End of training

README.md

@@ -0,0 +1,81 @@
+---
+library_name: transformers
+license: mit
+base_model: chandar-lab/NeoBERT
+tags:
+- generated_from_trainer
+metrics:
+- f1
+model-index:
+- name: NeoBERT-multiclass-classifier-ICLR
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# NeoBERT-multiclass-classifier-ICLR
+
+This model is a fine-tuned version of [chandar-lab/NeoBERT](https://huggingface.co/chandar-lab/NeoBERT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7258
+- F1: 0.5134
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0005
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 20
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| No log        | 1.0   | 45   | 1.2609          | 0.4641 |
+| No log        | 2.0   | 90   | 1.3167          | 0.4641 |
+| 1.2441        | 3.0   | 135  | 1.1799          | 0.5405 |
+| 1.2441        | 4.0   | 180  | 1.2810          | 0.5386 |
+| 1.0526        | 5.0   | 225  | 1.2742          | 0.5098 |
+| 1.0526        | 6.0   | 270  | 1.4929          | 0.5030 |
+| 0.7789        | 7.0   | 315  | 1.5076          | 0.5425 |
+| 0.7789        | 8.0   | 360  | 1.6513          | 0.4908 |
+| 0.5299        | 9.0   | 405  | 1.6172          | 0.5476 |
+| 0.5299        | 10.0  | 450  | 1.7358          | 0.5389 |
+| 0.5299        | 11.0  | 495  | 1.8935          | 0.4847 |
+| 0.4185        | 12.0  | 540  | 1.8012          | 0.5152 |
+| 0.4185        | 13.0  | 585  | 1.7241          | 0.5337 |
+| 0.3614        | 14.0  | 630  | 1.7109          | 0.5257 |
+| 0.3614        | 15.0  | 675  | 1.7233          | 0.5024 |
+| 0.3527        | 16.0  | 720  | 1.7104          | 0.5147 |
+| 0.3527        | 17.0  | 765  | 1.7282          | 0.5134 |
+| 0.3513        | 18.0  | 810  | 1.7257          | 0.5134 |
+| 0.3513        | 19.0  | 855  | 1.7263          | 0.5134 |
+| 0.3511        | 20.0  | 900  | 1.7258          | 0.5134 |
+
+
+### Framework versions
+
+- Transformers 4.53.0
+- Pytorch 2.7.1+cu126
+- Datasets 3.6.0
+- Tokenizers 0.21.2

config.json

@@ -0,0 +1,64 @@
+{
+  "architectures": [
+    "NeoBERTForSequenceClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.NeoBERTConfig",
+    "AutoModel": "model.NeoBERT",
+    "AutoModelForMaskedLM": "model.NeoBERTLMHead",
+    "AutoModelForSequenceClassification": "model.NeoBERTForSequenceClassification"
+  },
+  "classifier_init_range": 0.02,
+  "decoder_init_range": 0.02,
+  "dim_head": 64,
+  "embedding_init_range": 0.02,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Conceptual Integration",
+    "1": "Cross-Domain Application",
+    "2": "Direct Enhancement",
+    "3": "Other"
+  },
+  "intermediate_size": 3072,
+  "kwargs": {
+    "architectures": [
+      "NeoBERTLMHead"
+    ],
+    "attn_implementation": null,
+    "auto_map": {
+      "AutoConfig": "model.NeoBERTConfig",
+      "AutoModel": "model.NeoBERT",
+      "AutoModelForMaskedLM": "model.NeoBERTLMHead",
+      "AutoModelForSequenceClassification": "model.NeoBERTForSequenceClassification"
+    },
+    "classifier_init_range": 0.02,
+    "dim_head": 64,
+    "kwargs": {
+      "classifier_init_range": 0.02,
+      "pretrained_model_name_or_path": "google-bert/bert-base-uncased",
+      "trust_remote_code": true
+    },
+    "model_type": "neobert",
+    "pretrained_model_name_or_path": "google-bert/bert-base-uncased",
+    "torch_dtype": "float32",
+    "transformers_version": "4.48.2",
+    "trust_remote_code": true
+  },
+  "label2id": {
+    "Conceptual Integration": "0",
+    "Cross-Domain Application": "1",
+    "Direct Enhancement": "2",
+    "Other": "3"
+  },
+  "max_length": 4096,
+  "model_type": "neobert",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "pad_token_id": 0,
+  "pretrained_model_name_or_path": "google-bert/bert-base-uncased",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.0",
+  "trust_remote_code": true,
+  "vocab_size": 30522
+}

model.py

@@ -0,0 +1,434 @@
+# From https://github.com/facebookresearch/llama/blob/main/llama/model.py
+
+import torch
+from torch import nn
+
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.nn.functional import scaled_dot_product_attention
+
+from typing import Optional, Tuple
+import numpy as np
+
+from xformers.ops import SwiGLU
+
+try:
+    from flash_attn.flash_attn_interface import flash_attn_varlen_func
+
+    FLASH_ATTN_AVAILABLE = True
+except ImportError:
+    FLASH_ATTN_AVAILABLE = False
+
+from transformers import (
+    PreTrainedModel,
+    PretrainedConfig,
+    DataCollatorForLanguageModeling,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+)
+
+from .rotary import precompute_freqs_cis, apply_rotary_emb
+
+
+class DataCollatorWithPacking(DataCollatorForLanguageModeling):
+    def __init__(self, pack_sequences=False, **kwargs):
+        super().__init__(**kwargs)
+        self.pack_sequences = pack_sequences
+
+    def __call__(self, batch):
+        if self.pack_sequences:
+            # Add position_ids if not present
+            if "position_ids" not in batch[0]:
+                for item in batch:
+                    item["position_ids"] = list(range(len(item["input_ids"])))
+
+            # Pack the sequences into a single list
+            input_ids_list = [item["input_ids"] for item in batch]
+            position_ids_list = [item["position_ids"] for item in batch]
+            seqlens = np.array([0] + [len(ids) for ids in input_ids_list])
+
+            packed_batch = {
+                "position_ids": np.concatenate(position_ids_list, axis=0),
+                "input_ids": np.concatenate(input_ids_list, axis=0),
+                "cu_seqlens": np.cumsum(seqlens),
+                "max_seqlen": max(seqlens),
+            }
+
+            batch = super().__call__([packed_batch])
+            batch["cu_seqlens"] = batch["cu_seqlens"].to(torch.int32).squeeze()
+        else:
+            batch = super().__call__(batch)
+            batch["attention_mask"] = batch["attention_mask"].to(torch.bool)
+
+        return batch
+
+
+class NeoBERTConfig(PretrainedConfig):
+    model_type = "neobert"
+
+    # All config parameters must have a default value.
+    def __init__(
+        self,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 28,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        embedding_init_range: float = 0.02,
+        decoder_init_range: float = 0.02,
+        norm_eps: float = 1e-06,
+        vocab_size: int = 30522,
+        pad_token_id: int = 0,
+        max_length: int = 1024,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        if hidden_size % num_attention_heads != 0:
+            raise ValueError("Hidden size must be divisible by the number of heads.")
+        self.dim_head = hidden_size // num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.embedding_init_range = embedding_init_range
+        self.decoder_init_range = decoder_init_range
+        self.norm_eps = norm_eps
+        self.vocab_size = vocab_size
+        self.pad_token_id = pad_token_id
+        self.max_length = max_length
+        self.kwargs = kwargs
+
+
+class EncoderBlock(nn.Module):
+    """Transformer encoder block."""
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__()
+
+        self.config = config
+
+        # Attention
+        self.qkv = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size * 3, bias=False)
+        self.wo = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=False)
+
+        # Feedforward network
+        multiple_of = 8
+        intermediate_size = int(2 * config.intermediate_size / 3)
+        intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of)
+        self.ffn = SwiGLU(config.hidden_size, intermediate_size, config.hidden_size, bias=False)
+
+        # Layer norms
+        self.attention_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
+        self.ffn_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        output_attentions: bool,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+    ):
+        # Attention
+        attn_output, attn_weights = self._att_block(
+            self.attention_norm(x), attention_mask, freqs_cis, output_attentions, max_seqlen, cu_seqlens
+        )
+
+        # Residual
+        x = x + attn_output
+
+        # Feed-forward
+        x = x + self.ffn(self.ffn_norm(x))
+
+        return x, attn_weights
+
+    def _att_block(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        output_attentions: bool,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+    ):
+        batch_size, seq_len, _ = x.shape
+
+        xq, xk, xv = self.qkv(x).view(batch_size, seq_len, self.config.num_attention_heads, self.config.dim_head * 3).chunk(3, axis=-1)
+
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
+
+        # Attn block
+        attn_weights = None
+
+        # Flash attention if the tensors are packed
+        if cu_seqlens is not None:
+            attn = flash_attn_varlen_func(
+                q=xq.squeeze(0),
+                k=xk.squeeze(0),
+                v=xv.squeeze(0),
+                cu_seqlens_q=cu_seqlens,
+                cu_seqlens_k=cu_seqlens,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                dropout_p=0.0,
+                causal=False,
+            )
+        # Eager attention if attention weights are needed in the output
+        elif output_attentions:
+            attn_weights = xq.permute(0, 2, 1, 3) @ xk.permute(0, 2, 3, 1) / (xq.size(-1) ** 0.5)
+            if attention_mask is not None:
+                attn_weights = attn_weights * attention_mask
+            attn_weights = attn_weights.softmax(-1)
+            attn = attn_weights @ xv.permute(0, 2, 1, 3)
+            attn = attn.transpose(1, 2)
+        # Fall back to SDPA otherwise
+        else:
+            attn = scaled_dot_product_attention(
+                query=xq.transpose(1, 2),
+                key=xk.transpose(1, 2),
+                value=xv.transpose(1, 2),
+                attn_mask=attention_mask.bool(),
+                dropout_p=0,
+            ).transpose(1, 2)
+
+        return self.wo(attn.reshape(batch_size, seq_len, self.config.num_attention_heads * self.config.dim_head)), attn_weights
+
+
+class NeoBERTPreTrainedModel(PreTrainedModel):
+    config_class = NeoBERTConfig
+    base_model_prefix = "model"
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.uniform_(-self.config.decoder_init_range, self.config.decoder_init_range)
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.uniform_(-self.config.embedding_init_range, self.config.embedding_init_range)
+
+
+class NeoBERT(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.encoder = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+
+        # Ensures freqs_cis is moved to the same devices as the model. Non-persistent buffers are not saved in the state_dict.
+        freqs_cis = precompute_freqs_cis(config.hidden_size // config.num_attention_heads, config.max_length)
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+
+        self.transformer_encoder = nn.ModuleList()
+        for _ in range(config.num_hidden_layers):
+            self.transformer_encoder.append(EncoderBlock(config))
+
+        self.layer_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        **kwargs,
+    ):
+        # Initialize
+        hidden_states, attentions = [], []
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        # Expand and repeat: (Batch, Length) -> (Batch, Heads, Length, Length)
+        if attention_mask is not None:
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(1).repeat(1, self.config.num_attention_heads, attention_mask.size(-1), 1)
+
+        # Checks to be done if inputs are packed sequences
+        if cu_seqlens is not None:
+            assert (
+                FLASH_ATTN_AVAILABLE
+            ), "Flash-attention is not available. Please ''pip install flash_attn'', or provide un-packed sequences."
+            assert not output_attentions, "Output attentions is not supported when sequences are packed."
+            assert max_seqlen is not None, "Missing max_seqlen. It must be provided when cu_seqlens are not None."
+            assert (input_ids if input_ids is not None else inputs_embeds).shape[
+                0
+            ] == 1, "Cumulative sequence lengths are provided but inputs are not packed."
+            assert (
+                input_ids if input_ids is not None else inputs_embeds
+            ).is_cuda, "Packing uses an implementation of flash-attention and is only supported on GPU."
+
+        # RoPE
+        freqs_cis = (
+            self.freqs_cis[position_ids]
+            if position_ids is not None
+            else self.freqs_cis[: (input_ids if input_ids is not None else inputs_embeds).shape[1]].unsqueeze(0)
+        )
+
+        # Embedding
+        x = self.encoder(input_ids) if input_ids is not None else inputs_embeds
+
+        # Transformer encoder
+        for layer in self.transformer_encoder:
+            x, attn = layer(x, attention_mask, freqs_cis, output_attentions, max_seqlen, cu_seqlens)
+            if output_hidden_states:
+                hidden_states.append(x)
+            if output_attentions:
+                attentions.append(attn)
+
+        # Final normalization layer
+        x = self.layer_norm(x)
+
+        # Return the output of the last hidden layer
+        return BaseModelOutput(
+            last_hidden_state=x,
+            hidden_states=hidden_states if output_hidden_states else None,
+            attentions=attentions if output_attentions else None,
+        )
+
+
+class NeoBERTLMHead(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.model = NeoBERT(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        **kwargs,
+    ):
+
+        output = self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            max_seqlen=max_seqlen,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        logits = self.decoder(output.last_hidden_state)
+
+        return MaskedLMOutput(
+            hidden_states=output.hidden_states if output_hidden_states else None,
+            attentions=output.attentions if output_attentions else None,
+            logits=logits,
+        )
+
+
+class NeoBERTForSequenceClassification(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.num_labels = getattr(config, "num_labels", 2)
+        self.classifier_dropout = getattr(config, "classifier_dropout", 0.1)
+        self.classifier_init_range = getattr(config, "classifier_init_range", 0.02)
+
+        self.model = NeoBERT(config)
+
+        self.dense = nn.Linear(self.config.hidden_size, self.config.hidden_size)
+        self.dropout = nn.Dropout(self.classifier_dropout)
+        self.classifier = nn.Linear(self.config.hidden_size, self.num_labels)
+
+        self.post_init()
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.classifier_init_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        labels: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        output = self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            max_seqlen=max_seqlen,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        hidden_states = output.last_hidden_state
+
+        x = hidden_states[:, 0, :]
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+
+        logits = self.classifier(x)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            result = (logits,)
+            return ((loss,) + result) if loss is not None else result
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=output.hidden_states if output_hidden_states else None,
+            attentions=output.attentions if output_attentions else None,
+        )

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae1e6bdafb1fe7f334874ad2659ff30275c460a3bb9cecf892c443639a1b15da
+size 889056736

rotary.py

@@ -0,0 +1,61 @@
+# From https://github.com/facebookresearch/llama/blob/main/llama/model.py
+
+import torch
+from typing import Tuple
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
+
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        end (int): End index for precomputing frequencies.
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials.
+    """
+
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)
+    freqs = torch.outer(t, freqs).float()
+    return torch.polar(torch.ones_like(freqs), freqs)
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    assert freqs_cis.shape[1:] == (x.shape[1], x.shape[-1])
+    return freqs_cis.contiguous().unsqueeze(2)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor.
+
+    This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided
+    frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor
+    is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are
+    returned as real tensors.
+
+    Args:
+        xq (torch.Tensor): Query tensor to apply rotary embeddings.
+        xk (torch.Tensor): Key tensor to apply rotary embeddings.
+        freqs_cis (torch.Tensor): Precomputed frequency tensor for complex exponentials.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+    """
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)

runs/Jul08_17-29-30_ip-10-192-12-113/events.out.tfevents.1751995772.ip-10-192-12-113.3586.0

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+size 14825

training_args.bin

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