text stringlengths 14 100k | source stringclasses 1
value | repo stringclasses 810
values | language stringclasses 13
values |
|---|---|---|---|
<|fim_suffix|>in func_part:
# Still accumulating function name, not ready yet
return None, ""
func_name, _, args_part = func_part.partition("{")
func_name = func_name.strip()
# Strip trailing '}' if present (Gemma4 structural brace)
if args_part.endswith("}"):
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
"""Gemma4 tool call parsing utilities for offline inference.
Standalone functions that parse decoded model text to extract t... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
import regex as re
from vllm.entrypoints.chat_utils import make_tool_call_id
from vllm.entrypoints.openai.chat_completion.protocol import (
Cha... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
GLM-4.7 Tool Call Parser.
GLM-4.7 uses a slightly different tool call format compared to GLM-4.5:
- The function name may appear on the same line as ``<tool_call>`` without
a newline separ... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
GLM-4 Tool Call Parser with incremental string streaming support.
This parser fixes the streaming issue reported in Issue #32829 where long string
parameters (e.g., file content with 4000+ chara... | fim | vllm-project/vllm | python |
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from typing import TYPE_CHECKING
from vllm.entrypoints.openai.engine.protocol import (
DeltaMessage,
ExtractedToolCallInformation,
)
from vllm.tool_parsers.abstract_t... | fim | vllm-project/vllm | python |
<|fim_suffix|>n("Error trying to handle streaming tool call.")
return None
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
from typing import Any, Protocol, TypeVar
import regex as re
fro... | fim | vllm-project/vllm | python |
<|fim_suffix|> incomplete string since OpenAI only ever (as far as I have
# seen) allows sending the entire tool/ function name at once.
flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
try:
tool_call_arr = []
is_complete = []
try:
... | fim | vllm-project/vllm | python |
<|fim_suffix|> )
]
)
self.streamed_args_for_tool[self.current_tool_id] += (
argument_diff
)
# re-set stuff pertaining to progress in the current tool
self.c... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
import regex as re
from vllm.entrypoints.chat_utils import make_tool_call_id
from vllm.entrypoints.openai.chat_completion.protocol import (
Cha... | fim | vllm-project/vllm | python |
<|fim_suffix|>ind(
"},{", args_matches[current_idx].start()
)
if next_tool_pos != -1:
args_end_pos = next_tool_pos + 1
args_text = (
current_text[
a... | fim | vllm-project/vllm | python |
<|fim_suffix|> index=self.current_tool_id,
id=self._current_tool_call_id,
type="function",
function=DeltaFunctionCall(
name=tool_name,
),
)
]
)... | fim | vllm-project/vllm | python |
<|fim_suffix|>name_sent = True
self.streamed_args_for_tool.append("")
else:
delta = None
# now we know we're on the same tool call and we're streaming
# arguments
else:
prev_arguments = self.get_arguments(
... | fim | vllm-project/vllm | python |
<|fim_suffix|> )
return None
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
import partial_json_parser
import regex as re
from partial_json_parser.core.options import Allow... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
import regex as re
from vllm.entrypoints.openai.chat_completion.protocol import (
ChatCompletionRequest,
)
from vllm.entrypoints.openai.engine.protocol imp... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ast
from collections.abc import Sequence
import regex as re
import vllm.envs as envs
from vllm.entrypoints.openai.chat_completion.protocol import (
ChatCompletionRequest,
)
from vllm.en... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ast
from collections.abc import Sequence
import regex as re
from transformers import PreTrainedTokenizerBase
import vllm.envs as envs
from vllm.entrypoints.openai.chat_completion.protocol i... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
import partial_json_parser
import regex as re
from partial_json_parser.core.options import Allow
from transformers import PreTrainedTokenizerBase
i... | fim | vllm-project/vllm | python |
<|fim_suffix|>.tool_call_end_token: str = "</longcat_tool_call>"
self.tool_call_regex = re.compile(
r"<longcat_tool_call>(.*?)</longcat_tool_call>"
r"|<longcat_tool_call>(.*)",
re.DOTALL,
)
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText:... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
from typing import Any
import regex as re
from vllm.entrypoints.chat_utils import make_tool_call_id
from vllm.entrypoints.openai.chat_completion.pr... | fim | vllm-project/vllm | python |
<|fim_suffix|>Quick check
if self.tool_call_start_token not in model_output:
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
try:
tool_calls = []
# Find all complete tool_call blocks
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
from typing import Any
import regex as re
from vllm.entrypoints.chat_utils import make_tool_call_id
from vllm.entrypoints.openai.chat_completion.pr... | fim | vllm-project/vllm | python |
<|fim_suffix|> delta_text = self.bot_token + "".join(
delta_text.split(self.bot_token)[1:]
)
delta_tool_calls = self._generate_delta_tool_call(delta_text)
if not additional_content and len(delta_tool_calls) == 0:
if self.streaming_state in [
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ast
from collections.abc import Sequence
import regex as re
from transformers import PreTrainedTokenizerBase
import vllm.envs as envs
from vllm.entrypoints.openai.chat_completion.protocol i... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
from typing import Any
import regex as re
from transformers import PreTrainedTokenizerBase
from vllm.entrypoints.chat_utils import make_tool_call_i... | fim | vllm-project/vllm | python |
<|fim_suffix|>ol_id:
self.prev_tool_call_arr.append({})
while len(self._args_started) <= self.current_tool_id:
self._args_started.append(False)
while len(self._args_closed) <= self.current_tool_id:
self._args_closed.append(False)
while len(self._seen_keys) <= ... | fim | vllm-project/vllm | python |
<|fim_suffix|>t)
parsed = getattr(module.body[0], "value", None)
if isinstance(parsed, ast.List) and all(
isinstance(e, ast.Call) for e in parsed.elts
):
return ExtractedToolCallInformation(
tools_called=True,
to... | fim | vllm-project/vllm | python |
<|fim_suffix|>n None
# Handle normal content before tool calls
if not self.is_tool_call_started:
# Check if tool call is starting
if (
self.tool_call_start_token_id in delta_token_ids
or self.tool_call_start_token in delta_text
):
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import importlib
from collections.abc import Sequence
from typing import Any
from openai.types.responses.function_tool import FunctionTool
from vllm.entrypoints.chat_utils import make_tool_call_id... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from qwen3coder xml parser, All rights reserved.
# ruff: noqa: E501
import json
import uuid
from collections.abc import Sequence
import regex as re
from vllm.entrypoints.openai.chat_comp... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import contextlib
import json
from collections.abc import Sequence
from typing import Any
import regex as re
from vllm.entrypoints.openai.chat_completion.protocol import (
ChatCompletionReques... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
from typing import Any
from xml.parsers.expat import ParserCreate
import regex as re
from vllm.entrypoints.chat_utils import make_tool_call_id
from ... | fim | vllm-project/vllm | python |
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from typing import TYPE_CHECKING
import partial_json_parser
import regex as re
from partial_json_parser.core.options import Allow
from vllm.entrypoints.chat_utils import make_tool_call_id
from vllm.... | fim | vllm-project/vllm | python |
<|fim_suffix|>egin=begin + tool.function.name + arguments_field_prefix,
content=JSONSchemaFormat(
json_schema=_get_function_parameters(tool.function)
),
end=end,
)
for tool in tools
for begin, end in formats
]
@register_vllm_structural_ta... | fim | vllm-project/vllm | python |
<|fim_suffix|>dict_text = text[: text.rfind("{")]
num_keys = trailing_dict_text.count(":")
num_values = trailing_dict_text.count(",")
if num_keys <= num_values:
return None
if bracket_stack and bracket_stack[-1] == "(":
trailing_params_text = text[: text.rfind("(")]
... | fim | vllm-project/vllm | python |
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa
import json
from collections.abc import Sequence
from typing import Any, Optional, Union
import regex as re
from vllm.entrypoints.openai.chat_completion.protocol import (
ChatCompletionRequest,
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import functools
from collections.abc import Callable
from typing import Any, TypeAlias
# Import the implementation details
from .otel import (
SpanKind,
extract_trace_context,
init_ote... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import atexit
import functools
import inspect
import os
import traceback
from collections.abc import Mapping
from contextlib import contextmanager
from typing import Any
from vllm.logger import ini... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Mapping
from vllm.logger import init_logger
from vllm.utils.func_utils import run_once
logger = init_logger(<|fim_suffix|>argely based on OpenTelemetry Semantic Convent... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-Fil<|fim_suffix|> raise ImportError(
"Please install modelscope>=1.18.1 via "
"`pip install modelscope>=1.18.1` to use ModelScope."
) from err
<|fim_middle|>eCopyrightText: Copyright contributors to the vLLM project
from vll... | fim | vllm-project/vllm | python |
<|fim_suffix|>ck_path
__all__ = ["get_chat_template_fallback_path"]
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the v<|fim_middle|>LLM project
from .registry import get_chat_template_fallba<|endoftext|> | fim | vllm-project/vllm | python |
<|fim_suffix|>_deepseek_vl2.jinja",
"fuyu": CHAT_TEMPLATES_DIR / "template_fuyu.jinja",
"minicpmv": _get_minicpmv_chat_template_fallback,
"minicpmv4_6": _get_minicpmv_chat_template_fallback,
"paligemma": CHAT_TEMPLATES_DIR / "template_basic.jinja",
"siglip": CHAT_TEMPLATES_DIR / "template_basic.jinj... | fim | vllm-project/vllm | python |
<|fim_suffix|>00",
exc_info=e,
)
return max_position_embeddings
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from collections.abc import Callable, Iterator
from contextlib import contextmanager
from dataclass... | fim | vllm-project/vllm | python |
<|fim_suffix|>self,
model: str | Path,
trust_remote_code: bool,
revision: str | None = None,
code_revision: str | None = None,
**kwargs,
) -> tuple[dict, PretrainedConfig]:
raise NotImplementedError
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyri... | fim | vllm-project/vllm | python |
<|fim_suffix|>| None = 131072,
initializer_range: float | None = 0.02,
rms_norm_eps: float = 1e-6,
use_cache: bool | None = True,
pad_token_id: int | None = None,
bos_token_id: int | None = 163691,
eos_token_id: int | None = 163691,
pretraining_tp: int | None = 1,... | fim | vllm-project/vllm | python |
<|fim_suffix|>g",
"ColPaliConfig",
"ColQwen3Config",
"OpsColQwen3Config",
"Qwen3VLNemotronEmbedConfig",
"Cosmos3Config",
"DiffusionGemmaConfig",
"DiffusionGemmaTextConfig",
"DeepseekVLV2Config",
"DeepseekV3Config",
"DeepseekV4Config",
"DotsOCRConfig",
"EAGLEConfig",
"... | fim | vllm-project/vllm | python |
<|fim_suffix|>ge = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
rope_theta = kwargs.pop("rope_theta", 10000.0)
if rope_parameters is None:
rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
self.rope_parameters = rope... | fim | vllm-project/vllm | python |
<|fim_suffix|>position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_parameters: dict[str, Any] | None = None,
sliding_window=None,
... | fim | vllm-project/vllm | python |
<|fim_suffix|>of the language model."""
return self.llm_config.hidden_size
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers import PretrainedConfig, SiglipVisionConfig
from transformers.models.qwen2 import Qwen2Config
cl... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://github.com/zai-org/ChatGLM2-6B
from transformers import PretrainedConfig
class ChatGLMConfig(PretrainedConfig):
model_type = "chatglm"
attribute_map = {
"n... | fim | vllm-project/vllm | python |
<|fim_suffix|>r CheersTextConfig()
if isinstance(vision_representation_config, dict):
self.vision_representation_config = SiglipVisionConfig(
**vision_representation_config
)
else:
self.vision_representation_config = (
vision_represent... | fim | vllm-project/vllm | python |
<|fim_suffix|>ze // self.vision_config.patch_size
return (ps * ps) // (self.pixel_shuffle_factor**2)
def get_text_config(self, **kwargs):
return self.text_config
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Configuration... | fim | vllm-project/vllm | python |
<|fim_suffix|>a" from
# vlm_config).
if vlm_config is not None:
vlm_dict = (
vlm_config if isinstance(vlm_config, dict) else vlm_config.to_dict()
)
_conflicting = {"model_type", "_name_or_path"}
for key, value in vlm_dict.items():
... | fim | vllm-project/vllm | python |
<|fim_suffix|> colbert_dim: int | None = None,
pooling: str | None = None,
**kwargs,
):
# Store embedding projection config fields
self.embed_dim = embed_dim
self.dims = dims
self.dim = dim
self.projection_dim = projection_dim
self.colbert_dim =... | fim | vllm-project/vllm | python |
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.models.qwen3_vl.configuration_qwen3_vl import Qwen3VLConfig
class Cosmos3Config(Qwen3VLConfig):
model_type = "cosmos3_omni"
<|endoftext|> | fim | vllm-project/vllm | python |
<|fim_suffix|> = rope_theta
self.rope_parameters = rope_scaling or rope_parameters
super().__init__(**kwargs)
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers import PretrainedConfig
class... | fim | vllm-project/vllm | python |
<|fim_suffix|>
self.vision_config = VisionEncoderConfig(**kwargs.pop("vision_config", {}))
self.projector_config = MlpProjectorConfig(**kwargs.pop("projector_config", {}))
self.text_config = DeepseekVLV2TextConfig(**kwargs.pop("language_config", {}))
self.tile_tag = tile_tag
sel... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers import PretrainedConfig
from transformers.models.gemma4.configuration_gemma4 import Gemma4VisionConfig
def _init_text_config(self: PretrainedConfig, **kwar... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
from transformers.models.qwen2 import Qwen2Config
class DotsVisionConfig(PretrainedConfig):
model_type: str... | fim | vllm-project/vllm | python |
<|fim_suffix|>ig_dict(
pretrained_model_name_or_path, **without_trust_remote_code(kwargs)
)
return cls.from_dict(config_dict, **kwargs)
def to_json_string(self, use_diff: bool = True) -> str:
# we override use_diff to False as initializing
# EAGLEConfig with default argu... | fim | vllm-project/vllm | python |
<|fim_suffix|>_remote_code(kwargs)
)
return cls.from_dict(config_dict, **kwargs)
def to_json_string(self, use_diff: bool = True) -> str:
# we override use_diff to False as initializing
# ExtractHiddenStatesConfig with default arguments is not supported
del use_diff
r... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://huggingface.co/tiiuae/falcon-7b/blob/main/configuration_RW.py
# Copyright 2023 The vLLM team.
# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.
# All righ... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from __future__ import annotations
import contextlib
from transformers import AutoConfig
from transformers.configuration_utils import PretrainedConfig
class FireRedLIDConfig(PretrainedConfig):
... | fim | vllm-project/vllm | python |
<|fim_suffix|>ken_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing im... | fim | vllm-project/vllm | python |
<|fim_suffix|>k_size: int | None = None,
continuous_features_mode: str = "replace",
crq_transformer_config: dict | None = None,
eos_token_id: int | None = None,
group_size: int = 5,
enable_audio_invert_tower: bool = True,
pad_token_id: int | None = None,
**kwargs,... | fim | vllm-project/vllm | python |
<|fim_suffix|>
self.projector_hidden_act = projector_hidden_act
self.projector_dropout = projector_dropout
self.downsample_rate = downsample_rate
self.use_image_newline_parameter = use_image_newline_parameter
self.deepstack_layer_map = deepstack_layer_map or vision_layer_to_llm_l... | fim | vllm-project/vllm | python |
<|fim_suffix|>r().__getattribute__(key)
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py
from transformers ... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
class HYV3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of ... | fim | vllm-project/vllm | python |
<|fim_suffix|> tie_word_embeddings=tie_word_embeddings,
auto_map=auto_map,
**kwargs,
)
class HCXVisionConfig(PretrainedConfig):
"""Vendored HyperCLOVAX Vision config with transformers v5 fix.
The original remote code config does not handle empty initialization
(text_c... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from __future__ import annotations
from transformers import Qwen3Config
from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
class PixelShuffleSiglip2VisionConfig(Sigl... | fim | vllm-project/vllm | python |
<|fim_suffix|>attn_type
self.merge_type = merge_type
# MM Projector
self.mm_projector_type = mm_projector_type
if mm_hidden_size is not None:
self.mm_hidden_size = mm_hidden_size
else:
self.mm_hidden_size = hidden_size
self.projector_hidden_act = p... | fim | vllm-project/vllm | python |
<|fim_suffix|>
self.moe_intermediate_size = moe_intermediate_size
self.first_k_dense_replace = first_k_dense_replace
self.moe_layer_freq = moe_layer_freq
self.use_grouped_topk = use_grouped_topk
self.num_expert_group = num_expert_group
self.topk_group = topk_group
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
from transformers import DeepseekV2Config
from transformers.configuration_utils import Pretra... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM projec<|fim_suffix|>per().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
__all__ = ["LagunaConfig"]
<|fim_middle|>t
from transformers.configuration_utils import PretrainedConfig
class Lagun... | fim | vllm-project/vllm | python |
<|fim_suffix|> int = 32,
pad_token_id: int = 0,
bos_token_id: int = 1,
eos_token_id: int = 2,
tie_word_embeddings: bool = True,
rope_parameters: dict[str, Any] | None = None,
max_position_embeddings: int = 128_000,
use_cache: bool = True,
norm_eps: float =... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from transformers import PretrainedConfig
from vllm.transformers_utils.utils import without_trust_remote_code
class MedusaConfig(PretrainedConfig):
model_type = "medusa"
def _... | fim | vllm-project/vllm | python |
<|fim_suffix|>utors to the vLLM project
from transformers import Qwen3MoeConfig
class MellumConfig(Qwen3MoeConfig):
model_type = "mellum"
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyri<|fim_middle|>ght contrib<|endoftext|> | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 Horizon team, Xiaomi MiLM Plus.
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# Th... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors t<|fim_suffix|>_key_value_heads=8,
vit_window_attn_types=None,
visual_token_window_size=64,
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.hidden_siz... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from packaging.version import Version
from transformers import PretrainedConfig, WhisperConfig
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.logger i... | fim | vllm-project/vllm | python |
<|fim_suffix|> currently unused.
n_candidates: int
number of child candidates to create per sequence
tie_weights: bool
If true, use a single set of weights for every model
head/stage after the first. The initial projection
from the ... | fim | vllm-project/vllm | python |
<|fim_suffix|>am3VisionConfig(**vision_config)
# Store the original config dict for model access
self.config = config
tokenizer_config = config.get("tokenizer", {})
self.answer_token_id = tokenizer_config.get("answer_id", 3)
super().__init__(**kwargs)
# Expose key attr... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
from transformers.configuration_utils import PretrainedConfig
class MoonViTConfig(Pretrained... | fim | vllm-project/vllm | python |
<|fim_suffix|>_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
... | fim | vllm-project/vllm | python |
<|fim_suffix|>dings,
**kwargs,
)
@property
def layers_block_type(self):
return [
"mamba"
if self.hybrid_override_pattern[i] == "M"
else "attention"
if self.hybrid_override_pattern[i] == "*"
else "mlp"
if self.hy... | fim | vllm-project/vllm | python |
<|fim_suffix|>t | None = 32,
num_attention_heads: int | None = 30,
num_key_value_heads: int | None = None,
hidden_act: str | None = "silu",
max_position_embeddings: int | None = 65536,
initializer_range: float | None = 0.02,
use_cache: bool | None = True,
pad_toke... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""OpenVLA configuration support.
OpenVLA checkpoints use a custom ``model_type`` and nest the language model
configuration under ``text_config``. This shim lets vLLM load the checkpoint
configurati... | fim | vllm-project/vllm | python |
<|fim_suffix|>pe = llm_config["model_type"]
llm_config.pop("model_type")
llm_config = AutoConfig.for_model(model_type, **llm_config)
# map llm_config to text_config
self.text_config = llm_config
if visual_tokenizer_config is not None:
assert isinstanc... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from transformers import ParakeetEncoderConfig, PretrainedConfig
class ParakeetConfig(ParakeetEncoderConfig):
def __init__(
self,
llm_hidden_s... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers import PretrainedConfig
from transformers.models.auto import CONFIG_MAPPING
class QianfanOCRVisionConfig(PretrainedConfig):
model_type = "qianfan_ocr_... | fim | vllm-project/vllm | python |
<|fim_suffix|>TIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Qwen3.5 model configuration"""
from transformers.configuration_utils import PretrainedConfig
class Qwen3_5TextConfig(PretrainedConfig):
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 The Qwen Team and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complian... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa
# mypy: ignore-errors
# coding=utf-8
# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0... | fim | vllm-project/vllm | python |
<|fim_suffix|>n_layers=48,
num_attention_heads=16,
num_key_value_heads=2,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_parameters=None,
attentio... | fim | vllm-project/vllm | python |
<|fim_suffix|>multiple
self.teachers = teachers if teachers is not None else []
self.cls_token_per_teacher = cls_token_per_teacher
self.video_temporal_patch_size = video_temporal_patch_size
self.separate_video_embedder = separate_video_embedder
super().__init__(**kwargs)
<|fim_pr... | fim | vllm-project/vllm | python |
<|fim_suffix|>fig
__all__ = ["SpeculatorsConfig"]
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vL<|fim_middle|>LM project
from .base import SpeculatorsCon<|endoftext|> | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
SUPPORTED_SPECULATORS_TYPES = {}
def register_speculator(name):
def decorator(fn):
SUPPORTED_SPECULATORS_TYPES[name] = fn
return fn
return decorator
@register_speculator... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from dataclasses import fields
from typing import Any
from transformers import PretrainedConfig
from vllm.transformers_utils.configs.speculators.algos import (
SUPPORTED_SPECULATORS_T... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
class Step3VisionEncoderConfig(PretrainedConfig):
model_type = "step3_vision_encoder"
def __init__(
... | fim | vllm-project/vllm | python |
<|fim_prefix|># SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
class Step3p5Config(PretrainedConfig):
model_type = "step3p5"
def __init__(
self,
hidd... | fim | vllm-project/vllm | python |
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