miles-tito-tokenizers 0.1.1

Standalone TITO (Token-In-Token-Out) tokenizers extracted from the miles project.

miles-tito-tokenizers

Standalone TITO (Token-In-Token-Out) tokenizers extracted from the miles project.

This package lets you reuse miles' chat-template / incremental-tokenization logic for multi-turn agentic rollout without importing the full miles training framework.

Attribution

• The TITO tokenizer design and implementation are derived from the miles project by the radixark/miles contributors.
• DeepSeek V3.2 and V4 encoders are vendored from SGLang under the Apache License 2.0.
• See ATTRIBUTION.md and LICENSE for details.

Install

pip install miles-tito-tokenizers

No sglang installation is required — DeepSeek encoders are vendored as pure Python.

Quick start

from transformers import AutoTokenizer
from miles_tito_tokenizers import get_tito_tokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")

tito = get_tito_tokenizer(
    tokenizer,
    tokenizer_type="qwen3",
    allowed_append_roles=["tool", "user"],
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"},
]

ids = tito.render_messages(messages, add_generation_prompt=True)
print(ids)

For DeepSeek models:

tito = get_tito_tokenizer(
    tokenizer,
    tokenizer_type="deepseekv32",
    allowed_append_roles=["tool"],
)

Migrating from transformers

miles_tito_tokenizers.apply_chat_template mirrors tokenizer.apply_chat_template(..., return_dict=False), which is the format used by SGLang and miles:

What you want	transformers call	miles_tito_tokenizers call
Rendered text	tokenizer.apply_chat_template(messages, tokenize=False)	mtt.apply_chat_template(messages, tokenizer=tokenizer)
Token IDs	tokenizer.apply_chat_template(messages, tokenize=True, return_dict=False)	mtt.apply_chat_template(messages, tokenizer=tokenizer, tokenize=True)
BatchEncoding	tokenizer.apply_chat_template(messages)	not supported by design

apply_chat_template takes tokenizer as a keyword-only argument so the intent is explicit.

Supported model families

tokenizer_type	Notes
qwen3	Qwen3
qwen35	Qwen3.5
qwennext	Qwen3-Thinking-2507 / Qwen3-Next-Thinking
glm47	GLM 4.7
nemotron3	NVIDIA Nemotron 3
kimi25	Moonshot Kimi K2.5
kimi26	Moonshot Kimi K2.6
minimax_m25	MiniMax-M2.5
minimax_m27	MiniMax-M2.7
deepseekv32	DeepSeek V3.2
deepseekv4	DeepSeek V4
default	HF-native chat template, no TITO merge logic

Validation scripts

Two CLI helpers from miles are included:

# CPU / fast: verify that rendered token sequences are append-only.
python -m miles_tito_tokenizers.scripts.verify_chat_template \
    --model Qwen/Qwen3-4B \
    --tito-model qwen3 \
    --tito-allowed-append-roles tool user

# GPU / e2e: verify against a real SGLang-served checkpoint.
python -m miles_tito_tokenizers.scripts.verify_session_tito_tokenizer \
    --hf-checkpoint Qwen/Qwen3-4B \
    --tito-model qwen3 \
    --tito-allowed-append-roles tool user \
    --sglang-reasoning-parser <rp> \
    --sglang-tool-call-parser <tcp> \
    --rollout-num-gpus-per-engine 1

Relationship to miles

miles-tito-tokenizers is an independent, personal-fan package that extracts a well-defined slice of miles. It is not affiliated with or endorsed by the miles team. If you are building inside miles itself, use the original miles.utils.chat_template_utils module.

License

• Package code derived from miles: see LICENSE for the miles project license.
• Vendored DeepSeek encoders from SGLang: see LICENSE-SGLANG-DEEPSEEKV32.md and LICENSE-SGLANG-DEEPSEEKV4.md.

Contributing

See CONTRIBUTING.md.