Chess Challenge submission by OussamaleZ

README.md

@@ -0,0 +1,31 @@
+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess_oussamalez_overfit
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [OussamaleZ](https://huggingface.co/OussamaleZ)
+- **Parameters**: 931,840
+- **Organization**: LLM-course
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("LLM-course/chess_oussamalez_overfit", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("LLM-course/chess_oussamalez_overfit", trust_remote_code=True)
+```
+
+## Evaluation
+
+This model is evaluated at the [Chess Challenge Arena](https://huggingface.co/spaces/LLM-course/Chess1MChallenge).

config.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.2,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 350,
+  "n_embd": 160,
+  "n_head": 4,
+  "n_inner": 160,
+  "n_kv_head": 4,
+  "n_layer": 5,
+  "pad_token_id": 0,
+  "tie_weights": false,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 81,
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  }
+}

model.py

@@ -0,0 +1,484 @@
+"""
+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_kv_head: Optional[int] = None,
+        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_kv_head = n_kv_head if n_kv_head is not None else 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})"
+        assert config.n_head % config.n_kv_head == 0, \
+            f"n_head ({config.n_head}) must be divisible by n_kv_head ({config.n_kv_head})"
+        
+        self.n_head = config.n_head
+        self.n_kv_head = config.n_kv_head
+        self.n_embd = config.n_embd
+        self.head_dim = config.n_embd // config.n_head
+        assert self.head_dim % 2 == 0, "RoPE requires an even head_dim"
+        self.kv_repeat = self.n_head // self.n_kv_head
+        
+        # Q and KV projections (GQA)
+        self.q_proj = nn.Linear(config.n_embd, self.n_head * self.head_dim)
+        self.kv_proj = nn.Linear(config.n_embd, 2 * self.n_kv_head * self.head_dim)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        
+        self.dropout = nn.Dropout(config.dropout)
+        self.q_norm = nn.LayerNorm(self.head_dim)
+        self.k_norm = nn.LayerNorm(self.head_dim)
+        
+        # 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,
+        )
+        self.register_buffer(
+            "rope_cache",
+            self._build_rope_cache(config.n_ctx, self.head_dim),
+            persistent=False,
+        )
+
+    def _build_rope_cache(self, n_ctx: int, head_dim: int) -> torch.Tensor:
+        """Precompute cos/sin for RoPE."""
+        position = torch.arange(n_ctx, dtype=torch.float)
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, head_dim, 2, dtype=torch.float) / head_dim))
+        freqs = torch.einsum("i,j->ij", position, inv_freq)
+        cos = torch.cos(freqs)
+        sin = torch.sin(freqs)
+        return torch.stack((cos, sin), dim=0)
+
+    def _apply_rope(self, x: torch.Tensor, seq_len: int) -> torch.Tensor:
+        """Apply rotary positional embedding to q/k."""
+        # x: (batch, head, seq, head_dim)
+        cos = self.rope_cache[0, :seq_len, :].to(x.device)
+        sin = self.rope_cache[1, :seq_len, :].to(x.device)
+        cos = cos.unsqueeze(0).unsqueeze(0)
+        sin = sin.unsqueeze(0).unsqueeze(0)
+        x1 = x[..., 0::2]
+        x2 = x[..., 1::2]
+        x_rot = torch.cat((x1 * cos - x2 * sin, x1 * sin + x2 * cos), dim=-1)
+        return x_rot
+    
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        batch_size, seq_len, _ = x.size()
+        
+        # Compute Q, K, V (GQA)
+        q = self.q_proj(x)
+        kv = self.kv_proj(x)
+        k, v = kv.split(self.n_kv_head * self.head_dim, 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_kv_head, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, seq_len, self.n_kv_head, self.head_dim).transpose(1, 2)
+        
+        # QK-Norm
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        
+        # Repeat KV heads to match Q heads
+        if self.kv_repeat > 1:
+            k = k.repeat_interleave(self.kv_repeat, dim=1)
+            v = v.repeat_interleave(self.kv_repeat, dim=1)
+        
+        # Apply RoPE to queries and keys
+        q = self._apply_rope(q, seq_len)
+        k = self._apply_rope(k, seq_len)
+        
+        # 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.
+    
+    SwiGLU-style MLP for better quality at small scale.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        self.c_fc = nn.Linear(config.n_embd, 2 * 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, gate = x.chunk(2, dim=-1)
+        x = F.silu(x) * gate
+        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 embeddings (RoPE is applied inside attention)
+        self.wte = nn.Embedding(config.vocab_size, 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
+        
+        # Get embeddings
+        token_embeds = self.wte(input_ids)
+        hidden_states = self.drop(token_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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25c5b4b84e1f87b085ffd943659c7b189ac9327d4f5681e89b417dd380b548c2
+size 3735400

optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:63fac4b0e8a1d61e1e22525ffd554389e8dcf49e9f6af8f7dc8a76245c8f962d
+size 7533515

rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9459ef058cdba57aad3012a1900a3d266dc4f4e9a4251f413a14d42617a25f03
+size 14645

scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fd0a3aac54aaa944a0044de0e74c345d60a2a4f26f4623035f59b54caf1ee3f
+size 1465

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer.py

@@ -0,0 +1,293 @@
+"""
+Custom Chess Tokenizer for the Chess Challenge.
+
+This tokenizer treats each move as a single token using the extended UCI notation
+from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+
+The dataset format uses:
+- W/B prefix for White/Black
+- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
+- Source and destination squares (e.g., e2e4)
+- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional
+
+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,
+        vocab: Optional[Dict[str, int]] = None,
+        **kwargs,
+    ):
+        """
+        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).
+            **kwargs: Additional arguments passed to PreTrainedTokenizer.
+        """
+        # Initialize special tokens
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+
+        # Remove any duplicate special-token entries passed through kwargs
+        # to avoid "multiple values for keyword" errors when loading from disk.
+        kwargs.pop("pad_token", None)
+        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
+        elif vocab_file is not None and os.path.exists(vocab_file):
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            # 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,
+            bos_token=self._bos_token,
+            eos_token=self._eos_token,
+            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
+    
+    @staticmethod
+    def split_move(move: str) -> List[str]:
+        tokens = [move[0:2], move[2:4], move[4:6]]
+        if "=" in move:
+            promo = move.split("=", 1)[1][:1]
+            if promo:
+                tokens.append(promo.upper())
+        tokens.append(' ')
+        return tokens
+    
+    @classmethod
+    def build_vocab_from_iterator(
+        cls,
+        iterator,
+        min_frequency: int = 1,
+    ) -> "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.
+        """
+        from collections import Counter
+        
+        token_counts = Counter()
+        
+        for game in iterator:
+            moves = game.strip().split()
+            for move in moves:
+                token_counts.update(cls.split_move(move))
+        
+        # Filter by frequency
+        tokens = [
+            token for token, count in token_counts.items()
+            if count >= min_frequency
+        ]
+        
+        # Sort for reproducibility
+        tokens = sorted(tokens)
+        
+        # Build vocabulary
+        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
+        
+        return cls(vocab=vocab)
+    
+    @classmethod
+    def build_vocab_from_dataset(
+        cls,
+        dataset_name: str = "dlouapre/lichess_2025-01_1M",
+        split: str = "train",
+        column: str = "text",
+        min_frequency: int = 500,
+        max_samples: Optional[int] = 100000,
+    ) -> "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.
+        """
+        moves = text.strip().split()
+        out = []
+        for move in moves:
+            out += self.split_move(move)
+        return out
+    
+    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,
+        filename_prefix: Optional[str] = None,
+    ) -> 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,)
+
+
+def count_vocab_from_dataset(
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    split: str = "train",
+    column: str = "text",
+    max_samples: Optional[int] = 10000,
+) -> 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

@@ -0,0 +1,50 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ]
+  }
+}

trainer_state.json

@@ -0,0 +1,378 @@
+{
+  "best_global_step": 4000,
+  "best_metric": 0.7102092504501343,
+  "best_model_checkpoint": "/Data/data_chess/checkpoint-4000",
+  "epoch": 0.2694691457828079,
+  "eval_steps": 500,
+  "global_step": 4000,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.006736728644570197,
+      "grad_norm": 0.7572135925292969,
+      "learning_rate": 0.0006644295302013424,
+      "loss": 2.9711,
+      "step": 100
+    },
+    {
+      "epoch": 0.013473457289140393,
+      "grad_norm": 0.6671218276023865,
+      "learning_rate": 0.0009999714348540981,
+      "loss": 1.607,
+      "step": 200
+    },
+    {
+      "epoch": 0.02021018593371059,
+      "grad_norm": 0.5963013172149658,
+      "learning_rate": 0.0009997429332697315,
+      "loss": 1.2186,
+      "step": 300
+    },
+    {
+      "epoch": 0.026946914578280787,
+      "grad_norm": 0.46841374039649963,
+      "learning_rate": 0.0009992860345329128,
+      "loss": 1.0664,
+      "step": 400
+    },
+    {
+      "epoch": 0.03368364322285099,
+      "grad_norm": 0.44179075956344604,
+      "learning_rate": 0.0009986009474597423,
+      "loss": 1.0087,
+      "step": 500
+    },
+    {
+      "epoch": 0.03368364322285099,
+      "eval_loss": 0.919044017791748,
+      "eval_runtime": 4.0503,
+      "eval_samples_per_second": 1234.464,
+      "eval_steps_per_second": 19.505,
+      "step": 500
+    },
+    {
+      "epoch": 0.04042037186742118,
+      "grad_norm": 0.396930456161499,
+      "learning_rate": 0.0009976879851550707,
+      "loss": 0.9759,
+      "step": 600
+    },
+    {
+      "epoch": 0.04715710051199138,
+      "grad_norm": 0.39025160670280457,
+      "learning_rate": 0.0009965475648694023,
+      "loss": 0.9506,
+      "step": 700
+    },
+    {
+      "epoch": 0.05389382915656157,
+      "grad_norm": 0.3577525317668915,
+      "learning_rate": 0.0009951802078081975,
+      "loss": 0.9323,
+      "step": 800
+    },
+    {
+      "epoch": 0.060630557801131774,
+      "grad_norm": 0.3586902618408203,
+      "learning_rate": 0.0009935865388936683,
+      "loss": 0.9148,
+      "step": 900
+    },
+    {
+      "epoch": 0.06736728644570197,
+      "grad_norm": 0.3558438718318939,
+      "learning_rate": 0.0009917672864791695,
+      "loss": 0.8993,
+      "step": 1000
+    },
+    {
+      "epoch": 0.06736728644570197,
+      "eval_loss": 0.8303639888763428,
+      "eval_runtime": 4.0434,
+      "eval_samples_per_second": 1236.578,
+      "eval_steps_per_second": 19.538,
+      "step": 1000
+    },
+    {
+      "epoch": 0.07410401509027216,
+      "grad_norm": 0.33415552973747253,
+      "learning_rate": 0.0009897232820163203,
+      "loss": 0.887,
+      "step": 1100
+    },
+    {
+      "epoch": 0.08084074373484236,
+      "grad_norm": 0.3328455686569214,
+      "learning_rate": 0.0009874554596750068,
+      "loss": 0.8764,
+      "step": 1200
+    },
+    {
+      "epoch": 0.08757747237941256,
+      "grad_norm": 0.34494221210479736,
+      "learning_rate": 0.000984964855916438,
+      "loss": 0.8687,
+      "step": 1300
+    },
+    {
+      "epoch": 0.09431420102398276,
+      "grad_norm": 0.3205359876155853,
+      "learning_rate": 0.0009822526090194543,
+      "loss": 0.8616,
+      "step": 1400
+    },
+    {
+      "epoch": 0.10105092966855295,
+      "grad_norm": 0.31778866052627563,
+      "learning_rate": 0.0009793199585602988,
+      "loss": 0.8531,
+      "step": 1500
+    },
+    {
+      "epoch": 0.10105092966855295,
+      "eval_loss": 0.7877992391586304,
+      "eval_runtime": 4.0459,
+      "eval_samples_per_second": 1235.828,
+      "eval_steps_per_second": 19.526,
+      "step": 1500
+    },
+    {
+      "epoch": 0.10778765831312315,
+      "grad_norm": 0.2896178066730499,
+      "learning_rate": 0.0009761682448460969,
+      "loss": 0.845,
+      "step": 1600
+    },
+    {
+      "epoch": 0.11452438695769335,
+      "grad_norm": 0.30994096398353577,
+      "learning_rate": 0.0009727989083022944,
+      "loss": 0.8422,
+      "step": 1700
+    },
+    {
+      "epoch": 0.12126111560226355,
+      "grad_norm": 0.2902910113334656,
+      "learning_rate": 0.0009692134888143424,
+      "loss": 0.8343,
+      "step": 1800
+    },
+    {
+      "epoch": 0.12799784424683375,
+      "grad_norm": 0.30568113923072815,
+      "learning_rate": 0.0009654136250239245,
+      "loss": 0.8302,
+      "step": 1900
+    },
+    {
+      "epoch": 0.13473457289140395,
+      "grad_norm": 0.2924252152442932,
+      "learning_rate": 0.0009614010535800489,
+      "loss": 0.8237,
+      "step": 2000
+    },
+    {
+      "epoch": 0.13473457289140395,
+      "eval_loss": 0.7636091113090515,
+      "eval_runtime": 4.0523,
+      "eval_samples_per_second": 1233.862,
+      "eval_steps_per_second": 19.495,
+      "step": 2000
+    },
+    {
+      "epoch": 0.14147130153597412,
+      "grad_norm": 0.2940412163734436,
+      "learning_rate": 0.0009571776083453492,
+      "loss": 0.8196,
+      "step": 2100
+    },
+    {
+      "epoch": 0.14820803018054432,
+      "grad_norm": 0.2964770495891571,
+      "learning_rate": 0.0009527452195579558,
+      "loss": 0.8155,
+      "step": 2200
+    },
+    {
+      "epoch": 0.15494475882511452,
+      "grad_norm": 0.29473844170570374,
+      "learning_rate": 0.0009481059129493202,
+      "loss": 0.8126,
+      "step": 2300
+    },
+    {
+      "epoch": 0.16168148746968472,
+      "grad_norm": 0.26849669218063354,
+      "learning_rate": 0.0009432618088183964,
+      "loss": 0.81,
+      "step": 2400
+    },
+    {
+      "epoch": 0.16841821611425492,
+      "grad_norm": 0.30280986428260803,
+      "learning_rate": 0.0009382151210626026,
+      "loss": 0.8071,
+      "step": 2500
+    },
+    {
+      "epoch": 0.16841821611425492,
+      "eval_loss": 0.7478171586990356,
+      "eval_runtime": 4.0503,
+      "eval_samples_per_second": 1234.482,
+      "eval_steps_per_second": 19.505,
+      "step": 2500
+    },
+    {
+      "epoch": 0.17515494475882512,
+      "grad_norm": 0.31329289078712463,
+      "learning_rate": 0.0009329681561660051,
+      "loss": 0.8021,
+      "step": 2600
+    },
+    {
+      "epoch": 0.18189167340339532,
+      "grad_norm": 0.2696886360645294,
+      "learning_rate": 0.0009275233121451872,
+      "loss": 0.8008,
+      "step": 2700
+    },
+    {
+      "epoch": 0.18862840204796552,
+      "grad_norm": 0.26912084221839905,
+      "learning_rate": 0.0009218830774532855,
+      "loss": 0.7973,
+      "step": 2800
+    },
+    {
+      "epoch": 0.1953651306925357,
+      "grad_norm": 0.27081477642059326,
+      "learning_rate": 0.0009160500298426945,
+      "loss": 0.7935,
+      "step": 2900
+    },
+    {
+      "epoch": 0.2021018593371059,
+      "grad_norm": 0.27368906140327454,
+      "learning_rate": 0.0009100268351869579,
+      "loss": 0.7908,
+      "step": 3000
+    },
+    {
+      "epoch": 0.2021018593371059,
+      "eval_loss": 0.7335129976272583,
+      "eval_runtime": 4.0548,
+      "eval_samples_per_second": 1233.113,
+      "eval_steps_per_second": 19.483,
+      "step": 3000
+    },
+    {
+      "epoch": 0.2088385879816761,
+      "grad_norm": 0.32002657651901245,
+      "learning_rate": 0.0009038162462623858,
+      "loss": 0.787,
+      "step": 3100
+    },
+    {
+      "epoch": 0.2155753166262463,
+      "grad_norm": 0.25296279788017273,
+      "learning_rate": 0.0008974211014899564,
+      "loss": 0.7873,
+      "step": 3200
+    },
+    {
+      "epoch": 0.2223120452708165,
+      "grad_norm": 0.27432960271835327,
+      "learning_rate": 0.0008908443236380743,
+      "loss": 0.7827,
+      "step": 3300
+    },
+    {
+      "epoch": 0.2290487739153867,
+      "grad_norm": 0.2552671730518341,
+      "learning_rate": 0.0008840889184867782,
+      "loss": 0.7793,
+      "step": 3400
+    },
+    {
+      "epoch": 0.2357855025599569,
+      "grad_norm": 0.27509957551956177,
+      "learning_rate": 0.0008771579734540138,
+      "loss": 0.7781,
+      "step": 3500
+    },
+    {
+      "epoch": 0.2357855025599569,
+      "eval_loss": 0.7217926383018494,
+      "eval_runtime": 4.0438,
+      "eval_samples_per_second": 1236.453,
+      "eval_steps_per_second": 19.536,
+      "step": 3500
+    },
+    {
+      "epoch": 0.2425222312045271,
+      "grad_norm": 0.25811567902565,
+      "learning_rate": 0.0008700546561845919,
+      "loss": 0.7764,
+      "step": 3600
+    },
+    {
+      "epoch": 0.24925895984909727,
+      "grad_norm": 0.26611506938934326,
+      "learning_rate": 0.000862782213102482,
+      "loss": 0.7744,
+      "step": 3700
+    },
+    {
+      "epoch": 0.2559956884936675,
+      "grad_norm": 0.2390570193529129,
+      "learning_rate": 0.0008553439679271025,
+      "loss": 0.7717,
+      "step": 3800
+    },
+    {
+      "epoch": 0.2627324171382377,
+      "grad_norm": 0.26289165019989014,
+      "learning_rate": 0.0008477433201542824,
+      "loss": 0.769,
+      "step": 3900
+    },
+    {
+      "epoch": 0.2694691457828079,
+      "grad_norm": 0.2603146731853485,
+      "learning_rate": 0.0008399837435025926,
+      "loss": 0.7658,
+      "step": 4000
+    },
+    {
+      "epoch": 0.2694691457828079,
+      "eval_loss": 0.7102092504501343,
+      "eval_runtime": 4.0458,
+      "eval_samples_per_second": 1235.853,
+      "eval_steps_per_second": 19.526,
+      "step": 4000
+    }
+  ],
+  "logging_steps": 100,
+  "max_steps": 14844,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 1,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 493989888000000.0,
+  "train_batch_size": 64,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:554c8d7f2ee5a566a96af9e0c7c3aab64f9364723061d79d555fa187b12ef08d
+size 5777

vocab.json

@@ -0,0 +1,83 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  " ": 4,
+  "BB": 5,
+  "BK": 6,
+  "BN": 7,
+  "BP": 8,
+  "BQ": 9,
+  "BR": 10,
+  "WB": 11,
+  "WK": 12,
+  "WN": 13,
+  "WP": 14,
+  "WQ": 15,
+  "WR": 16,
+  "a1": 17,
+  "a2": 18,
+  "a3": 19,
+  "a4": 20,
+  "a5": 21,
+  "a6": 22,
+  "a7": 23,
+  "a8": 24,
+  "b1": 25,
+  "b2": 26,
+  "b3": 27,
+  "b4": 28,
+  "b5": 29,
+  "b6": 30,
+  "b7": 31,
+  "b8": 32,
+  "c1": 33,
+  "c2": 34,
+  "c3": 35,
+  "c4": 36,
+  "c5": 37,
+  "c6": 38,
+  "c7": 39,
+  "c8": 40,
+  "d1": 41,
+  "d2": 42,
+  "d3": 43,
+  "d4": 44,
+  "d5": 45,
+  "d6": 46,
+  "d7": 47,
+  "d8": 48,
+  "e1": 49,
+  "e2": 50,
+  "e3": 51,
+  "e4": 52,
+  "e5": 53,
+  "e6": 54,
+  "e7": 55,
+  "e8": 56,
+  "f1": 57,
+  "f2": 58,
+  "f3": 59,
+  "f4": 60,
+  "f5": 61,
+  "f6": 62,
+  "f7": 63,
+  "f8": 64,
+  "g1": 65,
+  "g2": 66,
+  "g3": 67,
+  "g4": 68,
+  "g5": 69,
+  "g6": 70,
+  "g7": 71,
+  "g8": 72,
+  "h1": 73,
+  "h2": 74,
+  "h3": 75,
+  "h4": 76,
+  "h5": 77,
+  "h6": 78,
+  "h7": 79,
+  "h8": 80
+}