Upload SFT checkpoint: C6p5e18_200m_alpha0.200_beta0.100

C6p5e18_200m_alpha0.200_beta0.100/config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 2304,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 2048,
+  "max_window_layers": 24,
+  "model_type": "qwen3",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "factor": 2.0,
+    "original_max_position_embeddings": 1024,
+    "type": "yarn"
+  },
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 84
+}

C6p5e18_200m_alpha0.200_beta0.100/generation_config.json

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+{
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": 1,
+  "max_new_tokens": 1024,
+  "transformers_version": "4.57.0"
+}

C6p5e18_200m_alpha0.200_beta0.100/model.safetensors

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+oid sha256:6f936fd76d26adac244e4a5dc00501ebc38da25515f68d108597ca9b4d99bcb2
+size 812060488

C6p5e18_200m_alpha0.200_beta0.100/optimizer_states/model.safetensors

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+size 812060488

C6p5e18_200m_alpha0.200_beta0.100/optimizer_states/optimizer.bin

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+size 1624285707

C6p5e18_200m_alpha0.200_beta0.100/optimizer_states/random_states_0.pkl

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+size 15017

C6p5e18_200m_alpha0.200_beta0.100/optimizer_states/scheduler.bin

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+version https://git-lfs.github.com/spec/v1
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+size 1465

C6p5e18_200m_alpha0.200_beta0.100/optimizer_states/training_state.json

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+{"step": 385, "epoch": 2}

C6p5e18_200m_alpha0.200_beta0.100/special_tokens_map.json

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+{
+  "bos_token": "<bos>",
+  "eos_token": "<eos>",
+  "pad_token": "<bos>",
+  "unk_token": "<unk>",
+  "env_token": null
+}

C6p5e18_200m_alpha0.200_beta0.100/tokenizer.py

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+"""
+Auto-generated self-contained HF tokenizer.
+Do NOT edit manually -- regenerate via training.hf_tokenizer_utils.save_hf_tokenizer().
+"""
+from __future__ import annotations
+
+# --- BaseTokenizer (inlined) ---
+# base_tokenizer.py
+from abc import ABC, abstractmethod
+from typing import List, Dict, Optional
+
+class BaseTokenizer(ABC):
+    """Minimal interface for tokenizers used in pretraining."""
+
+    # ---- required ----
+    @abstractmethod
+    def encode(self, text: str) -> List[int]:
+        """Convert text/PGN to token IDs."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def decode(self, ids: List[int]) -> str:
+        """Convert token IDs back to text/PGN."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_vocab(self) -> Dict[str, int]:
+        """Return token -> id mapping (if available)."""
+        raise NotImplementedError
+
+    def bos_id(self) -> Optional[int]: return None
+    def eos_id(self) -> Optional[int]: return None
+    def pad_id(self) -> Optional[int]: return None
+    def get_vocab_size(self) -> int: return len(self.get_vocab())
+
+    def __call__(self, text: str) -> List[int]:
+        """Alias for encode()."""
+        return self.encode(text)
+
+# --- Concrete tokenizer (inlined) ---
+# lan_tokenizer_sft.py
+"""
+LAN Tokenizer with SFT support (CoT format with <T> and <sep> tokens).
+
+This extends the base LAN tokenizer with SFT-specific functionality:
+- <T> token for marking thinking/CoT content
+- <sep> token for separating prompt from response
+"""
+from typing import List, Dict, Optional, Tuple
+import io
+import chess, chess.pgn
+from tokenizers import Tokenizer
+from tokenizers.models import WordLevel
+from tokenizers.pre_tokenizers import WhitespaceSplit
+_RESULT = {"1-0", "0-1", "1/2-1/2", "*"}
+FILES = "abcdefgh"
+RANKS = "12345678"
+SQUARES = [f+r for f in FILES for r in RANKS]
+PROMOS = "QRBN"
+DIGITS = set("0123456789")
+
+# SFT special tokens for CoT format
+T_TOKEN = "<T>"
+T_END_TOKEN = "</T>"
+SEP_TOKEN = "<sep>"
+
+# Environment interaction / reward special tokens
+CALL_ENV_TOKEN = "<call_env>"
+VERIFY_TOKEN = "<verify>"
+REWARD_POS_TOKEN = "<+1>"
+REWARD_NEG_TOKEN = "<-1>"
+REWARD_ZERO_TOKEN = "<0>"
+ENV_TOKENS = [CALL_ENV_TOKEN]
+REWARD_TOKENS = [VERIFY_TOKEN, REWARD_POS_TOKEN, REWARD_NEG_TOKEN, REWARD_ZERO_TOKEN]
+
+def _vocab_with_sft(
+    include_move_numbers: bool,
+    keep_result: bool,
+    bos: str,
+    eos: str,
+    unk: str,
+    include_env_tokens: bool = False,
+    include_reward_tokens: bool = False,
+) -> Dict[str, int]:
+    """Create vocabulary including SFT special tokens."""
+    base = [bos, eos, unk]
+    ops = ["x", "=", "+", "#", "O-O", "O-O-O", ".", "..."]
+    toks = base + list("KQRBNP") + SQUARES + list(PROMOS) + ops
+    if include_move_numbers:
+        toks += list("0123456789")
+    if keep_result:
+        toks += sorted(_RESULT)
+
+    # Add SFT special tokens for CoT format
+    sft_tokens = [T_TOKEN, T_END_TOKEN, SEP_TOKEN]
+    toks += sft_tokens
+
+    # Add environment / reward tokens when requested
+    if include_env_tokens:
+        toks += ENV_TOKENS
+    if include_reward_tokens:
+        toks += REWARD_TOKENS
+
+    return {t: i for i, t in enumerate(dict.fromkeys(toks))}
+
+
+class LanTokenizerSFT(BaseTokenizer):
+    """
+    LAN Tokenizer with SFT capabilities.
+
+    This tokenizer extends the base LAN tokenizer with:
+    - <T> token for marking thinking/CoT boundaries
+    - <sep> token for separating candidate trajectories
+
+    CoT Format: {prompt} <T> <sep> {traj1} <sep> {traj2} <sep> ... <sep> {trajN} <sep> <T> {answer}
+
+    Where:
+    - {prompt}: The game history/board state (PGN moves)
+    - {trajN}: Candidate reasoning trajectories
+    - {answer}: The final best move
+    """
+
+    # Special tokens for CoT format
+    T = T_TOKEN
+    T_END = T_END_TOKEN
+    SEP = SEP_TOKEN
+
+    # Environment / reward tokens (class-level constants for easy access)
+    CALL_ENV = CALL_ENV_TOKEN   # "<call_env>"
+    VERIFY = VERIFY_TOKEN       # "<verify>"
+    REWARD_POS = REWARD_POS_TOKEN  # "<+1>"
+    REWARD_NEG = REWARD_NEG_TOKEN  # "<-1>"
+    REWARD_ZERO = REWARD_ZERO_TOKEN  # "<0>"
+    ENV_TOKENS = ENV_TOKENS     # full list
+
+    def __init__(self, config: Optional[dict] = None):
+        """
+        Args:
+            config: Configuration dict with tokenizer settings.
+                include_env_tokens (bool): add <call_env>, <verify>, <+1>, <-1>, <0>
+                    to the vocabulary.  Default: False.
+        """
+        config = config or {}
+
+        include_move_numbers = config.get("include_move_numbers", False)
+        include_black_tripledots = config.get("include_black_tripledots", False)
+        bos = config.get("bos", "<bos>")
+        eos = config.get("eos", "<eos>")
+        unk = config.get("unk", "<unk>")
+        keep_result = config.get("keep_result", False)
+        include_env_tokens = config.get("include_env_tokens", False)
+        include_reward_tokens = config.get("include_reward_tokens", False)
+
+        self._bos = bos
+        self._eos = eos
+        self._unk = unk
+        self._keep_res = keep_result
+        self._include_nums = include_move_numbers
+        self._include_black_ellipses = include_black_tripledots
+        self._include_env_tokens = include_env_tokens
+        self._include_reward_tokens = include_reward_tokens
+
+        # Create vocabulary with SFT tokens
+        tok2id = _vocab_with_sft(
+            include_move_numbers, keep_result, bos, eos, unk,
+            include_env_tokens=include_env_tokens,
+            include_reward_tokens=include_reward_tokens,
+        )
+        self._tok2id = tok2id
+
+        # Initialize tokenizer
+        self.tk = Tokenizer(WordLevel(vocab=tok2id, unk_token=self._unk))
+        self.tk.pre_tokenizer = WhitespaceSplit()
+    
+    def _pgn_to_tokens(self, text: str) -> Optional[List[str]]:
+        """Convert PGN text to tokens."""
+        import os, contextlib
+        with open(os.devnull, "w") as devnull, contextlib.redirect_stderr(devnull):
+            g = chess.pgn.read_game(io.StringIO(text))
+        if g is None:
+            return None
+        
+        b, out, n = g.board(), [], 1
+        for mv in g.mainline_moves():
+            if b.turn == chess.WHITE and self._include_nums:
+                out += list(str(n)) + (
+                    ["..."] if self._include_black_ellipses and b.fullmove_number < n else ["."]
+                )
+            
+            if b.is_castling(mv):
+                b.push(mv)
+                suf = "#" if b.is_checkmate() else ("+" if b.is_check() else "")
+                b.pop()
+                out.append("O-O" if chess.square_file(mv.to_square) == 6 else "O-O-O")
+                if suf:
+                    out.append(suf)
+                b.push(mv)
+            else:
+                piece = b.piece_at(mv.from_square).symbol().upper()
+                frm = chess.square_name(mv.from_square)
+                to = chess.square_name(mv.to_square)
+                is_cap = b.is_capture(mv)
+                promo = mv.promotion
+                
+                b.push(mv)
+                suf = "#" if b.is_checkmate() else ("+" if b.is_check() else "")
+                
+                # Emit LAN tokens
+                out.append(piece)
+                out.append(frm)
+                if is_cap:
+                    out.append("x")
+                out.append(to)
+                if promo:
+                    out += ["=", chess.piece_symbol(promo).upper()]
+                if suf:
+                    out.append(suf)
+            
+            if b.turn == chess.WHITE:
+                n += 1
+        
+        res = g.headers.get("Result")
+        if self._keep_res and res in _RESULT:
+            out.append(res)
+        
+        return out
+    
+    def _lan_move_to_tokens(self, move: str) -> List[str]:
+        """
+        Convert a single LAN move to tokens.
+        
+        LAN format: [Piece][from_square][x]?[to_square][=Promo]?[+#]?
+        
+        Examples:
+            "Ng1f3" -> ["N", "g1", "f3"]
+            "Nd4xe6" -> ["N", "d4", "x", "e6"]
+            "Pe2e4" -> ["P", "e2", "e4"]
+            "Pe4xd5" -> ["P", "e4", "x", "d5"]
+            "O-O" -> ["O-O"]
+            "O-O-O" -> ["O-O-O"]
+            "Pe7e8=Q" -> ["P", "e7", "e8", "=", "Q"]
+            "Ng1f3+" -> ["N", "g1", "f3", "+"]
+        """
+        # Handle castling
+        if move in {"O-O", "O-O-O"}:
+            return [move]
+        if move.rstrip("+#") in {"O-O", "O-O-O"}:
+            base = move.rstrip("+#")
+            suffix = move[len(base):]
+            return [base] + ([suffix] if suffix else [])
+        
+        out = []
+        i = 0
+        n = len(move)
+        
+        # Get piece letter (required in LAN format)
+        if i < n and move[i] in "KQRBNP":
+            out.append(move[i])
+            i += 1
+        else:
+            # No piece letter - might be malformed, return as-is
+            return [move]
+        
+        # Get from square (required in LAN format)
+        if i + 1 < n and move[i] in FILES and move[i + 1] in RANKS:
+            out.append(move[i:i+2])
+            i += 2
+        
+        # Handle capture
+        if i < n and move[i] == "x":
+            out.append("x")
+            i += 1
+        
+        # Get to square (required in LAN format)
+        if i + 1 < n and move[i] in FILES and move[i + 1] in RANKS:
+            out.append(move[i:i+2])
+            i += 2
+        
+        # Handle promotion
+        if i < n and move[i] == "=":
+            out.append("=")
+            i += 1
+            if i < n and move[i] in PROMOS:
+                out.append(move[i])
+                i += 1
+        
+        # Handle check/checkmate
+        if i < n and move[i] in "+#":
+            out.append(move[i])
+            i += 1
+        
+        return out
+    
+    def _active_env_tokens(self) -> set:
+        """Return the set of env tokens that are active for this instance."""
+        return set(ENV_TOKENS) if self._include_env_tokens else set()
+
+    def _cot_to_tokens(self, text: str) -> List[str]:
+        """
+        Convert CoT formatted text to tokens.
+        Handles special tokens and LAN moves.
+        """
+        env_toks = self._active_env_tokens()
+        out = []
+        for token in text.split():
+            if token in {self.T, self.T_END, self.SEP} or token in env_toks:
+                # Keep special tokens as-is
+                out.append(token)
+            elif token in _RESULT:
+                # Game result
+                out.append(token)
+            elif token and token[0].isdigit() and "." in token:
+                # Move number like "1." or "15..."
+                # Split into digits and dots
+                num_part = token.rstrip(".")
+                dot_part = token[len(num_part):]
+                out.extend(list(num_part))
+                if dot_part:
+                    out.append("..." if len(dot_part) > 1 else ".")
+            elif token and all(c.isdigit() for c in token):
+                # Pure number - tokenize each digit
+                out.extend(list(token))
+            else:
+                # LAN move - tokenize it
+                out.extend(self._lan_move_to_tokens(token))
+        return out
+    
+    def encode(self, text: str) -> List[int]:
+        """
+        Encode text to token IDs.
+        
+        Args:
+            text: Text to encode (can be PGN or CoT formatted)
+        
+        Returns:
+            List of token IDs
+        """
+        # Check if this is CoT-formatted text (contains special tokens)
+        sft_special = (
+            [self.T, self.T_END, self.SEP]
+            + (ENV_TOKENS if self._include_env_tokens else [])
+        )
+        is_cot_format = any(token in text for token in sft_special)
+        
+        if is_cot_format:
+            t_idx = text.index(self.T)
+            prompt_part = text[:t_idx].strip()
+            rest_part = text[t_idx:]  # starts with <T>
+
+            pgn_tokens = self._pgn_to_tokens(prompt_part) if prompt_part else None
+            if pgn_tokens is None:
+                pgn_tokens = self._cot_to_tokens(prompt_part) if prompt_part else []
+            rest_tokens = self._cot_to_tokens(rest_part)
+            tokens = [self._bos] + pgn_tokens + rest_tokens + [self._eos]
+        else:
+            pgn_tokens = self._pgn_to_tokens(text)
+            if pgn_tokens is not None and len(pgn_tokens) > 0:
+                tokens = [self._bos] + pgn_tokens + [self._eos]
+            else:
+                # Not valid PGN — treat each word as a LAN move
+                lan_tokens = []
+                for word in text.split():
+                    lan_tokens.extend(self._lan_move_to_tokens(word))
+                tokens = [self._bos] + lan_tokens + [self._eos]
+        
+        return self.tk.encode(" ".join(tokens)).ids
+    
+    def decode(self, ids: List[int]) -> str:
+        """
+        Decode token IDs to text.
+        
+        Args:
+            ids: List of token IDs
+        
+        Returns:
+            Decoded text
+        """
+        toks = [t for t in self.tk.decode(ids).split() if t not in {self._bos, self._eos}]
+        
+        # Otherwise, use LAN decoding logic
+        out: List[str] = []
+        i, n = 0, len(toks)
+        
+        while i < n:
+            t = toks[i]
+            
+            if t in {self.T, self.T_END, self.SEP} or t in _RESULT or t in self._active_env_tokens():
+                out.append(t)
+                i += 1
+                continue
+            
+            if t and all(ch in DIGITS for ch in t):
+                j = i
+                num = []
+                while j < n and all(ch in DIGITS for ch in toks[j]):
+                    num.append(toks[j])
+                    j += 1
+                dots = ""
+                if j < n and toks[j] in {".", "..."}:
+                    dots = toks[j]
+                    j += 1
+                out.append("".join(num) + dots)
+                i = j
+                continue
+            
+            if t in {"O-O", "O-O-O"}:
+                j = i + 1
+                suf = toks[j] if j < n and toks[j] in {"+", "#"} else ""
+                if suf:
+                    j += 1
+                out.append(t + suf)
+                i = j
+                continue
+            
+            if t in set("KQRBNP"):
+                piece = t
+                j = i + 1
+                frm = toks[j] if j < n else ""
+                j += 1
+                cap = ""
+                if j < n and toks[j] == "x":
+                    cap = "x"
+                    j += 1
+                to = toks[j] if j < n else ""
+                j += 1
+                promo = ""
+                if j + 1 <= n - 1 and toks[j] == "=" and toks[j + 1] in set(PROMOS):
+                    promo = "=" + toks[j + 1]
+                    j += 2
+                suf = ""
+                if j < n and toks[j] in {"+", "#"}:
+                    suf = toks[j]
+                    j += 1
+                lan = f"{piece}{frm}{cap}{to}{promo}{suf}"
+                out.append(lan)
+                i = j
+                continue
+            
+            out.append(t)
+            i += 1
+        
+        return " ".join(out)
+    
+    def get_vocab(self) -> Dict[str, int]:
+        """Get token-to-id vocabulary mapping."""
+        return self._tok2id
+    
+    def bos_id(self) -> Optional[int]:
+        """Get BOS token ID."""
+        return self._tok2id[self._bos]
+    
+    def eos_id(self) -> Optional[int]:
+        """Get EOS token ID."""
+        return self._tok2id[self._eos]
+    
+    def pad_id(self) -> Optional[int]:
+        """Get PAD token ID (uses BOS as pad by default)."""
+        return self._tok2id.get("<pad>", self.bos_id())
+    
+    def get_vocab_size(self) -> int:
+        """Get vocabulary size."""
+        return len(self._tok2id)
+    
+    def t_id(self) -> int:
+        """Get <T> token ID."""
+        return self._tok2id[self.T]
+    
+    def sep_id(self) -> int:
+        """Get <sep> token ID."""
+        return self._tok2id[self.SEP]
+    
+    def t_end_id(self) -> int:
+        """Get </T> token ID."""
+        return self._tok2id[self.T_END]
+    
+    # ------------------------------------------------------------------
+    # Environment / reward token accessors
+    # ------------------------------------------------------------------
+
+    def _require_env_tokens(self) -> None:
+        if not self._include_env_tokens:
+            raise ValueError(
+                "Environment tokens are not enabled. "
+                "Pass include_env_tokens=True in the config."
+            )
+
+    def call_env_id(self) -> int:
+        """Get <call_env> token ID."""
+        self._require_env_tokens()
+        return self._tok2id[CALL_ENV_TOKEN]
+
+    def verify_id(self) -> int:
+        """Get <verify> token ID."""
+        self._require_env_tokens()
+        return self._tok2id[VERIFY_TOKEN]
+
+    def reward_pos_id(self) -> int:
+        """Get <+1> (positive reward) token ID."""
+        self._require_env_tokens()
+        return self._tok2id[REWARD_POS_TOKEN]
+
+    def reward_neg_id(self) -> int:
+        """Get <-1> (negative reward) token ID."""
+        self._require_env_tokens()
+        return self._tok2id[REWARD_NEG_TOKEN]
+
+    def reward_zero_id(self) -> int:
+        """Get <0> (zero reward) token ID."""
+        self._require_env_tokens()
+        return self._tok2id[REWARD_ZERO_TOKEN]
+
+    def reward_id(self, value) -> int:
+        """
+        Get reward token ID by numeric value.
+
+        Args:
+            value: 1, -1, or 0  (or the strings "+1", "-1", "0")
+
+        Returns:
+            Token ID for the corresponding reward token.
+        """
+        self._require_env_tokens()
+        mapping = {1: REWARD_POS_TOKEN, -1: REWARD_NEG_TOKEN, 0: REWARD_ZERO_TOKEN,
+                   "+1": REWARD_POS_TOKEN, "-1": REWARD_NEG_TOKEN, "0": REWARD_ZERO_TOKEN}
+        if value not in mapping:
+            raise ValueError(f"reward value must be one of 1, -1, 0 (or '+1', '-1', '0'), got {value!r}")
+        return self._tok2id[mapping[value]]
+
+    def env_token_ids(self) -> Dict[str, int]:
+        """Get mapping of all env/reward special tokens to their IDs."""
+        self._require_env_tokens()
+        return {tok: self._tok2id[tok] for tok in ENV_TOKENS}
+    
+    def extract_parts(self, text: str) -> Tuple[Optional[str], Optional[List[str]], str]:
+        """
+        Extract prompt, trajectories and answer from BoN CoT formatted text.
+        
+        Args:
+            text: Text in format: {prompt} <T> <sep> {traj1} <sep> ... <sep> <T> {answer}
+        
+        Returns:
+            prompt: The prompt/context (or None if not present)
+            trajectories: List of trajectory strings (or None if not present)
+            answer: The final answer
+        """
+        if self.T not in text:
+            return None, None, text
+        
+        try:
+            # Split by <T> to get prompt, thinking section, and answer
+            t_parts = text.split(self.T)
+            if len(t_parts) < 3:
+                return None, None, text
+            
+            # t_parts[0] is prompt (before first <T>)
+            # t_parts[1] is the thinking section with trajectories
+            # t_parts[2] is the answer
+            prompt = t_parts[0].strip() if t_parts[0].strip() else None
+            thinking_section = t_parts[1].strip()
+            answer = t_parts[2].strip()
+            
+            # Split thinking section by <sep> to get trajectories
+            trajectories = [t.strip() for t in thinking_section.split(self.SEP) if t.strip()]
+            
+            return prompt, trajectories, answer
+        except (ValueError, IndexError):
+            return None, None, text
+    
+    def extract_thinking_and_answer(self, text: str) -> Tuple[Optional[List[str]], str]:
+        """
+        Extract trajectories and answer from BoN CoT formatted text (ignores prompt).
+        
+        Args:
+            text: Text in format: {prompt} <T> <sep> {traj1} <sep> ... <sep> <T> {answer}
+        
+        Returns:
+            trajectories: List of trajectory strings (or None if not present)
+            answer: The final answer
+        """
+        _, trajectories, answer = self.extract_parts(text)
+        return trajectories, answer
+    
+    def get_sft_special_tokens(self) -> List[str]:
+        """Get list of SFT special tokens (including env/reward tokens if enabled)."""
+        toks = [self.T, self.T_END, self.SEP]
+        if self._include_env_tokens:
+            toks += ENV_TOKENS
+        return toks
+
+    def get_sft_token_ids(self) -> Dict[str, int]:
+        """Get mapping of SFT special tokens to their IDs."""
+        result = {
+            self.T: self._tok2id[self.T],
+            self.T_END: self._tok2id[self.T_END],
+            self.SEP: self._tok2id[self.SEP],
+        }
+        if self._include_env_tokens:
+            for tok in ENV_TOKENS:
+                result[tok] = self._tok2id[tok]
+        return result
+    
+    def parse_cot_line(self, line: str) -> Tuple[Optional[List[str]], Optional[str]]:
+        """
+        Parse a CoT data line in format: <T> <sep> ... <sep> <T> {answer}
+        
+        Args:
+            line: A line from the CoT data file
+        
+        Returns:
+            trajectories: List of trajectory strings
+            answer: The final answer/move
+        """
+        line = line.strip()
+        if not line or not line.startswith(self.T):
+            return None, None
+        
+        return self.extract_thinking_and_answer(line)
+
+# ============================================================
+# HuggingFace-compatible wrapper (auto-generated)
+# ============================================================
+import json as _json
+from pathlib import Path as _Path
+from transformers import PreTrainedTokenizer
+import torch
+from transformers.tokenization_utils_base import BatchEncoding
+
+from huggingface_hub import hf_hub_download
+
+class HFTokenizerWrapper(PreTrainedTokenizer):
+    def __init__(self, model_max_length=2048, **kwargs):
+        # These are usually provided by from_pretrained
+        repo_id = kwargs.get("name_or_path") or kwargs.get("_name_or_path")
+        revision = kwargs.get("revision", None)
+
+        if not repo_id or "/" not in str(repo_id):
+            # Fallback: user may pass repo_id explicitly
+            repo_id = kwargs.get("repo_id", None)
+        if not repo_id:
+            raise ValueError("Cannot infer repo_id; pass repo_id=... or ensure name_or_path is set.")
+
+        import os
+        if os.path.isdir(repo_id):
+            vocab_path = os.path.join(repo_id, "vocab.json")
+            cfg_path   = os.path.join(repo_id, "tokenizer_config.json")
+        else:
+            vocab_path = hf_hub_download(repo_id=repo_id, filename="vocab.json", revision=revision)
+            cfg_path   = hf_hub_download(repo_id=repo_id, filename="tokenizer_config.json", revision=revision)
+
+        with open(vocab_path, "r", encoding="utf-8") as _f:
+            saved_vocab = _json.load(_f)
+        with open(cfg_path, "r", encoding="utf-8") as _f:
+            _tok_cfg = _json.load(_f)
+
+        lan_config = _tok_cfg.get("lan_config", {})
+        lan_class_name = _tok_cfg.get("lan_tokenizer_class", "LanTokenizerSFT")
+
+        _cls = globals()[lan_class_name]
+        custom_tokenizer = _cls(config=lan_config)
+
+        # Override vocab with the saved vocab
+        custom_tokenizer._tok2id = saved_vocab
+        from tokenizers import Tokenizer as _TkTokenizer
+        from tokenizers.models import WordLevel as _WordLevel
+        from tokenizers.pre_tokenizers import WhitespaceSplit as _WhitespaceSplit
+        custom_tokenizer.tk = _TkTokenizer(_WordLevel(vocab=saved_vocab, unk_token=custom_tokenizer._unk))
+        custom_tokenizer.tk.pre_tokenizer = _WhitespaceSplit()
+
+        self.custom_tokenizer = custom_tokenizer
+        self._vocab = dict(saved_vocab)
+        self._id_to_token = {i: t for t, i in self._vocab.items()}
+
+        bos_token = _tok_cfg.get("bos_token")
+        eos_token = _tok_cfg.get("eos_token")
+        pad_token = _tok_cfg.get("pad_token")
+        unk_token = _tok_cfg.get("unk_token")
+        env_token = _tok_cfg.get("env_token")
+        if "env_id" in _tok_cfg:
+            env_token = self._id_to_token[_tok_cfg.get("env_id")]
+        else:
+            env_token = _tok_cfg.get("env_token")
+        self.env_token = env_token
+
+        for _key in ("bos_token","eos_token","pad_token","unk_token","env_token",
+                     "model_max_length","name_or_path","lan_config",
+                     "lan_tokenizer_class","tokenizer_class","auto_map","use_fast",
+                     "revision","repo_id"):
+            kwargs.pop(_key, None)
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            unk_token=unk_token,
+            model_max_length=model_max_length,
+            **kwargs,
+        )
+
+    # ---- PreTrainedTokenizer interface ----
+
+    @property
+    def vocab_size(self):
+        return len(self._vocab)
+
+    def get_vocab(self):
+        return dict(self._vocab)
+
+    def _tokenize(self, text):
+        return []  # we override encode/decode directly
+
+    def _convert_token_to_id(self, token):
+        return self._vocab.get(token, self._vocab.get(self.unk_token, 0))
+
+    def _convert_id_to_token(self, index):
+        return self._id_to_token.get(index, self.unk_token or "")
+
+    def convert_tokens_to_string(self, tokens):
+        ids = [self._convert_token_to_id(t) for t in tokens]
+        return self.custom_tokenizer.decode(ids)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if token_ids_1 is None:
+            return token_ids_0
+        return token_ids_0 + token_ids_1
+
+    def encode(self, text, add_special_tokens=True, **kwargs):
+        ids = self.custom_tokenizer.encode(text)
+        if add_special_tokens:
+            return ids[:-1]  # strip trailing EOS; vLLM adds its own
+        if (len(ids) >= 2
+                and self.bos_token_id is not None
+                and self.eos_token_id is not None
+                and ids[0] == self.bos_token_id
+                and ids[-1] == self.eos_token_id):
+            return ids[1:-1]
+        return ids
+
+    def decode(self, token_ids, skip_special_tokens=True, **kwargs):
+        import numpy as np
+        if isinstance(token_ids, torch.Tensor):
+            token_ids = token_ids.detach().cpu().tolist()
+        elif isinstance(token_ids, np.ndarray):
+            token_ids = token_ids.tolist()
+        return self.custom_tokenizer.decode(token_ids)
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        save_directory = _Path(save_directory)
+        save_directory.mkdir(parents=True, exist_ok=True)
+        vocab_file = 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 (str(vocab_file),)
+
+    def __call__(
+        self,
+        text,
+        text_pair=None,
+        add_special_tokens=True,
+        truncation=False,
+        max_length=None,
+        padding=False,
+        return_tensors=None,
+        **kwargs,
+    ):
+        if text_pair is not None:
+            raise ValueError("text_pair not supported for this tokenizer.")
+
+        # Normalize to batch
+        is_batched = isinstance(text, (list, tuple))
+        texts = list(text) if is_batched else [text]
+
+        input_ids = [self.encode(t, add_special_tokens=add_special_tokens) for t in texts]
+
+        # Truncation
+        if truncation and max_length is not None:
+            if self.truncation_side == "left":
+                input_ids = [ids[-max_length:] for ids in input_ids]
+            else:
+                input_ids = [ids[:max_length] for ids in input_ids]
+
+        # Attention masks (pre-padding)
+        attention_mask = [[1] * len(ids) for ids in input_ids]
+
+        # Padding
+        if padding:
+            if padding == "max_length":
+                if max_length is None:
+                    raise ValueError("padding='max_length' requires max_length.")
+                pad_to = max_length
+            else:
+                pad_to = max(len(ids) for ids in input_ids) if input_ids else 0
+
+            pad_id = self.pad_token_id
+            if pad_id is None:
+                pad_id = self.bos_token_id if self.bos_token_id is not None else 0
+
+            for i, ids in enumerate(input_ids):
+                pad_len = pad_to - len(ids)
+                if pad_len > 0:
+                    input_ids[i] = ids + [pad_id] * pad_len
+                    attention_mask[i] = attention_mask[i] + [0] * pad_len
+
+        data = {"input_ids": input_ids, "attention_mask": attention_mask}
+
+        # Unbatch if single example and no tensor return
+        if not is_batched and return_tensors is None:
+            data = {"input_ids": data["input_ids"][0], "attention_mask": data["attention_mask"][0]}
+
+        # Tensors
+        if return_tensors == "pt":
+            data = {k: torch.tensor(v, dtype=torch.long) for k, v in data.items()}
+
+        return BatchEncoding(data, tensor_type=None)
+
+
+__all__ = ["HFTokenizerWrapper"]

C6p5e18_200m_alpha0.200_beta0.100/tokenizer_config.json

@@ -0,0 +1,26 @@
+{
+  "tokenizer_class": "HFTokenizerWrapper",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.HFTokenizerWrapper",
+      null
+    ]
+  },
+  "model_max_length": 2048,
+  "bos_token": "<bos>",
+  "eos_token": "<eos>",
+  "pad_token": "<bos>",
+  "unk_token": "<unk>",
+  "env_token": null,
+  "use_fast": false,
+  "lan_config": {
+    "name": "LanTokenizerSFT",
+    "include_move_numbers": false,
+    "include_black_tripledots": false,
+    "bos": "<bos>",
+    "eos": "<eos>",
+    "unk": "<unk>",
+    "keep_result": false
+  },
+  "lan_tokenizer_class": "LanTokenizerSFT"
+}

C6p5e18_200m_alpha0.200_beta0.100/training_state.json

@@ -0,0 +1 @@
+{"step": 385, "epoch": 2}

C6p5e18_200m_alpha0.200_beta0.100/vocab.json

@@ -0,0 +1,86 @@
+{
+  "<bos>": 0,
+  "<eos>": 1,
+  "<unk>": 2,
+  "K": 3,
+  "Q": 4,
+  "R": 5,
+  "B": 6,
+  "N": 7,
+  "P": 8,
+  "a1": 9,
+  "a2": 10,
+  "a3": 11,
+  "a4": 12,
+  "a5": 13,
+  "a6": 14,
+  "a7": 15,
+  "a8": 16,
+  "b1": 17,
+  "b2": 18,
+  "b3": 19,
+  "b4": 20,
+  "b5": 21,
+  "b6": 22,
+  "b7": 23,
+  "b8": 24,
+  "c1": 25,
+  "c2": 26,
+  "c3": 27,
+  "c4": 28,
+  "c5": 29,
+  "c6": 30,
+  "c7": 31,
+  "c8": 32,
+  "d1": 33,
+  "d2": 34,
+  "d3": 35,
+  "d4": 36,
+  "d5": 37,
+  "d6": 38,
+  "d7": 39,
+  "d8": 40,
+  "e1": 41,
+  "e2": 42,
+  "e3": 43,
+  "e4": 44,
+  "e5": 45,
+  "e6": 46,
+  "e7": 47,
+  "e8": 48,
+  "f1": 49,
+  "f2": 50,
+  "f3": 51,
+  "f4": 52,
+  "f5": 53,
+  "f6": 54,
+  "f7": 55,
+  "f8": 56,
+  "g1": 57,
+  "g2": 58,
+  "g3": 59,
+  "g4": 60,
+  "g5": 61,
+  "g6": 62,
+  "g7": 63,
+  "g8": 64,
+  "h1": 65,
+  "h2": 66,
+  "h3": 67,
+  "h4": 68,
+  "h5": 69,
+  "h6": 70,
+  "h7": 71,
+  "h8": 72,
+  "x": 73,
+  "=": 74,
+  "+": 75,
+  "#": 76,
+  "O-O": 77,
+  "O-O-O": 78,
+  ".": 79,
+  "...": 80,
+  "<T>": 81,
+  "</T>": 82,
+  "<sep>": 83
+}