": 5.594328292668647e-06,
+      "loss": 0.7736,
+      "step": 689
+    },
+    {
+      "epoch": 1.82,
+      "learning_rate": 5.586633002712952e-06,
+      "loss": 0.7626,
+      "step": 690
+    },
+    {
+      "epoch": 1.83,
+      "learning_rate": 5.578922031664992e-06,
+      "loss": 0.7627,
+      "step": 691
+    },
+    {
+      "epoch": 1.83,
+      "learning_rate": 5.571195437472881e-06,
+      "loss": 0.7522,
+      "step": 692
+    },
+    {
+      "epoch": 1.83,
+      "learning_rate": 5.563453278202141e-06,
+      "loss": 0.7572,
+      "step": 693
+    },
+    {
+      "epoch": 1.83,
+      "learning_rate": 5.555695612035269e-06,
+      "loss": 0.7501,
+      "step": 694
+    },
+    {
+      "epoch": 1.84,
+      "learning_rate": 5.547922497271293e-06,
+      "loss": 0.7641,
+      "step": 695
+    },
+    {
+      "epoch": 1.84,
+      "learning_rate": 5.54013399232534e-06,
+      "loss": 0.769,
+      "step": 696
+    },
+    {
+      "epoch": 1.84,
+      "learning_rate": 5.532330155728192e-06,
+      "loss": 0.7741,
+      "step": 697
+    },
+    {
+      "epoch": 1.84,
+      "learning_rate": 5.524511046125852e-06,
+      "loss": 0.7799,
+      "step": 698
+    },
+    {
+      "epoch": 1.85,
+      "learning_rate": 5.516676722279097e-06,
+      "loss": 0.7538,
+      "step": 699
+    },
+    {
+      "epoch": 1.85,
+      "learning_rate": 5.50882724306304e-06,
+      "loss": 0.769,
+      "step": 700
+    },
+    {
+      "epoch": 1.85,
+      "learning_rate": 5.500962667466689e-06,
+      "loss": 0.7714,
+      "step": 701
+    },
+    {
+      "epoch": 1.86,
+      "learning_rate": 5.4930830545925e-06,
+      "loss": 0.7627,
+      "step": 702
+    },
+    {
+      "epoch": 1.86,
+      "learning_rate": 5.485188463655933e-06,
+      "loss": 0.7669,
+      "step": 703
+    },
+    {
+      "epoch": 1.86,
+      "learning_rate": 5.477278953985013e-06,
+      "loss": 0.7449,
+      "step": 704
+    },
+    {
+      "epoch": 1.86,
+      "learning_rate": 5.469354585019874e-06,
+      "loss": 0.7427,
+      "step": 705
+    },
+    {
+      "epoch": 1.87,
+      "learning_rate": 5.461415416312322e-06,
+      "loss": 0.7405,
+      "step": 706
+    },
+    {
+      "epoch": 1.87,
+      "learning_rate": 5.453461507525382e-06,
+      "loss": 0.7655,
+      "step": 707
+    },
+    {
+      "epoch": 1.87,
+      "learning_rate": 5.4454929184328505e-06,
+      "loss": 0.7592,
+      "step": 708
+    },
+    {
+      "epoch": 1.87,
+      "learning_rate": 5.437509708918847e-06,
+      "loss": 0.7678,
+      "step": 709
+    },
+    {
+      "epoch": 1.88,
+      "learning_rate": 5.429511938977365e-06,
+      "loss": 0.7652,
+      "step": 710
+    },
+    {
+      "epoch": 1.88,
+      "learning_rate": 5.421499668711819e-06,
+      "loss": 0.7534,
+      "step": 711
+    },
+    {
+      "epoch": 1.88,
+      "learning_rate": 5.4134729583345936e-06,
+      "loss": 0.7434,
+      "step": 712
+    },
+    {
+      "epoch": 1.88,
+      "learning_rate": 5.405431868166591e-06,
+      "loss": 0.7676,
+      "step": 713
+    },
+    {
+      "epoch": 1.89,
+      "learning_rate": 5.3973764586367775e-06,
+      "loss": 0.7609,
+      "step": 714
+    },
+    {
+      "epoch": 1.89,
+      "learning_rate": 5.389306790281732e-06,
+      "loss": 0.776,
+      "step": 715
+    },
+    {
+      "epoch": 1.89,
+      "learning_rate": 5.381222923745186e-06,
+      "loss": 0.7525,
+      "step": 716
+    },
+    {
+      "epoch": 1.9,
+      "learning_rate": 5.3731249197775734e-06,
+      "loss": 0.7476,
+      "step": 717
+    },
+    {
+      "epoch": 1.9,
+      "learning_rate": 5.365012839235568e-06,
+      "loss": 0.7695,
+      "step": 718
+    },
+    {
+      "epoch": 1.9,
+      "learning_rate": 5.356886743081632e-06,
+      "loss": 0.7484,
+      "step": 719
+    },
+    {
+      "epoch": 1.9,
+      "learning_rate": 5.348746692383553e-06,
+      "loss": 0.7758,
+      "step": 720
+    },
+    {
+      "epoch": 1.91,
+      "learning_rate": 5.34059274831399e-06,
+      "loss": 0.7764,
+      "step": 721
+    },
+    {
+      "epoch": 1.91,
+      "learning_rate": 5.33242497215001e-06,
+      "loss": 0.7557,
+      "step": 722
+    },
+    {
+      "epoch": 1.91,
+      "learning_rate": 5.324243425272624e-06,
+      "loss": 0.7578,
+      "step": 723
+    },
+    {
+      "epoch": 1.91,
+      "learning_rate": 5.316048169166339e-06,
+      "loss": 0.7841,
+      "step": 724
+    },
+    {
+      "epoch": 1.92,
+      "learning_rate": 5.30783926541868e-06,
+      "loss": 0.7584,
+      "step": 725
+    },
+    {
+      "epoch": 1.92,
+      "learning_rate": 5.299616775719737e-06,
+      "loss": 0.7628,
+      "step": 726
+    },
+    {
+      "epoch": 1.92,
+      "learning_rate": 5.2913807618616975e-06,
+      "loss": 0.7618,
+      "step": 727
+    },
+    {
+      "epoch": 1.92,
+      "learning_rate": 5.283131285738386e-06,
+      "loss": 0.7585,
+      "step": 728
+    },
+    {
+      "epoch": 1.93,
+      "learning_rate": 5.274868409344793e-06,
+      "loss": 0.7537,
+      "step": 729
+    },
+    {
+      "epoch": 1.93,
+      "learning_rate": 5.266592194776616e-06,
+      "loss": 0.7753,
+      "step": 730
+    },
+    {
+      "epoch": 1.93,
+      "learning_rate": 5.258302704229784e-06,
+      "loss": 0.7528,
+      "step": 731
+    },
+    {
+      "epoch": 1.94,
+      "learning_rate": 5.25e-06,
+      "loss": 0.7833,
+      "step": 732
+    },
+    {
+      "epoch": 1.94,
+      "learning_rate": 5.241684144482266e-06,
+      "loss": 0.7568,
+      "step": 733
+    },
+    {
+      "epoch": 1.94,
+      "learning_rate": 5.233355200170416e-06,
+      "loss": 0.7591,
+      "step": 734
+    },
+    {
+      "epoch": 1.94,
+      "learning_rate": 5.225013229656649e-06,
+      "loss": 0.7611,
+      "step": 735
+    },
+    {
+      "epoch": 1.95,
+      "learning_rate": 5.216658295631052e-06,
+      "loss": 0.7563,
+      "step": 736
+    },
+    {
+      "epoch": 1.95,
+      "learning_rate": 5.208290460881136e-06,
+      "loss": 0.7676,
+      "step": 737
+    },
+    {
+      "epoch": 1.95,
+      "learning_rate": 5.199909788291361e-06,
+      "loss": 0.7736,
+      "step": 738
+    },
+    {
+      "epoch": 1.95,
+      "learning_rate": 5.1915163408426645e-06,
+      "loss": 0.7544,
+      "step": 739
+    },
+    {
+      "epoch": 1.96,
+      "learning_rate": 5.183110181611983e-06,
+      "loss": 0.7441,
+      "step": 740
+    },
+    {
+      "epoch": 1.96,
+      "learning_rate": 5.174691373771788e-06,
+      "loss": 0.7579,
+      "step": 741
+    },
+    {
+      "epoch": 1.96,
+      "learning_rate": 5.166259980589603e-06,
+      "loss": 0.7773,
+      "step": 742
+    },
+    {
+      "epoch": 1.96,
+      "learning_rate": 5.157816065427529e-06,
+      "loss": 0.7492,
+      "step": 743
+    },
+    {
+      "epoch": 1.97,
+      "learning_rate": 5.149359691741772e-06,
+      "loss": 0.7707,
+      "step": 744
+    },
+    {
+      "epoch": 1.97,
+      "learning_rate": 5.1408909230821635e-06,
+      "loss": 0.7464,
+      "step": 745
+    },
+    {
+      "epoch": 1.97,
+      "learning_rate": 5.132409823091685e-06,
+      "loss": 0.7513,
+      "step": 746
+    },
+    {
+      "epoch": 1.98,
+      "learning_rate": 5.123916455505986e-06,
+      "loss": 0.7552,
+      "step": 747
+    },
+    {
+      "epoch": 1.98,
+      "learning_rate": 5.1154108841529075e-06,
+      "loss": 0.7706,
+      "step": 748
+    },
+    {
+      "epoch": 1.98,
+      "learning_rate": 5.106893172952004e-06,
+      "loss": 0.7581,
+      "step": 749
+    },
+    {
+      "epoch": 1.98,
+      "learning_rate": 5.098363385914059e-06,
+      "loss": 0.7519,
+      "step": 750
+    },
+    {
+      "epoch": 1.99,
+      "learning_rate": 5.089821587140607e-06,
+      "loss": 0.7698,
+      "step": 751
+    },
+    {
+      "epoch": 1.99,
+      "learning_rate": 5.081267840823451e-06,
+      "loss": 0.747,
+      "step": 752
+    },
+    {
+      "epoch": 1.99,
+      "learning_rate": 5.0727022112441795e-06,
+      "loss": 0.7374,
+      "step": 753
+    },
+    {
+      "epoch": 1.99,
+      "learning_rate": 5.064124762773687e-06,
+      "loss": 0.7555,
+      "step": 754
+    },
+    {
+      "epoch": 2.0,
+      "learning_rate": 5.055535559871682e-06,
+      "loss": 0.7688,
+      "step": 755
+    },
+    {
+      "epoch": 2.0,
+      "learning_rate": 5.046934667086212e-06,
+      "loss": 0.75,
+      "step": 756
+    },
+    {
+      "epoch": 2.0,
+      "learning_rate": 5.038322149053172e-06,
+      "loss": 0.7457,
+      "step": 757
+    },
+    {
+      "epoch": 2.0,
+      "learning_rate": 5.029698070495822e-06,
+      "loss": 0.7543,
+      "step": 758
+    },
+    {
+      "epoch": 2.01,
+      "learning_rate": 5.021062496224301e-06,
+      "loss": 0.7317,
+      "step": 759
+    },
+    {
+      "epoch": 2.01,
+      "learning_rate": 5.012415491135133e-06,
+      "loss": 0.7653,
+      "step": 760
+    },
+    {
+      "epoch": 2.01,
+      "learning_rate": 5.0037571202107525e-06,
+      "loss": 0.7702,
+      "step": 761
+    },
+    {
+      "epoch": 2.02,
+      "learning_rate": 4.995087448519005e-06,
+      "loss": 0.7647,
+      "step": 762
+    },
+    {
+      "epoch": 2.02,
+      "learning_rate": 4.986406541212659e-06,
+      "loss": 0.7484,
+      "step": 763
+    }
+  ],
+  "logging_steps": 1,
+  "max_steps": 1496,
+  "num_train_epochs": 4,
+  "save_steps": 500,
+  "total_flos": 1.2700841023456543e+19,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-763/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46e4ff946a52f7ad865e9875e81fb905b4052faefbb3e2ea02b382193a53532b
+size 4539

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "stabilityai/stablelm-3b-4e1t",
+  "architectures": [
+    "StableLMEpochForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig",
+    "AutoModelForCausalLM": "modeling_stablelm_epoch.StableLMEpochForCausalLM"
+  },
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "max_position_embeddings": 4096,
+  "model_type": "stablelm_epoch",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "rope_pct": 0.25,
+  "rope_theta": 10000,
+  "rotary_scaling_factor": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.1",
+  "use_cache": false,
+  "vocab_size": 50304
+}

configuration_stablelm_epoch.py

@@ -0,0 +1,110 @@
+# coding=utf-8
+# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" StableLM Epoch model configuration"""
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class StableLMEpochConfig(PretrainedConfig):
+    r"""
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50_304):
+            Vocabulary size of the StableLM model. Defines the number of different tokens that
+            can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
+        intermediate_size (`int`, *optional*, defaults to 6912):
+            Dimension of the MLP representations.
+        hidden_size (`int`, *optional*, defaults to 2560):
+            Dimension of the decoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string).
+        rope_pct (`float`, *optional*, defaults to 1.0):
+            Percentage of hidden dimensions to allocate to rotary embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 1e-5):
+            The standard deviation of the truncated_normal_initializer for initializing
+             all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-8):
+            The epsilon used by the normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions
+            (not used by all models). Only relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+    """
+    model_type = "stablelm_epoch"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50_304,
+        intermediate_size=6912,
+        hidden_size=2560,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        rope_pct=0.25,
+        rope_theta=10_000,
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        norm_eps=1.0e-5,
+        use_cache=True,
+        bos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.intermediate_size = intermediate_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.hidden_act = hidden_act
+        self.rope_pct = rope_pct
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.34.1"
+}

modeling_stablelm_epoch.py

@@ -0,0 +1,687 @@
+# coding=utf-8
+# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This code is based off the following work:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+""" PyTorch StableLM Epoch model. """
+from typing import Optional, Tuple, Union
+import math
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from .configuration_stablelm_epoch import StableLMEpochConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+):
+    """Make causal mask used for bi-directional self-attention."""
+    batch_size, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
+    batch_size, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_position_embeddings: int,
+        base: int = 10_000,
+        device: Optional[torch.device] = None,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
+        )
+
+    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+        # Don't do einsum, it converts fp32 to fp16 under AMP
+        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
+        # x: [batch_size, num_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x: torch.Tensor):
+    """Rotates half the hidden dims of the input."""
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MLP(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.act_fn = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Attention(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+
+        self._init_rope()
+
+    def _init_rope(self):
+        self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
+        self.rotary_emb = RotaryEmbedding(
+            self.rotary_ndims,
+            max_position_embeddings=self.config.max_position_embeddings,
+            base=self.config.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        query_rot = query_states[..., : self.rotary_ndims]
+        query_pass = query_states[..., self.rotary_ndims :]
+        key_rot = key_states[..., : self.rotary_ndims]
+        key_pass = key_states[..., self.rotary_ndims :]
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # [batch_size, num_heads, seq_len, head_dim]
+        query_states = torch.cat((query_states, query_pass), dim=-1)
+        key_states = torch.cat((key_states, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            # Reuse k, v, self_attention
+            key_states = torch.cat((past_key_value[0], key_states), dim=2)
+            value_states = torch.cat((past_key_value[1], value_states), dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # Upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        # Merge heads
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        # Final linear projection
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__()
+        self.self_attn = Attention(config)
+        self.mlp = MLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.FloatTensor],
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class StableLMEpochPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and a simple interface
+    for downloading and loading pretrained models.
+    """
+
+    config_class = StableLMEpochConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
+        if isinstance(module, StableLMEpochModel):
+            module.gradient_checkpointing = value
+
+
+class StableLMEpochModel(StableLMEpochPreTrainedModel):
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
+        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_shape: torch.Size,
+        inputs_embeds: torch.Tensor,
+        past_key_values_length: int,
+    ):
+        # Create causal mask
+        # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # Embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # Decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # Add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: StableLMEpochConfig):
+        super().__init__(config)
+
+        self.model = StableLMEpochModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        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 set_output_embeddings(self, new_embeddings: nn.Module):
+        self.lm_head = new_embeddings
+
+    def get_decoder(self):
+        return self.model
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states).float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        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=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        # Trim decoder_input_ids if past is used
+        if past_key_values and past_key_values[0] is not None:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # Create position_ids on the fly for batch generation
+            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)
+
+        # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+StableLMEpochConfig.register_for_auto_class()
+StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50626311715a9622d3ed0e8f2cb9cee829d1272bd686da8674539e0c78fc0b1a
+size 6106785781

special_tokens_map.json

@@ -0,0 +1,18 @@
+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

@@ -0,0 +1,236 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|padding|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50254": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50255": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "                  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50261": {
+      "content": "                 ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50262": {
+      "content": "                ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50263": {
+      "content": "               ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50264": {
+      "content": "              ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50265": {
+      "content": "             ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50266": {
+      "content": "            ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50267": {
+      "content": "           ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50268": {
+      "content": "          ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50269": {
+      "content": "         ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50270": {
+      "content": "        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50271": {
+      "content": "       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50272": {
+      "content": "      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50273": {
+      "content": "     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50274": {
+      "content": "    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50275": {
+      "content": "   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50276": {
+      "content": "  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50277": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50278": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50279": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|im_end|>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "unk_token": "<|endoftext|>"
+}