Instructions to use LLM-course/chess-yentl-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-course/chess-yentl-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM-course/chess-yentl-v1")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-yentl-v1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LLM-course/chess-yentl-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM-course/chess-yentl-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM-course/chess-yentl-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM-course/chess-yentl-v1
- SGLang
How to use LLM-course/chess-yentl-v1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "LLM-course/chess-yentl-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM-course/chess-yentl-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "LLM-course/chess-yentl-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM-course/chess-yentl-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM-course/chess-yentl-v1 with Docker Model Runner:
docker model run hf.co/LLM-course/chess-yentl-v1
| """ | |
| Component-based Chess Tokenizer - Optimized for Parameter Efficiency. | |
| This tokenizer decomposes chess moves into reusable components: | |
| - Piece type (P, N, B, R, Q, K) | |
| - Source square (a1-h8) | |
| - Destination square (a1-h8) | |
| - Modifiers (capture, check, castling, etc.) | |
| Example: | |
| "WPe2e4" → ["P", "e2", "e4"] | |
| "BNg8f6(x)" → ["N", "g8", "f6", "(x)"] | |
| This reduces vocabulary from ~1682 to ~80 tokens, saving 205K parameters. | |
| """ | |
| from __future__ import annotations | |
| import json | |
| import os | |
| from typing import Dict, List, Optional | |
| from transformers import PreTrainedTokenizer | |
| class ComponentChessTokenizer(PreTrainedTokenizer): | |
| """ | |
| Component-based tokenizer for chess moves. | |
| Decomposes moves into: [piece, from_square, to_square, modifiers...] | |
| Key advantages: | |
| - 95% smaller vocabulary (1682 → 80 tokens) | |
| - Saves 205K embedding parameters | |
| - Better generalization to rare move combinations | |
| - Compositional understanding of chess structure | |
| """ | |
| 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]" | |
| SEP_TOKEN = "[SEP]" # Separates components within a move | |
| # Chess piece types (6 tokens) | |
| PIECES = ["P", "N", "B", "R", "Q", "K"] | |
| # All squares on the board (64 tokens) | |
| FILES = "abcdefgh" | |
| RANKS = "12345678" | |
| # Move modifiers (10 tokens) | |
| MODIFIERS = [ | |
| "(x)", # capture | |
| "(+)", # check | |
| "(+*)", # checkmate | |
| "(o)", # kingside castling | |
| "(O)", # queenside castling | |
| "=Q", # promotion to queen | |
| "=R", # promotion to rook | |
| "=B", # promotion to bishop | |
| "=N", # promotion to knight | |
| "(e.p.)", # en passant | |
| ] | |
| def __init__( | |
| self, | |
| vocab_file: Optional[str] = None, | |
| vocab: Optional[Dict[str, int]] = None, | |
| **kwargs, | |
| ): | |
| """Initialize the component chess tokenizer.""" | |
| # 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 duplicate special-token entries | |
| 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: | |
| self._vocab = self._create_component_vocab() | |
| # Create reverse mapping | |
| self._ids_to_tokens = {v: k for k, v in self._vocab.items()} | |
| # Call parent init | |
| 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_component_vocab(self) -> Dict[str, int]: | |
| """ | |
| Create the component vocabulary. | |
| Vocabulary structure: | |
| - Special tokens (5): [PAD], [BOS], [EOS], [UNK], [SEP] | |
| - Pieces (6): P, N, B, R, Q, K | |
| - Squares (64): a1, a2, ..., h8 | |
| - Modifiers (10): (x), (+), (+*), (o), (O), =Q, =R, =B, =N, (e.p.) | |
| Total: 85 tokens (vs 1682 in original tokenizer) | |
| """ | |
| tokens = [ | |
| self.PAD_TOKEN, | |
| self.BOS_TOKEN, | |
| self.EOS_TOKEN, | |
| self.UNK_TOKEN, | |
| self.SEP_TOKEN, | |
| ] | |
| # Add pieces | |
| tokens.extend(self.PIECES) | |
| # Add all squares | |
| squares = [f + r for f in self.FILES for r in self.RANKS] | |
| tokens.extend(squares) | |
| # Add modifiers | |
| tokens.extend(self.MODIFIERS) | |
| # Create vocabulary | |
| vocab = {token: idx for idx, token in enumerate(tokens)} | |
| return vocab | |
| def build_vocab(cls) -> "ComponentChessTokenizer": | |
| """ | |
| Build tokenizer with component vocabulary. | |
| No dataset needed - vocabulary is deterministic based on chess rules. | |
| """ | |
| return cls() | |
| 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 _decompose_move(self, move: str) -> List[str]: | |
| """ | |
| Decompose a move string into components. | |
| Examples: | |
| "WPe2e4" → ["P", "e2", "e4"] | |
| "BNg8f6(x)" → ["N", "g8", "f6", "(x)"] | |
| "WKe1g1(o)" → ["K", "e1", "g1", "(o)"] | |
| "BPe7e8=Q(+)" → ["P", "e7", "e8", "=Q", "(+)"] | |
| Args: | |
| move: Extended UCI move string (e.g., "WPe2e4") | |
| Returns: | |
| List of component tokens | |
| """ | |
| if not move or move in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]: | |
| return [move] | |
| components = [] | |
| # Remove color prefix (W/B) | |
| if move.startswith(('W', 'B')): | |
| move = move[1:] | |
| if not move: | |
| return [self.UNK_TOKEN] | |
| # Extract piece type | |
| piece = move[0] | |
| if piece in self.PIECES: | |
| components.append(piece) | |
| move = move[1:] | |
| else: | |
| # Invalid piece | |
| return [self.UNK_TOKEN] | |
| # Extract squares (from and to) | |
| # Format: <piece><from_square><to_square>[modifiers] | |
| # E.g., "Pe2e4", "Ng1f3(x)", "Ke1g1(o)" | |
| if len(move) < 4: | |
| # Not enough characters for two squares | |
| return [self.UNK_TOKEN] | |
| # Generate valid squares for checking | |
| valid_squares = [f + r for f in self.FILES for r in self.RANKS] | |
| # Extract from_square (2 chars) | |
| from_square = move[0:2] | |
| if from_square in valid_squares: | |
| components.append(from_square) | |
| else: | |
| return [self.UNK_TOKEN] | |
| # Extract to_square (2 chars) | |
| to_square = move[2:4] | |
| if to_square in valid_squares: | |
| components.append(to_square) | |
| else: | |
| return [self.UNK_TOKEN] | |
| # Extract modifiers (remaining characters) | |
| remaining = move[4:] | |
| if remaining: | |
| # Parse modifiers: (x), (+), (+*), (o), (O), =Q, =R, =B, =N, (e.p.) | |
| i = 0 | |
| while i < len(remaining): | |
| # Check for known modifiers | |
| found = False | |
| for modifier in self.MODIFIERS: | |
| if remaining[i:].startswith(modifier): | |
| components.append(modifier) | |
| i += len(modifier) | |
| found = True | |
| break | |
| if not found: | |
| # Unknown character, skip it | |
| i += 1 | |
| return components | |
| def _tokenize(self, text: str) -> List[str]: | |
| """ | |
| Tokenize a string of moves into component tokens. | |
| Args: | |
| text: Space-separated moves (e.g., "WPe2e4 BPe7e5 WNg1f3") | |
| Returns: | |
| List of component tokens | |
| """ | |
| moves = text.strip().split() | |
| tokens = [] | |
| for move in moves: | |
| # Skip special tokens | |
| if move in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]: | |
| tokens.append(move) | |
| else: | |
| # Decompose move into components | |
| components = self._decompose_move(move) | |
| tokens.extend(components) | |
| 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 component tokens back to move strings. | |
| This reconstructs moves from components. | |
| Note: We lose the W/B color prefix, but it's redundant | |
| (can be inferred from move position). | |
| """ | |
| # Filter out special tokens | |
| special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.SEP_TOKEN} | |
| tokens = [t for t in tokens if t not in special] | |
| # Generate valid squares for checking | |
| valid_squares = [f + r for f in self.FILES for r in self.RANKS] | |
| # Reconstruct moves from components | |
| moves = [] | |
| i = 0 | |
| while i < len(tokens): | |
| # Expect: piece, from_square, to_square, [modifiers...] | |
| if i + 2 >= len(tokens): | |
| break | |
| piece = tokens[i] | |
| from_sq = tokens[i + 1] | |
| to_sq = tokens[i + 2] | |
| if piece in self.PIECES and from_sq in valid_squares and to_sq in valid_squares: | |
| move = f"{piece}{from_sq}{to_sq}" | |
| i += 3 | |
| # Collect modifiers | |
| while i < len(tokens) and tokens[i] in self.MODIFIERS: | |
| move += tokens[i] | |
| i += 1 | |
| moves.append(move) | |
| else: | |
| # Skip invalid tokens | |
| i += 1 | |
| return " ".join(moves) | |
| def save_vocabulary( | |
| self, | |
| save_directory: str, | |
| filename_prefix: Optional[str] = None, | |
| ) -> tuple: | |
| """Save the vocabulary to a JSON 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,) | |