Text Generation
Transformers
Safetensors
Chinese
English
yunmo_next
chat
traditional-chinese
hybrid-attention
kimi-delta-attention
linear-attention
research
custom_code
Instructions to use yuhuanstudio/Yunmo-Next-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuhuanstudio/Yunmo-Next-1B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yuhuanstudio/Yunmo-Next-1B-Instruct", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("yuhuanstudio/Yunmo-Next-1B-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yuhuanstudio/Yunmo-Next-1B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yuhuanstudio/Yunmo-Next-1B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yuhuanstudio/Yunmo-Next-1B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yuhuanstudio/Yunmo-Next-1B-Instruct
- SGLang
How to use yuhuanstudio/Yunmo-Next-1B-Instruct 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 "yuhuanstudio/Yunmo-Next-1B-Instruct" \ --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": "yuhuanstudio/Yunmo-Next-1B-Instruct", "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 "yuhuanstudio/Yunmo-Next-1B-Instruct" \ --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": "yuhuanstudio/Yunmo-Next-1B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yuhuanstudio/Yunmo-Next-1B-Instruct with Docker Model Runner:
docker model run hf.co/yuhuanstudio/Yunmo-Next-1B-Instruct
File size: 13,423 Bytes
b901794 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 | """Versioned tokenizer protocol ABI shared by data, post-training and serving.
The tokenizer owns 256 atomic IDs at the top of its fixed 65,536-entry
vocabulary. Token strings and IDs are immutable after tokenizer freeze;
semantic use is versioned here so consumers cannot silently invent a second
mapping. Reserved Omni aliases intentionally keep their on-disk
``<|reserved_NNN|>`` strings: a structured processor splices their IDs and
projected features replace only the corresponding embeddings, without a
tokenizer or embedding resize.
"""
from __future__ import annotations
import hashlib
import json
from pathlib import Path
from typing import Any
from yunmo_next.protocol_tokens import (
ACTIVE_PROTOCOL_TOKENS,
PROTOCOL_SLOT_COUNT,
PROTOCOL_TOKENS,
)
CONTROL_BASE = 65_280
VOCAB_SIZE = 65_536
FINAL_TOKENIZER_SHA256 = (
"0d1b129c0e757043821030f8662d248f5d96d86a8199c9ad64bc698f14e71324"
)
TOKEN_TO_ID = {
token: CONTROL_BASE + offset for offset, token in enumerate(PROTOCOL_TOKENS)
}
ID_TO_TOKEN = {identifier: token for token, identifier in TOKEN_TO_ID.items()}
BOS = TOKEN_TO_ID["<|bos|>"]
EOS = TOKEN_TO_ID["<|eos|>"]
PAD = TOKEN_TO_ID["<|pad|>"]
UNK = TOKEN_TO_ID["<|unk|>"]
START = TOKEN_TO_ID["<|start|>"]
CHANNEL = TOKEN_TO_ID["<|channel|>"]
MESSAGE = TOKEN_TO_ID["<|message|>"]
END = TOKEN_TO_ID["<|end|>"]
RETURN = TOKEN_TO_ID["<|return|>"]
RECIPIENT = TOKEN_TO_ID["<|recipient|>"]
CALL = TOKEN_TO_ID["<|call|>"]
DOC_START = TOKEN_TO_ID["<|doc_start|>"]
DOC_END = TOKEN_TO_ID["<|doc_end|>"]
TEXT_ROLES = ("system", "developer", "user", "assistant", "tool")
TEXT_CHANNELS = ("analysis", "final")
# These aliases reserve stable embedding positions for a future modality
# adapter. Raw tokenizers can emit the literal reserved strings if they appear
# in text; production text encoders must reject resulting control IDs. The
# aliases are not audio-codebook IDs. A future Omni branch may replace the
# embedding at frame/patch positions with projected encoder features while
# keeping the text model vocabulary and LM head unchanged.
OMNI_RESERVED_ALIASES = {
"image_start": TOKEN_TO_ID["<|reserved_000|>"],
"image_patch": TOKEN_TO_ID["<|reserved_001|>"],
"image_end": TOKEN_TO_ID["<|reserved_002|>"],
"audio_input_start": TOKEN_TO_ID["<|reserved_003|>"],
"audio_input_frame": TOKEN_TO_ID["<|reserved_004|>"],
"audio_input_end": TOKEN_TO_ID["<|reserved_005|>"],
"audio_output_start": TOKEN_TO_ID["<|reserved_006|>"],
"audio_output_frame": TOKEN_TO_ID["<|reserved_007|>"],
"audio_output_end": TOKEN_TO_ID["<|reserved_008|>"],
"voice_reference_start": TOKEN_TO_ID["<|reserved_009|>"],
"voice_reference_frame": TOKEN_TO_ID["<|reserved_010|>"],
"voice_reference_end": TOKEN_TO_ID["<|reserved_011|>"],
}
REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
PRODUCTION_TOKENIZER_DIRECTORY = (
REPOSITORY_ROOT / "data/tokenizer/Yunmo_Next_Tokenizer"
)
DEFAULT_TOKENIZER = PRODUCTION_TOKENIZER_DIRECTORY / "tokenizer.json"
DEFAULT_PROTOCOL_ABI = (
PRODUCTION_TOKENIZER_DIRECTORY / "protocol_abi_v2.json"
)
PROTOCOL_ABI_TOP_LEVEL_KEYS = {
"format",
"tokenizer_sha256",
"vocabulary_size",
"lexical_vocabulary_size",
"protocol_slot_count",
"protocol_first_id",
"protocol_last_id",
"active_token_id_map",
"reserved_slot_scheme",
"text_message_protocol",
"omni_reserved_aliases",
"omni_embedding_protocol",
}
TEXT_ACTION_TOKENS = (
"<|bos|>",
"<|eos|>",
"<|pad|>",
"<|start|>",
"<|channel|>",
"<|message|>",
"<|end|>",
"<|return|>",
"<|recipient|>",
"<|call|>",
)
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(8 * 2**20), b""):
digest.update(chunk)
return digest.hexdigest()
def expected_token_map() -> dict[str, int]:
return dict(TOKEN_TO_ID)
def validate_tokenizer_protocol(tokenizer: Any) -> None:
size = int(tokenizer.get_vocab_size(with_added_tokens=True))
if size != VOCAB_SIZE:
raise RuntimeError(
f"tokenizer vocabulary changed: expected={VOCAB_SIZE} actual={size}"
)
mismatches = {
token: (identifier, tokenizer.token_to_id(token))
for token, identifier in TOKEN_TO_ID.items()
if tokenizer.token_to_id(token) != identifier
}
if mismatches:
first = next(iter(mismatches.items()))
raise RuntimeError(f"tokenizer protocol mapping changed: {first}")
def validate_tokenizer_file(path: Path) -> None:
resolved = path.expanduser().resolve(strict=True)
observed = sha256(resolved)
if observed != FINAL_TOKENIZER_SHA256:
raise RuntimeError(
"final tokenizer file identity changed: "
f"expected={FINAL_TOKENIZER_SHA256} actual={observed}"
)
def _strict_json_object(pairs: list[tuple[str, Any]]) -> dict[str, Any]:
value: dict[str, Any] = {}
for key, item in pairs:
if key in value:
raise ValueError(f"duplicate JSON key: {key}")
value[key] = item
return value
def _reject_json_constant(value: str) -> None:
raise ValueError(f"non-finite JSON number: {value}")
def _canonical_json(value: Any) -> bytes:
return json.dumps(
value,
ensure_ascii=False,
sort_keys=True,
separators=(",", ":"),
allow_nan=False,
).encode("utf-8")
def _normalize_string_set(value: Any, *, field: str) -> list[str]:
if (
not isinstance(value, list)
or any(type(item) is not str for item in value)
or len(set(value)) != len(value)
):
raise RuntimeError(f"tokenizer protocol ABI {field} is invalid")
return sorted(value)
def _normalize_active_token_map(value: Any) -> list[dict[str, Any]]:
if not isinstance(value, list):
raise RuntimeError("tokenizer protocol ABI active token map is invalid")
normalized = []
for item in value:
if (
not isinstance(item, dict)
or set(item) != {"id", "token"}
or type(item["id"]) is not int
or type(item["token"]) is not str
):
raise RuntimeError("tokenizer protocol ABI active token map is invalid")
normalized.append({"id": item["id"], "token": item["token"]})
if (
len({item["id"] for item in normalized}) != len(normalized)
or len({item["token"] for item in normalized}) != len(normalized)
):
raise RuntimeError("tokenizer protocol ABI active token map has duplicates")
return sorted(normalized, key=lambda item: (item["id"], item["token"]))
def _expected_protocol_abi() -> dict[str, Any]:
return {
"format": "yunmo-next-tokenizer-protocol-abi-v2",
"tokenizer_sha256": FINAL_TOKENIZER_SHA256,
"vocabulary_size": VOCAB_SIZE,
"lexical_vocabulary_size": CONTROL_BASE,
"protocol_slot_count": PROTOCOL_SLOT_COUNT,
"protocol_first_id": CONTROL_BASE,
"protocol_last_id": VOCAB_SIZE - 1,
"active_token_id_map": [
{"id": TOKEN_TO_ID[token], "token": token}
for token in ACTIVE_PROTOCOL_TOKENS
],
"reserved_slot_scheme": {
"count": PROTOCOL_SLOT_COUNT - len(ACTIVE_PROTOCOL_TOKENS),
"first_id": CONTROL_BASE + len(ACTIVE_PROTOCOL_TOKENS),
"last_id": VOCAB_SIZE - 1,
"token_format": "<|reserved_{index:03d}|>",
},
"text_message_protocol": {
"roles": sorted(TEXT_ROLES),
"channels": sorted(TEXT_CHANNELS),
"prompt_message": "<|start|>{role}<|channel|>analysis<|message|>{content}<|end|>",
"assistant_reasoning": "<|start|>assistant<|channel|>analysis<|message|>{reasoning}<|end|>",
"assistant_final": "<|start|>assistant<|channel|>final<|message|>{answer}<|return|>",
"assistant_tool_call": "<|start|>assistant<|channel|>analysis<|recipient|>{tool_name}<|message|>{json}<|call|>",
"tool_result": "<|start|>tool<|channel|>analysis<|message|>{result}<|end|>",
"content_protocol_ids_allowed": False,
"supervision": {
"assistant_body_and_terminal": True,
"prompt_and_structural_prefix": False,
"label_offset": -1,
},
},
"omni_reserved_aliases": OMNI_RESERVED_ALIASES,
"omni_embedding_protocol": {
"structured_content_part_types": sorted(
[
"text",
"image",
"audio_input",
"audio_output",
"voice_reference",
]
),
"raw_text_protocol_ids_allowed": False,
"feature_injection": "replace_placeholder_embeddings",
"span_layout": {
"order": ["start", "placeholder", "end"],
"placeholder_repeats": "feature_count",
},
"feature_override": {
"positions": {
"dtype": "int64",
"rank": 2,
"columns": ["batch", "token_position"],
},
"values": {
"dtype": "model_embedding_dtype",
"rank": 2,
"last_dimension": "hidden_size",
},
"placeholder_ids": {"dtype": "int64", "rank": 1},
"positions_unique": True,
"positions_in_current_chunk": True,
"input_ids_match_placeholder_ids": True,
},
"supervision": {
"control_and_placeholder_targets": False,
"label_offset": -1,
},
},
}
def _normalize_protocol_abi(value: dict[str, Any]) -> dict[str, Any]:
normalized = dict(value)
normalized["active_token_id_map"] = _normalize_active_token_map(
value["active_token_id_map"]
)
text = value.get("text_message_protocol")
if not isinstance(text, dict):
raise RuntimeError("tokenizer protocol ABI text message contract is invalid")
normalized_text = dict(text)
normalized_text["roles"] = _normalize_string_set(
text.get("roles"), field="text roles"
)
normalized_text["channels"] = _normalize_string_set(
text.get("channels"), field="text channels"
)
normalized["text_message_protocol"] = normalized_text
omni = value.get("omni_embedding_protocol")
if not isinstance(omni, dict):
raise RuntimeError("tokenizer protocol ABI Omni embedding contract is invalid")
normalized_omni = dict(omni)
normalized_omni["structured_content_part_types"] = _normalize_string_set(
omni.get("structured_content_part_types"), field="Omni content part types"
)
normalized["omni_embedding_protocol"] = normalized_omni
return normalized
def load_protocol_abi(path: Path = DEFAULT_PROTOCOL_ABI) -> dict[str, Any]:
resolved = path.resolve(strict=True)
try:
value = json.loads(
resolved.read_text(encoding="utf-8"),
object_pairs_hook=_strict_json_object,
parse_constant=_reject_json_constant,
)
except (json.JSONDecodeError, ValueError) as error:
raise RuntimeError("tokenizer protocol ABI is not strict JSON") from error
if not isinstance(value, dict) or set(value) != PROTOCOL_ABI_TOP_LEVEL_KEYS:
raise RuntimeError("tokenizer protocol ABI schema changed")
normalized = _normalize_protocol_abi(value)
if _canonical_json(normalized) != _canonical_json(_expected_protocol_abi()):
raise RuntimeError("tokenizer protocol ABI executable semantics changed")
return normalized
def _canonical_sha256(value: Any) -> str:
return hashlib.sha256(_canonical_json(value)).hexdigest()
def text_protocol_semantics(path: Path = DEFAULT_PROTOCOL_ABI) -> dict[str, Any]:
"""Return only semantics consumed by text serialization and objectives."""
value = load_protocol_abi(path)
token_ids = {
item["token"]: item["id"] for item in value["active_token_id_map"]
}
return {
"tokenizer_sha256": value["tokenizer_sha256"],
"vocabulary_size": value["vocabulary_size"],
"lexical_vocabulary_size": value["lexical_vocabulary_size"],
"token_ids": {token: token_ids[token] for token in TEXT_ACTION_TOKENS},
"message_protocol": value["text_message_protocol"],
}
def omni_protocol_semantics(path: Path = DEFAULT_PROTOCOL_ABI) -> dict[str, Any]:
"""Return only semantics consumed by structured Omni input construction."""
value = load_protocol_abi(path)
return {
"tokenizer_sha256": value["tokenizer_sha256"],
"vocabulary_size": value["vocabulary_size"],
"lexical_vocabulary_size": value["lexical_vocabulary_size"],
"reserved_aliases": value["omni_reserved_aliases"],
"embedding_protocol": value["omni_embedding_protocol"],
}
def text_protocol_semantic_sha256(path: Path = DEFAULT_PROTOCOL_ABI) -> str:
return _canonical_sha256(text_protocol_semantics(path))
def omni_protocol_semantic_sha256(path: Path = DEFAULT_PROTOCOL_ABI) -> str:
return _canonical_sha256(omni_protocol_semantics(path))
def active_protocol_count() -> int:
return len(ACTIVE_PROTOCOL_TOKENS)
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