Chess Challenge submission by MDaytek

README.md

@@ -1,26 +1,31 @@
----
-library_name: transformers
-tags:
-- chess
-- llm-course
-- chess-challenge
-license: mit
----
-
-# chess-submission-v21-MDaytek
-
-Chess model submitted to the LLM Course Chess Challenge.
-
-## Submission Info
-
-- **Submitted by**: [MDaytek](https://huggingface.co/MDaytek)
-- **Parameters**: 880,544
-- **Organization**: LLM-course
-
-## Model Details
-
-- **Architecture**: Chess Transformer (GPT-style)
-- **Vocab size**: 1075
-- **Embedding dim**: 112
-- **Layers**: 5
-- **Heads**: 8
+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-submission-v21-MDaytek
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [MDaytek](https://huggingface.co/MDaytek)
+- **Parameters**: 1,040,640
+- **Organization**: LLM-course
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-submission-v21-MDaytek", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("LLM-course/chess-submission-v21-MDaytek", trust_remote_code=True)
+```
+
+## Evaluation
+
+This model is evaluated at the [Chess Challenge Arena](https://huggingface.co/spaces/LLM-course/Chess1MChallenge).

config.json

@@ -1,19 +1,18 @@
-{
-  "architectures": [
-    "ChessForCausalLM"
-  ],
-  "auto_map": {
-    "AutoConfig": "model.ChessConfig",
-    "AutoModelForCausalLM": "model.ChessForCausalLM"
-  },
-  "dropout": 0.1,
-  "dtype": "float32",
-  "model_type": "chess_gpt_rope",
-  "n_ctx": 256,
-  "n_embd": 112,
-  "n_head": 8,
-  "n_layer": 5,
-  "n_positions": 256,
-  "transformers_version": "4.57.6",
-  "vocab_size": 1075
-}
+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "dropout": 0.1,
+  "dtype": "float32",
+  "model_type": "chess_gpt",
+  "n_embd": 96,
+  "n_head": 8,
+  "n_layer": 6,
+  "n_positions": 256,
+  "transformers_version": "4.57.6",
+  "vocab_size": 1796,
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  }
+}

model.py

@@ -1,190 +1,48 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel, PretrainedConfig
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-class ChessConfig(PretrainedConfig):
-    model_type = "chess_gpt"
-
-    def __init__(
-        self,
-        vocab_size=477,
-        n_positions=256,
-        n_embd=114,
-        n_layer=6,
-        n_head=6,
-        dropout=0.1,
-        use_sinusoidal_pos=True,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.vocab_size = vocab_size
-        self.n_positions = n_positions
-        self.n_ctx = n_positions
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.dropout = dropout
-        self.use_sinusoidal_pos = use_sinusoidal_pos
-        self.tie_word_embeddings = True
-
-def sinusoidal_positional_encoding(n_positions, n_embd):
-    """Crée une positional encoding sinusoïdale (0 paramètres)"""
-    position = torch.arange(0, n_positions, dtype=torch.float).unsqueeze(1)
-    div_term = torch.exp(
-        torch.arange(0, n_embd, 2, dtype=torch.float) * (-math.log(10000.0) / n_embd)
-    )
-    pe = torch.zeros(n_positions, n_embd)
-    pe[:, 0::2] = torch.sin(position * div_term)
-    if n_embd % 2 == 0:
-        pe[:, 1::2] = torch.cos(position * div_term)
-    else:
-        pe[:, 1::2] = torch.cos(position * div_term[:-1])
-    return pe
-
-class CausalSelfAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        assert config.n_embd % config.n_head == 0
-        
-        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
-        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
-        
-        self.attn_dropout = nn.Dropout(config.dropout)
-        self.resid_dropout = nn.Dropout(config.dropout)
-        
-        self.n_head = config.n_head
-        self.n_embd = config.n_embd
-        
-    def forward(self, x):
-        B, T, C = x.size()
-        
-        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
-        
-        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
-        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
-        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
-        
-        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-        
-        mask = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1)
-        att = att.masked_fill(mask, -1e9)
-        
-        att = F.softmax(att, dim=-1)
-        att = self.attn_dropout(att)
-        
-        y = att @ v
-        y = y.transpose(1, 2).contiguous().view(B, T, C)
-        
-        y = self.resid_dropout(self.c_proj(y))
-        return y
-
-class GPTBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.ln_1 = nn.LayerNorm(config.n_embd)
-        self.attn = CausalSelfAttention(config)
-        self.ln_2 = nn.LayerNorm(config.n_embd)
-        self.mlp = nn.Sequential(
-            nn.Linear(config.n_embd, 4 * config.n_embd),
-            nn.GELU(),
-            nn.Linear(4 * config.n_embd, config.n_embd),
-            nn.Dropout(config.dropout)
-        )
-        
-    def forward(self, x):
-        x = x + self.attn(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-class ChessForCausalLM(PreTrainedModel):
-    config_class = ChessConfig
-    _no_split_modules = ["GPTBlock"]
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        
-        self.token_emb = nn.Embedding(config.vocab_size, config.n_embd)
-        
-        if config.use_sinusoidal_pos:
-            self.register_buffer(
-                "pos_emb",
-                sinusoidal_positional_encoding(config.n_positions, config.n_embd)
-            )
-            self.use_learned_pos = False
-        else:
-            self.pos_emb = nn.Embedding(config.n_positions, config.n_embd)
-            self.use_learned_pos = True
-        
-        self.dropout = nn.Dropout(config.dropout)
-        
-        self.blocks = nn.ModuleList([GPTBlock(config) for _ in range(config.n_layer)])
-        
-        self.ln_f = nn.LayerNorm(config.n_embd)
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-        
-        self.apply(self._init_weights)
-        self.post_init()
-        
-    def _init_weights(self, module):
-        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.zeros_(module.bias)
-            torch.nn.init.ones_(module.weight)
-    
-    def get_input_embeddings(self):
-        return self.token_emb
-    
-    def set_input_embeddings(self, value):
-        self.token_emb = value
-    
-    def get_output_embeddings(self):
-        return self.lm_head
-    
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-    
-    def tie_weights(self):
-        if self.config.tie_word_embeddings:
-            self._tie_or_clone_weights(self.lm_head, self.token_emb)
-
-    def forward(self, input_ids, labels=None, **kwargs):
-        B, T = input_ids.shape
-        device = input_ids.device
-        
-        tok_emb = self.token_emb(input_ids)
-        
-        if self.use_learned_pos:
-            pos = torch.arange(0, T, dtype=torch.long, device=device)
-            pos_emb = self.pos_emb(pos)
-        else:
-            pos_emb = self.pos_emb[:T, :].to(device)
-        
-        x = self.dropout(tok_emb + pos_emb)
-        
-        for block in self.blocks:
-            x = block(x)
-        
-        x = self.ln_f(x)
-        logits = self.lm_head(x)
-        
-        loss = None
-        if labels is not None:
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            
-            loss_fct = nn.CrossEntropyLoss(ignore_index=0)
-            loss = loss_fct(
-                shift_logits.view(-1, shift_logits.size(-1)),
-                shift_labels.view(-1)
-            )
-        
-        return CausalLMOutputWithPast(loss=loss, logits=logits)
+import math, torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+class ChessConfig(PretrainedConfig):
+    model_type = "chess_gpt"
+    def __init__(self, vocab_size=1804, n_positions=256, n_embd=96, n_layer=6, n_head=8, dropout=0.1, **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.dropout = dropout
+        self.tie_word_embeddings = True
+
+class ChessForCausalLM(PreTrainedModel):
+    config_class = ChessConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.token_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Embedding(config.n_positions, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(
+            d_model=config.n_embd, nhead=config.n_head, 
+            dim_feedforward=4*config.n_embd, dropout=config.dropout,
+            batch_first=True) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.post_init()
+    
+    def forward(self, input_ids, labels=None, **kwargs):
+        B, T = input_ids.shape
+        pos = torch.arange(0, T, dtype=torch.long, device=input_ids.device).unsqueeze(0)
+        x = self.token_emb(input_ids) + self.pos_emb(pos)
+        x = self.dropout(x)
+        mask = nn.Transformer.generate_square_subsequent_mask(T, device=input_ids.device)
+        for block in self.blocks:
+            x = block(x, src_mask=mask, is_causal=True)
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+            loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
+        return CausalLMOutputWithPast(loss=loss, logits=logits)

model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:5bbb597fc11d260a8c1bfb522838e8a4152a2a98e6aad64f1d77c0af6c2cc6bd
-size 3672008
+oid sha256:971af605f1f266eba24bf507782307628735cbaf1a7b21d12e087d5b51faa6e2
+size 4169488

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf70ce190a12abf05b43834e09a83188ee82d7d4dff636aa14a866e7ba097661
+size 4189781

tokenizer.py

@@ -1,171 +1,42 @@
-import json
-import os
-import torch
-
-class BatchEncoding(dict):
-    """
-    Wrapper class for tokenizer outputs that supports .to(device)
-    Compatible with HuggingFace Transformers
-    """
-    def to(self, device):
-        """Move all tensors to device"""
-        for key, value in self.items():
-            if isinstance(value, torch.Tensor):
-                self[key] = value.to(device)
-        return self
-    
-    def __getattr__(self, item):
-        try:
-            return self[item]
-        except KeyError:
-            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-class ChessTokenizer:
-    """Tokenizer pour le modèle Chess - Compatible HuggingFace"""
-    
-    def __init__(self, vocab):
-        self.vocab = vocab
-        self.id_to_token = {v: k for k, v in vocab.items()}
-        
-        # Token IDs
-        self.pad_token_id = vocab["[PAD]"]
-        self.bos_token_id = vocab["[BOS]"]
-        self.eos_token_id = vocab["[EOS]"]
-        self.unk_token_id = vocab["[UNK]"]
-        
-        # Token strings (HuggingFace attend ces attributs)
-        self.pad_token = "[PAD]"
-        self.bos_token = "[BOS]"
-        self.eos_token = "[EOS]"
-        self.unk_token = "[UNK]"
-        
-        # Autres attributs HF
-        self.model_max_length = 256
-
-    @property
-    def vocab_size(self):
-        return len(self.vocab)
-
-    def _convert_token_to_id(self, token):
-        return self.vocab.get(token, self.unk_token_id)
-    
-    def _convert_id_to_token(self, id):
-        return self.id_to_token.get(id, self.unk_token)
-    
-    def convert_tokens_to_ids(self, tokens):
-        if isinstance(tokens, str):
-            return self._convert_token_to_id(tokens)
-        return [self._convert_token_to_id(t) for t in tokens]
-    
-    def convert_ids_to_tokens(self, ids):
-        if isinstance(ids, int):
-            return self._convert_id_to_token(ids)
-        return [self._convert_id_to_token(i) for i in ids]
-    
-    def encode(self, text, add_special_tokens=True, return_tensors=None):
-        """Encode text to token IDs"""
-        tokens = text.split()
-        ids = []
-        
-        if add_special_tokens:
-            ids.append(self.bos_token_id)
-        
-        for token in tokens:
-            ids.append(self._convert_token_to_id(token))
-        
-        if add_special_tokens:
-            ids.append(self.eos_token_id)
-        
-        if return_tensors == "pt":
-            return BatchEncoding({"input_ids": torch.tensor([ids], dtype=torch.long)})
-        
-        return ids
-    
-    def __call__(self, text, return_tensors=None, padding=False, truncation=False, max_length=None, **kwargs):
-        """
-        Make tokenizer callable (HuggingFace compatibility).
-        Returns dict with input_ids that can be moved to device.
-        """
-        if isinstance(text, str):
-            text = [text]
-        
-        all_ids = []
-        for t in text:
-            ids = self.encode(t, add_special_tokens=True, return_tensors=None)
-            all_ids.append(ids)
-        
-        # Padding
-        if padding:
-            max_len = max(len(ids) for ids in all_ids)
-            if max_length:
-                max_len = min(max_len, max_length)
-            
-            all_ids = [
-                ids[:max_len] + [self.pad_token_id] * (max_len - len(ids[:max_len]))
-                for ids in all_ids
-            ]
-        
-        if return_tensors == "pt":
-            return BatchEncoding({"input_ids": torch.tensor(all_ids, dtype=torch.long)})
-        
-        return BatchEncoding({"input_ids": all_ids})
-    
-    def decode(self, ids, skip_special_tokens=True):
-        """Decode token IDs to text"""
-        if isinstance(ids, torch.Tensor):
-            ids = ids.tolist()
-        
-        tokens = []
-        for id in ids:
-            if skip_special_tokens and id in [
-                self.pad_token_id,
-                self.bos_token_id,
-                self.eos_token_id
-            ]:
-                continue
-            tokens.append(self._convert_id_to_token(id))
-        
-        return " ".join(tokens)
-
-    def save_pretrained(self, path):
-        os.makedirs(path, exist_ok=True)
-        with open(os.path.join(path, "vocab.json"), "w") as f:
-            json.dump(self.vocab, f, indent=2)
-    
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        """
-        Load tokenizer from path or HuggingFace repo.
-        
-        Args:
-            pretrained_model_name_or_path: Local path or "org/model" repo name
-            **kwargs: Additional arguments (trust_remote_code, etc.)
-        """
-        # Si c'est un nom de repo HF (contient "/")
-        if "/" in pretrained_model_name_or_path and not os.path.exists(pretrained_model_name_or_path):
-            from huggingface_hub import hf_hub_download
-            try:
-                vocab_file = hf_hub_download(
-                    repo_id=pretrained_model_name_or_path,
-                    filename="vocab.json"
-                )
-                vocab_dir = os.path.dirname(vocab_file)
-            except Exception as e:
-                raise RuntimeError(f"Failed to download vocab.json from {pretrained_model_name_or_path}: {e}")
-        else:
-            vocab_dir = pretrained_model_name_or_path
-        
-        vocab_path = os.path.join(vocab_dir, "vocab.json")
-        
-        if not os.path.exists(vocab_path):
-            raise FileNotFoundError(f"vocab.json not found in {vocab_dir}")
-        
-        with open(vocab_path, "r") as f:
-            vocab = json.load(f)
-        
-        return cls(vocab)
-    
-    @classmethod
-    def register_for_auto_class(cls, auto_class="AutoTokenizer"):
-        """Register tokenizer for AutoTokenizer"""
-        return cls
+import json, os, torch
+class BatchEncoding(dict):
+    def to(self, device):
+        for key, value in self.items():
+            if isinstance(value, torch.Tensor):
+                self[key] = value.to(device)
+        return self
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+class ChessTokenizer:
+    def __init__(self, vocab):
+        self.vocab = vocab
+        self.id_to_token = {v: k for k, v in vocab.items()}
+        self.pad_token_id = vocab["[PAD]"]
+        self.bos_token_id = vocab["[BOS]"]
+        self.eos_token_id = vocab["[EOS]"]
+        self.unk_token_id = vocab["[UNK]"]
+        self.pad_token = "[PAD]"
+        self.bos_token = "[BOS]"
+        self.eos_token = "[EOS]"
+        self.unk_token = "[UNK]"
+        self.model_max_length = 256
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+    def _convert_token_to_id(self, token):
+        return self.vocab.get(token, self.unk_token_id)
+    def _convert_id_to_token(self, id):
+        return self.id_to_token.get(id, self.unk_token)
+    def save_pretrained(self, path):
+        os.makedirs(path, exist_ok=True)
+        with open(os.path.join(path, "vocab.json"), "w") as f:
+            json.dump(self.vocab, f, indent=2)
+    @classmethod
+    def from_pretrained(cls, path, **kwargs):
+        with open(os.path.join(path, "vocab.json")) as f:
+            vocab = json.load(f)
+        return cls(vocab)

tokenizer_config.json

@@ -1,50 +1,13 @@
-{
-  "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
-    }
-  },
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenizer.ChessTokenizer",
-      null
-    ]
-  },
-  "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
+    ]
+  },
+  "model_max_length": 256,
+  "pad_token": "[PAD]",
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -1,1077 +1,1798 @@
-{
-  "[PAD]": 0,
-  "[BOS]": 1,
-  "[EOS]": 2,
-  "[UNK]": 3,
-  "WNg1f3": 4,
-  "BNg8f6": 5,
-  "WPe2e4": 6,
-  "WPd2d4": 7,
-  "WKe1g1(o)": 8,
-  "WNb1c3": 9,
-  "BNb8c6": 10,
-  "BKe8g8(o)": 11,
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