Chess Challenge submission by pultch

README.md

@@ -14,13 +14,18 @@ Chess model submitted to the LLM Course Chess Challenge.
 ## Submission Info
 
 - **Submitted by**: [pultch](https://huggingface.co/pultch)
-- **Parameters**: 852,992
+- **Parameters**: 861,184
 - **Organization**: LLM-course
 
-## Model Details
+## Usage
 
-- **Architecture**: Chess Transformer (GPT-style)
-- **Vocab size**: 74
-- **Embedding dim**: 128
-- **Layers**: 5
-- **Heads**: 8
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("LLM-course/simple_tokenizer", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("LLM-course/simple_tokenizer", trust_remote_code=True)
+```
+
+## Evaluation
+
+This model is evaluated at the [Chess Challenge Arena](https://huggingface.co/spaces/LLM-course/Chess1MChallenge).

config.json

@@ -8,7 +8,7 @@
   "eos_token_id": 2,
   "layer_norm_epsilon": 1e-05,
   "model_type": "chess_transformer",
-  "n_ctx": 128,
+  "n_ctx": 192,
   "n_embd": 128,
   "n_head": 8,
   "n_inner": 384,
@@ -16,5 +16,9 @@
   "pad_token_id": 0,
   "tie_weights": true,
   "transformers_version": "4.57.6",
-  "vocab_size": 74
-}
+  "vocab_size": 74,
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  }
+}

model.py

@@ -0,0 +1,438 @@
+"""
+Chess Transformer Model for the Chess Challenge.
+
+This module provides a simple GPT-style transformer architecture
+designed to fit within the 1M parameter constraint.
+
+Key components:
+- ChessConfig: Configuration class for model hyperparameters
+- ChessForCausalLM: The main model class for next-move prediction
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class ChessConfig(PretrainedConfig):
+    """
+    Configuration class for the Chess Transformer model.
+    
+    This configuration is designed for a ~1M parameter model.
+    Students can adjust these values to explore different architectures.
+    
+    Parameter budget breakdown (with default values):
+    - Embeddings (vocab): 1200 x 128 = 153,600
+    - Position Embeddings: 256 x 128 = 32,768
+    - Transformer Layers: 6 x ~120,000 = ~720,000
+    - LM Head (with weight tying): 0 (shared with embeddings)
+    - Total: ~906,000 parameters
+    
+    Attributes:
+        vocab_size: Size of the vocabulary (number of unique moves).
+        n_embd: Embedding dimension (d_model).
+        n_layer: Number of transformer layers.
+        n_head: Number of attention heads.
+        n_ctx: Maximum sequence length (context window).
+        n_inner: Feed-forward inner dimension (default: 3 * n_embd).
+        dropout: Dropout probability.
+        layer_norm_epsilon: Epsilon for layer normalization.
+        tie_weights: Whether to tie embedding and output weights.
+    """
+    
+    model_type = "chess_transformer"
+    
+    def __init__(
+        self,
+        vocab_size: int = 1200,
+        n_embd: int = 128,
+        n_layer: int = 6,
+        n_head: int = 4,
+        n_ctx: int = 256,
+        n_inner: Optional[int] = None,
+        dropout: float = 0.1,
+        layer_norm_epsilon: float = 1e-5,
+        tie_weights: bool = True,
+        pad_token_id: int = 0,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_ctx = n_ctx
+        self.n_inner = n_inner if n_inner is not None else 3 * n_embd  # Reduced from 4x to 3x
+        self.dropout = dropout
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.tie_weights = tie_weights
+        # Inform HF base class about tying behavior
+        self.tie_word_embeddings = bool(tie_weights)
+
+
+class MultiHeadAttention(nn.Module):
+    """
+    Multi-head self-attention module.
+    
+    This is a standard scaled dot-product attention implementation
+    with causal masking for autoregressive generation.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        assert config.n_embd % config.n_head == 0, \
+            f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})"
+        
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.head_dim = config.n_embd // config.n_head
+        
+        # Combined QKV projection for efficiency
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        
+        self.dropout = nn.Dropout(config.dropout)
+        
+        # Causal mask (will be created on first forward pass)
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(
+                1, 1, config.n_ctx, config.n_ctx
+            ),
+            persistent=False,
+        )
+    
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        batch_size, seq_len, _ = x.size()
+        
+        # Compute Q, K, V
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        
+        # Reshape for multi-head attention
+        q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
+        
+        # Scaled dot-product attention
+        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        
+        # Apply causal mask
+        causal_mask = self.bias[:, :, :seq_len, :seq_len]
+        attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf"))
+        
+        # Apply attention mask (for padding)
+        if attention_mask is not None:
+            # attention_mask shape: (batch_size, seq_len) -> (batch_size, 1, 1, seq_len)
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf"))
+        
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        
+        # Apply attention to values
+        attn_output = torch.matmul(attn_weights, v)
+        
+        # Reshape back
+        attn_output = attn_output.transpose(1, 2).contiguous().view(
+            batch_size, seq_len, self.n_embd
+        )
+        
+        # Output projection
+        attn_output = self.c_proj(attn_output)
+        
+        return attn_output
+
+
+class FeedForward(nn.Module):
+    """
+    Feed-forward network (MLP) module.
+    
+    Standard two-layer MLP with GELU activation.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        self.c_fc = nn.Linear(config.n_embd, config.n_inner)
+        self.c_proj = nn.Linear(config.n_inner, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.c_fc(x)
+        x = F.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+class TransformerBlock(nn.Module):
+    """
+    A single transformer block with attention and feed-forward layers.
+    
+    Uses pre-normalization (LayerNorm before attention/FFN) for better
+    training stability.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = MultiHeadAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.mlp = FeedForward(config)
+    
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        # Pre-norm attention
+        x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
+        # Pre-norm FFN
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class ChessForCausalLM(PreTrainedModel):
+    """
+    Chess Transformer for Causal Language Modeling (next-move prediction).
+    
+    This model is designed to predict the next chess move given a sequence
+    of previous moves. It uses a GPT-style architecture with:
+    - Token embeddings for chess moves
+    - Learned positional embeddings
+    - Stacked transformer blocks
+    - Linear head for next-token prediction
+    
+    The model supports weight tying between the embedding layer and the
+    output projection to save parameters.
+    
+    Example:
+        >>> config = ChessConfig(vocab_size=1200, n_embd=128, n_layer=6)
+        >>> model = ChessForCausalLM(config)
+        >>> inputs = {"input_ids": torch.tensor([[1, 42, 87]])}
+        >>> outputs = model(**inputs)
+        >>> next_move_logits = outputs.logits[:, -1, :]
+    """
+    
+    config_class = ChessConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    # Suppress missing-key warning for tied lm_head when loading
+    keys_to_ignore_on_load_missing = ["lm_head.weight"]
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__(config)
+        
+        # Token and position embeddings
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
+        
+        self.drop = nn.Dropout(config.dropout)
+        
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            TransformerBlock(config) for _ in range(config.n_layer)
+        ])
+        
+        # Final layer norm
+        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        
+        # Output head
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        
+        # Declare tied weights for proper serialization
+        if config.tie_weights:
+            self._tied_weights_keys = ["lm_head.weight"]
+        
+        # Initialize weights
+        self.post_init()
+        
+        # Tie weights if configured
+        if config.tie_weights:
+            self.tie_weights()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings: nn.Module):
+        self.wte = new_embeddings
+        if getattr(self.config, "tie_weights", False):
+            self.tie_weights()
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings: nn.Module):
+        self.lm_head = new_embeddings
+
+    def tie_weights(self):
+        # Use HF helper to tie or clone depending on config
+        if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False):
+            self._tie_or_clone_weights(self.lm_head, self.wte)
+    
+    def _init_weights(self, module: nn.Module):
+        """Initialize weights following GPT-2 style."""
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            torch.nn.init.ones_(module.weight)
+            torch.nn.init.zeros_(module.bias)
+    
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        """
+        Forward pass of the model.
+        
+        Args:
+            input_ids: Token IDs of shape (batch_size, seq_len).
+            attention_mask: Attention mask of shape (batch_size, seq_len).
+            position_ids: Position IDs of shape (batch_size, seq_len).
+            labels: Labels for language modeling loss.
+            return_dict: Whether to return a ModelOutput object.
+        
+        Returns:
+            CausalLMOutputWithPast containing loss (if labels provided) and logits.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        batch_size, seq_len = input_ids.size()
+        device = input_ids.device
+        
+        # Create position IDs if not provided
+        if position_ids is None:
+            position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
+        
+        # Get embeddings
+        token_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = self.drop(token_embeds + position_embeds)
+        
+        # Pass through transformer blocks
+        for block in self.h:
+            hidden_states = block(hidden_states, attention_mask=attention_mask)
+        
+        # Final layer norm
+        hidden_states = self.ln_f(hidden_states)
+        
+        # Get logits
+        logits = self.lm_head(hidden_states)
+        
+        # Compute loss if labels are provided
+        loss = None
+        if labels is not None:
+            # Shift logits and labels for next-token prediction
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            
+            # Flatten for cross-entropy
+            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+            # loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1),
+            )
+        
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+        
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=None,
+            hidden_states=None,
+            attentions=None,
+        )
+    
+    @torch.no_grad()
+    def generate_move(
+        self,
+        input_ids: torch.LongTensor,
+        temperature: float = 1.0,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+    ) -> int:
+        """
+        Generate the next move given a sequence of moves.
+        
+        Args:
+            input_ids: Token IDs of shape (1, seq_len).
+            temperature: Sampling temperature (1.0 = no change).
+            top_k: If set, only sample from top k tokens.
+            top_p: If set, use nucleus sampling with this threshold.
+        
+        Returns:
+            The token ID of the predicted next move.
+        """
+        self.eval()
+        
+        # Get logits for the last position
+        outputs = self(input_ids)
+        logits = outputs.logits[:, -1, :] / temperature
+        
+        # Apply top-k filtering
+        if top_k is not None:
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = float("-inf")
+        
+        # Apply top-p (nucleus) filtering
+        if top_p is not None:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+            
+            # Remove tokens with cumulative probability above the threshold
+            sorted_indices_to_remove = cumulative_probs > top_p
+            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+            sorted_indices_to_remove[..., 0] = 0
+            
+            indices_to_remove = sorted_indices_to_remove.scatter(
+                dim=-1, index=sorted_indices, src=sorted_indices_to_remove
+            )
+            logits[indices_to_remove] = float("-inf")
+        
+        # Sample from the distribution
+        probs = F.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1)
+        
+        return next_token.item()
+
+
+# Register the model with Auto classes for easy loading
+from transformers import AutoConfig, AutoModelForCausalLM
+
+AutoConfig.register("chess_transformer", ChessConfig)
+AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6185731b28f686833e495232d80a8be24f5c3dbad31752396b5cfc2f4cd413f4
-size 3417392
+oid sha256:bdba2c9107ce95c2cae9977558f734b4293fb4d7ae68b0015cedf5c668d9b115
+size 3450160

tokenizer.py

@@ -25,26 +25,26 @@ from transformers import PreTrainedTokenizer
 class ChessTokenizer(PreTrainedTokenizer):
     """
     A custom tokenizer for chess moves using extended UCI notation.
-    
+
     This tokenizer maps each possible chess move to a unique token ID.
     The vocabulary is built from the training dataset to ensure all moves
     encountered during training have a corresponding token.
-    
+
     Example:
         >>> tokenizer = ChessTokenizer()
         >>> tokenizer.encode("WPe2e4 BPe7e5")
         [1, 42, 87, 2]  # [BOS, e2e4, e7e5, EOS]
     """
-    
+
     model_input_names = ["input_ids", "attention_mask"]
     vocab_files_names = {"vocab_file": "vocab.json"}
-    
+
     # Special tokens
     PAD_TOKEN = "[PAD]"
     BOS_TOKEN = "[BOS]"
     EOS_TOKEN = "[EOS]"
     UNK_TOKEN = "[UNK]"
-    
+
     def __init__(
         self,
         vocab_file: Optional[str] = None,
@@ -53,7 +53,7 @@ class ChessTokenizer(PreTrainedTokenizer):
     ):
         """
         Initialize the chess tokenizer.
-        
+
         Args:
             vocab_file: Path to a JSON file containing the vocabulary mapping.
             vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
@@ -71,7 +71,7 @@ class ChessTokenizer(PreTrainedTokenizer):
         kwargs.pop("bos_token", None)
         kwargs.pop("eos_token", None)
         kwargs.pop("unk_token", None)
-        
+
         # Load or create vocabulary
         if vocab is not None:
             self._vocab = vocab
@@ -82,10 +82,10 @@ class ChessTokenizer(PreTrainedTokenizer):
             # Create a minimal vocabulary with just special tokens
             # The full vocabulary should be built from the dataset
             self._vocab = self._create_default_vocab()
-        
+
         # Create reverse mapping
         self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
-        
+
         # Call parent init AFTER setting up vocab
         super().__init__(
             pad_token=self._pad_token,
@@ -94,18 +94,18 @@ class ChessTokenizer(PreTrainedTokenizer):
             unk_token=self._unk_token,
             **kwargs,
         )
-    
+
     def _create_default_vocab(self) -> Dict[str, int]:
         """
         Create a minimal default vocabulary with just special tokens.
-        
+
         For the full vocabulary, use `build_vocab_from_dataset()`.
         This minimal vocab is just a placeholder - you should build from data.
         """
         special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
         vocab = {token: idx for idx, token in enumerate(special_tokens)}
         return vocab
-    
+
     @classmethod
     def build_vocab_from_iterator(
         cls,
@@ -114,11 +114,11 @@ class ChessTokenizer(PreTrainedTokenizer):
     ) -> "ChessTokenizer":
         """
         Build a tokenizer vocabulary from an iterator of game strings.
-        
+
         Args:
             iterator: An iterator yielding game strings (space-separated moves).
             min_frequency: Minimum frequency for a token to be included.
-        
+
         Returns:
             A ChessTokenizer with the built vocabulary.
         """
@@ -127,7 +127,7 @@ class ChessTokenizer(PreTrainedTokenizer):
         # from collections import Counter
         #
         # token_counts = Counter()
-        
+
         # for game in iterator:
         #     moves = game.strip().split()
         #     token_counts.update(moves)
@@ -141,7 +141,7 @@ class ChessTokenizer(PreTrainedTokenizer):
         #
         # # Sort for reproducibility
         # tokens = sorted(tokens)
-        
+
         # Build vocabulary
         special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
         piece = ['K', 'Q', 'R', 'B', 'N', 'P']
@@ -149,9 +149,9 @@ class ChessTokenizer(PreTrainedTokenizer):
 
         # vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
         vocab = {token: idx for idx, token in enumerate(special_tokens + piece + move)}
-        
+
         return cls(vocab=vocab)
-    
+
     @classmethod
     def build_vocab_from_dataset(
         cls,
@@ -163,46 +163,46 @@ class ChessTokenizer(PreTrainedTokenizer):
     ) -> "ChessTokenizer":
         """
         Build a tokenizer vocabulary from a Hugging Face dataset.
-        
+
         Args:
             dataset_name: Name of the dataset on Hugging Face Hub.
             split: Dataset split to use.
             column: Column containing the game strings.
             min_frequency: Minimum frequency for a token to be included (default: 500).
             max_samples: Maximum number of samples to process (default: 100k).
-        
+
         Returns:
             A ChessTokenizer with the built vocabulary.
         """
         from datasets import load_dataset
-        
+
         dataset = load_dataset(dataset_name, split=split)
-        
+
         if max_samples is not None:
             dataset = dataset.select(range(min(max_samples, len(dataset))))
-        
+
         def game_iterator():
             for example in dataset:
                 yield example[column]
-        
+
         return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)
-    
+
     @property
     def vocab_size(self) -> int:
         """Return the size of the vocabulary."""
         return len(self._vocab)
-    
+
     def get_vocab(self) -> Dict[str, int]:
         """Return the vocabulary as a dictionary."""
         return dict(self._vocab)
-    
+
     def _tokenize(self, text: str) -> List[str]:
         """
         Tokenize a string of moves into a list of tokens.
-        
+
         Args:
             text: A string of space-separated moves.
-        
+
         Returns:
             List of move tokens.
         """
@@ -218,21 +218,21 @@ class ChessTokenizer(PreTrainedTokenizer):
                 tokens += self.UNK_TOKEN
 
         return tokens
-    
+
     def _convert_token_to_id(self, token: str) -> int:
         """Convert a token to its ID."""
         return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
-    
+
     def _convert_id_to_token(self, index: int) -> str:
         """Convert an ID to its token."""
         return self._ids_to_tokens.get(index, self.UNK_TOKEN)
-    
+
     def convert_tokens_to_string(self, tokens: List[str]) -> str:
         """Convert a list of tokens back to a string."""
         # Filter out special tokens for cleaner output
         special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
         return " ".join(t for t in tokens if t not in special)
-    
+
     def save_vocabulary(
         self,
         save_directory: str,
@@ -240,25 +240,25 @@ class ChessTokenizer(PreTrainedTokenizer):
     ) -> tuple:
         """
         Save the vocabulary to a JSON file.
-        
+
         Args:
             save_directory: Directory to save the vocabulary.
             filename_prefix: Optional prefix for the filename.
-        
+
         Returns:
             Tuple containing the path to the saved vocabulary file.
         """
         if not os.path.isdir(save_directory):
             os.makedirs(save_directory, exist_ok=True)
-        
+
         vocab_file = os.path.join(
             save_directory,
             (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
         )
-        
+
         with open(vocab_file, "w", encoding="utf-8") as f:
             json.dump(self._vocab, f, ensure_ascii=False, indent=2)
-        
+
         return (vocab_file,)
 
 
@@ -270,28 +270,28 @@ def count_vocab_from_dataset(
 ) -> Dict[str, int]:
     """
     Count token frequencies in a dataset (useful for vocabulary analysis).
-    
+
     Args:
         dataset_name: Name of the dataset on Hugging Face Hub.
         split: Dataset split to use.
         column: Column containing the game strings.
         max_samples: Maximum number of samples to process.
-    
+
     Returns:
         Dictionary mapping tokens to their frequencies.
     """
     from collections import Counter
     from datasets import load_dataset
-    
+
     dataset = load_dataset(dataset_name, split=split)
-    
+
     if max_samples is not None:
         dataset = dataset.select(range(min(max_samples, len(dataset))))
-    
+
     token_counts = Counter()
-    
+
     for example in dataset:
         moves = example[column].strip().split()
         token_counts.update(moves)
-    
+
     return dict(token_counts)

tokenizer_config.json

@@ -33,12 +33,6 @@
       "special": true
     }
   },
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenizer.ChessTokenizer",
-      null
-    ]
-  },
   "bos_token": "[BOS]",
   "clean_up_tokenization_spaces": false,
   "eos_token": "[EOS]",
@@ -46,5 +40,11 @@
   "model_max_length": 1000000000000000019884624838656,
   "pad_token": "[PAD]",
   "tokenizer_class": "ChessTokenizer",
-  "unk_token": "[UNK]"
-}
+  "unk_token": "[UNK]",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ]
+  }
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6233dcdd155640aa91cb55f44e1d6354ba7a1bfb4fbd7928908a301ea7f30b1
+size 5777