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README.md

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+---
+language: en
+license: mit
+tags:
+  - text-generation
+  - causal-lm
+  - randygpt
+  - rust
+---
+
+# randyGPT — model-ds2
+
+A GPT-style language model trained from scratch in Rust on Project Gutenberg.
+
+## Model Details
+
+| | |
+|---|---|
+| Architecture | Transformer (causal LM) |
+| Parameters | 2.90M |
+| Layers | 12 |
+| Heads | 4 |
+| Embedding dim | 128 |
+| Context window | 256 tokens |
+| Vocab size | 2000 (BPE) |
+| Training iters | 7625 |
+| Best val loss | 4.1204 |
+
+## Training
+
+Trained on ~98MB of cleaned Project Gutenberg text (112 public domain books,
+v3 cleaning with Unicode normalization) with BPE-2000 tokenization,
+AdamW optimizer, cosine LR decay, ReduceLROnPlateau, dropout=0.1, and
+Metal GPU via Candle on Apple Silicon.
+
+## Usage
+
+```python
+from modeling_randygpt import RandyGPTConfig, RandyGPTForCausalLM
+from tokenizer_randygpt import RandyGPTTokenizer
+from safetensors.torch import load_file
+import torch
+
+# Load
+cfg   = RandyGPTConfig.from_pretrained("MonumentalSystems/randygpt-ds2")
+model = RandyGPTForCausalLM(cfg)
+state = load_file("model.safetensors")
+model.load_state_dict(state, strict=True)
+model.eval()
+
+tok = RandyGPTTokenizer.from_file("tokenizer.json")
+
+# Generate
+prompt  = "Once upon a time"
+ids     = torch.tensor([tok.encode(prompt)], dtype=torch.long)
+out_ids = model.generate_text(ids, max_new_tokens=200, temperature=0.8)
+print(tok.decode(out_ids[0].tolist()))
+```
+
+## Source
+
+Trained with [randyGPT](https://github.com/MonumentalSystems/RandyGPT) —
+a GPT implementation in Rust with Metal GPU acceleration.

config.json

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+{
+  "architectures": [
+    "RandyGPTForCausalLM"
+  ],
+  "model_type": "randygpt",
+  "auto_map": {
+    "AutoConfig": "modeling_randygpt.RandyGPTConfig",
+    "AutoModelForCausalLM": "modeling_randygpt.RandyGPTForCausalLM"
+  },
+  "vocab_size": 2000,
+  "n_embd": 128,
+  "n_head": 4,
+  "n_layer": 12,
+  "block_size": 256,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "trained_iters": 7625,
+  "best_val_loss": 4.120370864868164
+}

generation_config.json

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+{
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "max_new_tokens": 256,
+  "temperature": 0.8,
+  "top_p": 0.9,
+  "do_sample": true
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98b6e4967d1122252703f86ec1f51565448536b9d6a23537e32b0b2bf08f4ae9
+size 11623200

modeling_randygpt.py

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+"""
+modeling_randygpt.py — PyTorch implementation of the randyGPT architecture.
+
+Matches the Rust forward pass exactly:
+  - RMSNorm with no learnable parameters (x / rms(x))
+  - Multi-head causal self-attention (no bias, scaled dot-product)
+  - MLP with squared-ReLU activation and 4× expansion
+  - No final layer norm before lm_head
+  - Learned token + position embeddings
+
+Compatible with HuggingFace transformers via PreTrainedModel/PretrainedConfig.
+
+Usage (standalone):
+    from modeling_randygpt import RandyGPTConfig, RandyGPTForCausalLM
+    from safetensors.torch import load_file
+
+    cfg   = RandyGPTConfig.from_pretrained("path/to/model")
+    model = RandyGPTForCausalLM(cfg)
+    model.load_state_dict(load_file("path/to/model.safetensors"), strict=True)
+    model.eval()
+
+Usage (HuggingFace Hub):
+    model = RandyGPTForCausalLM.from_pretrained("username/randygpt-s")
+"""
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutput
+
+
+# ── Config ────────────────────────────────────────────────────────────────────
+
+class RandyGPTConfig(PretrainedConfig):
+    model_type = "randygpt"
+
+    def __init__(
+        self,
+        vocab_size: int = 1500,
+        n_embd:     int = 128,
+        n_head:     int = 4,
+        n_layer:    int = 8,
+        block_size: int = 256,
+        bos_token_id: int = 0,
+        eos_token_id: int = 1,
+        **kwargs,
+    ):
+        super().__init__(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_head     = n_head
+        self.n_layer    = n_layer
+        self.block_size = block_size
+        self.head_dim   = n_embd // n_head
+        self.mlp_dim    = 4 * n_embd
+
+
+# ── Modules ───────────────────────────────────────────────────────────────────
+
+def rmsnorm(x: torch.Tensor, eps: float = 1e-5) -> torch.Tensor:
+    """RMSNorm with no learnable scale — matches Rust rmsnorm_fwd exactly."""
+    return x * (x.pow(2).mean(-1, keepdim=True) + eps).rsqrt()
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, cfg: RandyGPTConfig):
+        super().__init__()
+        self.n_head   = cfg.n_head
+        self.head_dim = cfg.head_dim
+        self.n_embd   = cfg.n_embd
+        self.scale    = 1.0 / math.sqrt(cfg.head_dim)
+
+        # No bias — matches Rust linear_fwd (pure matmul)
+        self.wq = nn.Linear(cfg.n_embd, cfg.n_embd, bias=False)
+        self.wk = nn.Linear(cfg.n_embd, cfg.n_embd, bias=False)
+        self.wv = nn.Linear(cfg.n_embd, cfg.n_embd, bias=False)
+        self.wo = nn.Linear(cfg.n_embd, cfg.n_embd, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, T, C = x.shape
+
+        q = self.wq(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)  # [B,H,T,Dh]
+        k = self.wk(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        v = self.wv(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+
+        # Causal attention scores
+        scores = q @ k.transpose(-2, -1) * self.scale  # [B,H,T,T]
+        mask   = torch.full((T, T), float('-inf'), device=x.device).triu(1)
+        scores = scores + mask
+
+        attn   = F.softmax(scores, dim=-1)
+        out    = attn @ v                                         # [B,H,T,Dh]
+        out    = out.transpose(1, 2).contiguous().view(B, T, C)  # [B,T,D]
+        return self.wo(out)
+
+
+class MLP(nn.Module):
+    def __init__(self, cfg: RandyGPTConfig):
+        super().__init__()
+        self.fc1 = nn.Linear(cfg.n_embd,   cfg.mlp_dim, bias=False)
+        self.fc2 = nn.Linear(cfg.mlp_dim,  cfg.n_embd,  bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.fc1(x)
+        h = F.relu(h).pow(2)   # squared ReLU — matches Rust exactly
+        return self.fc2(h)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, cfg: RandyGPTConfig):
+        super().__init__()
+        self.attn = CausalSelfAttention(cfg)
+        self.mlp  = MLP(cfg)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(rmsnorm(x))   # pre-norm attention + residual
+        x = x + self.mlp(rmsnorm(x))    # pre-norm MLP + residual
+        return x
+
+
+# ── Full model ────────────────────────────────────────────────────────────────
+
+class RandyGPTForCausalLM(PreTrainedModel):
+    config_class = RandyGPTConfig
+
+    def __init__(self, cfg: RandyGPTConfig):
+        super().__init__(cfg)
+        self.wte     = nn.Embedding(cfg.vocab_size, cfg.n_embd)
+        self.wpe     = nn.Embedding(cfg.block_size, cfg.n_embd)
+        self.layers  = nn.ModuleList([TransformerBlock(cfg) for _ in range(cfg.n_layer)])
+        self.lm_head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
+        # No final layer norm — matches Rust forward pass
+
+    def forward(
+        self,
+        input_ids:     torch.Tensor,          # [B, T]
+        labels:        torch.Tensor = None,   # [B, T] for loss
+        **kwargs,
+    ) -> CausalLMOutput:
+        B, T = input_ids.shape
+        assert T <= self.config.block_size, f"Sequence length {T} > block_size {self.config.block_size}"
+
+        pos = torch.arange(T, device=input_ids.device).unsqueeze(0)  # [1, T]
+        x   = self.wte(input_ids) + self.wpe(pos)                    # [B, T, D]
+
+        for block in self.layers:
+            x = block(x)
+
+        logits = self.lm_head(x)  # [B, T, V] — no final norm, matches Rust
+
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(
+                logits.view(-1, self.config.vocab_size),
+                labels.view(-1),
+                ignore_index=-100,
+            )
+
+        return CausalLMOutput(loss=loss, logits=logits)
+
+    @torch.no_grad()
+    def generate_text(
+        self,
+        prompt_ids:  torch.Tensor,   # [1, T] — already tokenized
+        max_new_tokens: int = 200,
+        temperature:    float = 0.8,
+        top_p:          float = 0.9,
+    ) -> torch.Tensor:
+        """Simple top-p nucleus sampling. Returns full sequence including prompt."""
+        self.eval()
+        ids = prompt_ids.clone()
+
+        for _ in range(max_new_tokens):
+            ctx   = ids[:, -self.config.block_size:]
+            out   = self(ctx)
+            logits = out.logits[:, -1, :] / temperature  # [1, V]
+
+            # Top-p (nucleus) sampling
+            probs = F.softmax(logits, dim=-1)
+            sorted_probs, sorted_idx = torch.sort(probs, descending=True)
+            cumprobs = sorted_probs.cumsum(dim=-1)
+            mask     = cumprobs - sorted_probs > top_p
+            sorted_probs[mask] = 0.0
+            sorted_probs /= sorted_probs.sum()
+            next_id = sorted_idx[0, torch.multinomial(sorted_probs[0], 1)]
+            ids     = torch.cat([ids, next_id.view(1, 1)], dim=1)
+
+        return ids

tokenizer.json

@@ -0,0 +1,6462 @@
+{
+  "vocab": [
+    "<|bos|>",
+    "<|eos|>",
+    "\t",
+    "\n",
+    " ",
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
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+    "'",
+    "(",
+    ")",
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+    "+",
+    ",",
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+    "F",
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+    "H",
+    "I",
+    "J",
+    "K",
+    "L",
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+    "Q",
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+    "S",
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+    "]",
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+    "|",
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+    "©",
+    "«",
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+    "½",
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+    "Ä",
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+    "Ç",
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+    "Ñ",
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+    [
+      "for",
+      "\n"
+    ],
+    [
+      " ha",
+      "v"
+    ],
+    [
+      " a",
+      "ll "
+    ],
+    [
+      "ent",
+      "ly "
+    ],
+    [
+      "m",
+      "uch"
+    ],
+    [
+      "ri",
+      "ed "
+    ],
+    [
+      "of",
+      " my "
+    ],
+    [
+      "l",
+      "augh"
+    ],
+    [
+      "s",
+      "aw"
+    ],
+    [
+      "m",
+      "ent "
+    ],
+    [
+      "is",
+      ", "
+    ],
+    [
+      "sel",
+      "v"
+    ],
+    [
+      "r",
+      "ound "
+    ],
+    [
+      "up",
+      "on the "
+    ],
+    [
+      "h",
+      "and "
+    ],
+    [
+      "l",
+      "ed "
+    ],
+    [
+      "r",
+      "y "
+    ],
+    [
+      "he",
+      "ad "
+    ],
+    [
+      "s",
+      "k"
+    ],
+    [
+      "turn",
+      "ed "
+    ],
+    [
+      "a",
+      " w"
+    ],
+    [
+      " which",
+      " "
+    ],
+    [
+      "dr",
+      "aw"
+    ],
+    [
+      "E",
+      "N"
+    ],
+    [
+      "th",
+      "e"
+    ],
+    [
+      "k",
+      "ing "
+    ],
+    [
+      "on",
+      ", "
+    ],
+    [
+      "oul",
+      "d\n"
+    ],
+    [
+      "n",
+      "ess"
+    ],
+    [
+      "t",
+      "ing"
+    ],
+    [
+      "ch",
+      "ang"
+    ],
+    [
+      "y",
+      ", and "
+    ],
+    [
+      "c",
+      "lo"
+    ],
+    [
+      "al",
+      "ways "
+    ],
+    [
+      "to s",
+      "ee "
+    ],
+    [
+      "you",
+      "\n"
+    ],
+    [
+      "i",
+      "x"
+    ],
+    [
+      "to",
+      " his "
+    ],
+    [
+      "an",
+      "other"
+    ],
+    [
+      "f",
+      "ind "
+    ],
+    [
+      "L",
+      "ORD"
+    ],
+    [
+      "se",
+      "e "
+    ],
+    [
+      ",”",
+      " he "
+    ],
+    [
+      "as",
+      "ter"
+    ],
+    [
+      "s",
+      "or"
+    ],
+    [
+      "dou",
+      "b"
+    ],
+    [
+      "s",
+      "up"
+    ],
+    [
+      "b",
+      "us"
+    ],
+    [
+      "per",
+      "son"
+    ],
+    [
+      "ne",
+      "x"
+    ],
+    [
+      "T",
+      "ER"
+    ],
+    [
+      "no",
+      "thing"
+    ],
+    [
+      "bod",
+      "y "
+    ],
+    [
+      "a",
+      "it"
+    ],
+    [
+      "it",
+      "ch"
+    ],
+    [
+      "ay",
+      ", "
+    ],
+    [
+      "th",
+      "en "
+    ],
+    [
+      "er",
+      "e\n"
+    ],
+    [
+      "ten",
+      "d"
+    ],
+    [
+      "lo",
+      "ve "
+    ],
+    [
+      ". ",
+      "‘"
+    ],
+    [
+      "t",
+      "ro"
+    ],
+    [
+      "es ",
+      "and "
+    ],
+    [
+      "n",
+      "am"
+    ],
+    [
+      "g",
+      "irl"
+    ],
+    [
+      "; ",
+      "“"
+    ],
+    [
+      "c",
+      "as"
+    ],
+    [
+      "plac",
+      "e "
+    ],
+    [
+      "T",
+      "his "
+    ],
+    [
+      "read",
+      "y "
+    ],
+    [
+      "b",
+      "es"
+    ],
+    [
+      "S",
+      "he "
+    ],
+    [
+      "m",
+      " "
+    ],
+    [
+      "gen",
+      "er"
+    ],
+    [
+      "s",
+      "ec"
+    ],
+    [
+      "p",
+      "ow"
+    ],
+    [
+      "a",
+      "ther"
+    ],
+    [
+      "n",
+      "ight "
+    ],
+    [
+      "w",
+      "as"
+    ],
+    [
+      "C",
+      "HA"
+    ],
+    [
+      "W",
+      "ith"
+    ],
+    [
+      "s ",
+      "were "
+    ],
+    [
+      "down",
+      " "
+    ],
+    [
+      "por",
+      "t"
+    ],
+    [
+      "him",
+      "self"
+    ],
+    [
+      " th",
+      "at"
+    ],
+    [
+      "I am",
+      " "
+    ],
+    [
+      "ce",
+      "iv"
+    ],
+    [
+      "sai",
+      "d"
+    ],
+    [
+      "st",
+      "and"
+    ],
+    [
+      "rem",
+      "ain"
+    ],
+    [
+      "w",
+      "ind"
+    ],
+    [
+      "si",
+      "on "
+    ],
+    [
+      "to",
+      "ge"
+    ],
+    [
+      "p",
+      " "
+    ],
+    [
+      " c",
+      "ould "
+    ],
+    [
+      "ent",
+      ", "
+    ],
+    [
+      "a",
+      "h"
+    ],
+    [
+      "u",
+      "f"
+    ],
+    [
+      ":",
+      "\n"
+    ],
+    [
+      "br",
+      "ought "
+    ],
+    [
+      "d",
+      "one "
+    ],
+    [
+      "er",
+      "e"
+    ],
+    [
+      "em",
+      "ber"
+    ],
+    [
+      "h",
+      "is\n"
+    ],
+    [
+      "ri",
+      "ght "
+    ],
+    [
+      "p",
+      "ic"
+    ],
+    [
+      "i",
+      "tion"
+    ],
+    [
+      "on",
+      "\n"
+    ],
+    [
+      "ex",
+      "pres"
+    ],
+    [
+      "de",
+      "ath"
+    ],
+    [
+      "v",
+      "e"
+    ],
+    [
+      "pec",
+      "t"
+    ],
+    [
+      "i",
+      "on "
+    ],
+    [
+      "all",
+      " the "
+    ],
+    [
+      "su",
+      "b"
+    ],
+    [
+      "t",
+      "ter "
+    ],
+    [
+      "in",
+      "de"
+    ],
+    [
+      "un",
+      "g"
+    ],
+    [
+      "hear",
+      "d "
+    ],
+    [
+      "l",
+      "aw"
+    ],
+    [
+      "s ",
+      "that "
+    ],
+    [
+      "u",
+      "ck"
+    ],
+    [
+      "T",
+      "H"
+    ],
+    [
+      "le",
+      "ep"
+    ],
+    [
+      "them",
+      " "
+    ],
+    [
+      "again",
+      "st "
+    ],
+    [
+      "!",
+      "”\n\n"
+    ],
+    [
+      "i",
+      "es "
+    ],
+    [
+      "the ",
+      "m"
+    ],
+    [
+      ". ",
+      "Wh"
+    ],
+    [
+      "on",
+      "d"
+    ],
+    [
+      "Q",
+      "u"
+    ],
+    [
+      "A",
+      "N"
+    ],
+    [
+      " m",
+      "uch"
+    ],
+    [
+      "th",
+      "ought "
+    ],
+    [
+      "d",
+      "uc"
+    ],
+    [
+      "ed ",
+      "her"
+    ],
+    [
+      "w",
+      "if"
+    ],
+    [
+      "l",
+      "in"
+    ],
+    [
+      "ques",
+      "tion"
+    ],
+    [
+      "on",
+      " his "
+    ],
+    [
+      "on",
+      "der"
+    ],
+    [
+      "le",
+      "d"
+    ],
+    [
+      "s",
+      "en"
+    ],
+    [
+      "P",
+      "r"
+    ],
+    [
+      "long",
+      " "
+    ],
+    [
+      "an",
+      "n"
+    ],
+    [
+      "ing ",
+      "and "
+    ],
+    [
+      "w",
+      "al"
+    ],
+    [
+      "sid",
+      "er"
+    ],
+    [
+      "m",
+      "ake "
+    ],
+    [
+      "h",
+      "ere "
+    ],
+    [
+      "v",
+      "al"
+    ]
+  ]
+}

tokenizer_config.json

@@ -0,0 +1,6 @@
+{
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "bos_token": "<|bos|>",
+  "eos_token": "<|eos|>",
+  "model_max_length": 256
+}

tokenizer_randygpt.py

@@ -0,0 +1,105 @@
+"""
+tokenizer_randygpt.py — Python port of the randyGPT BPE tokenizer.
+
+Matches the Rust BpeTokenizer encode/decode exactly:
+  - Encode: apply merges in priority order (lowest merged_id first)
+  - Decode: concatenate vocab[id] for each token, skipping bos/eos
+  - Splits on newlines before encoding (same as Rust rayon parallel path)
+
+Usage:
+    from tokenizer_randygpt import RandyGPTTokenizer
+
+    tok = RandyGPTTokenizer.from_file("vocab.json")
+    ids = tok.encode("Once upon a time")
+    txt = tok.decode(ids)
+"""
+
+import json
+from pathlib import Path
+from typing import List, Dict, Tuple
+
+
+class RandyGPTTokenizer:
+    def __init__(
+        self,
+        vocab:     List[str],
+        merges:    List[Tuple[str, str]],
+    ):
+        self.vocab      = vocab
+        self.merges     = merges
+        self.token_to_id: Dict[str, int] = {s: i for i, s in enumerate(vocab)}
+        self.bos_id     = self.token_to_id.get("<|bos|>", 0)
+        self.eos_id     = self.token_to_id.get("<|eos|>", 1)
+        self.vocab_size = len(vocab)
+
+        # (left_id, right_id) → merged_id — first insertion wins (highest priority)
+        self.merge_map: Dict[Tuple[int, int], int] = {}
+        for left, right in merges:
+            l = self.token_to_id.get(left)
+            r = self.token_to_id.get(right)
+            merged = self.token_to_id.get(left + right)
+            if l is not None and r is not None and merged is not None:
+                self.merge_map.setdefault((l, r), merged)
+
+    @classmethod
+    def from_file(cls, path: str) -> "RandyGPTTokenizer":
+        data = json.loads(Path(path).read_text(encoding="utf-8"))
+        vocab  = data["vocab"]
+        merges = [tuple(m) for m in data["merges"]]
+        return cls(vocab, merges)
+
+    def _encode_chunk(self, text: str) -> List[int]:
+        """Encode a single line (no newlines). Mirrors Rust encode_chunk."""
+        tokens = [self.token_to_id[c] for c in text if c in self.token_to_id]
+        if len(tokens) < 2:
+            return tokens
+
+        # Repeatedly apply the highest-priority merge (lowest merged_id)
+        while True:
+            best_merged_id = None
+            for i in range(len(tokens) - 1):
+                m = self.merge_map.get((tokens[i], tokens[i + 1]))
+                if m is not None:
+                    if best_merged_id is None or m < best_merged_id:
+                        best_merged_id = m
+
+            if best_merged_id is None:
+                break
+
+            out = []
+            i = 0
+            while i < len(tokens):
+                if (i + 1 < len(tokens) and
+                        self.merge_map.get((tokens[i], tokens[i + 1])) == best_merged_id):
+                    out.append(best_merged_id)
+                    i += 2
+                else:
+                    out.append(tokens[i])
+                    i += 1
+            tokens = out
+
+        return tokens
+
+    def encode(self, text: str) -> List[int]:
+        """Encode text. Splits on newlines matching Rust parallel encode path."""
+        nl_id = self.token_to_id.get("\n")
+        lines = text.split("\n")
+        result = []
+        for i, line in enumerate(lines):
+            result.extend(self._encode_chunk(line))
+            if i < len(lines) - 1 and nl_id is not None:
+                result.append(nl_id)
+        return result
+
+    def decode(self, ids: List[int], skip_special: bool = True) -> str:
+        """Decode token ids back to text."""
+        parts = []
+        for i in ids:
+            if skip_special and i in (self.bos_id, self.eos_id):
+                continue
+            if 0 <= i < len(self.vocab):
+                parts.append(self.vocab[i])
+        return "".join(parts)
+
+    def __len__(self) -> int:
+        return self.vocab_size