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# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple
from transformers.utils import is_torch_npu_available
from ..chat import ChatModel
from ..data import Role
from ..extras.constants import PEFT_METHODS
from ..extras.misc import torch_gc
from ..extras.packages import is_gradio_available
from .common import get_save_dir, load_config
from .locales import ALERTS
if TYPE_CHECKING:
from ..chat import BaseEngine
from .manager import Manager
if is_gradio_available():
import gradio as gr
def _format_response(text: str, lang: str, thought_words: Tuple[str, str] = ("<think>", "</think>")) -> str:
r"""
Post-processes the response text.
Based on: https://huggingface.co/spaces/Lyte/DeepSeek-R1-Distill-Qwen-1.5B-Demo-GGUF/blob/main/app.py
"""
if thought_words[0] not in text:
return text
text = text.replace(thought_words[0], "")
result = text.split(thought_words[1], maxsplit=1)
if len(result) == 1:
summary = ALERTS["info_thinking"][lang]
thought, answer = text, ""
else:
summary = ALERTS["info_thought"][lang]
thought, answer = result
return (
f"<details open><summary class='thinking-summary'><span>{summary}</span></summary>\n\n"
f"<div class='thinking-container'>\n{thought}\n</div>\n</details>{answer}"
)
class WebChatModel(ChatModel):
def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
self.manager = manager
self.demo_mode = demo_mode
self.engine: Optional["BaseEngine"] = None
if not lazy_init: # read arguments from command line
super().__init__()
if demo_mode and os.environ.get("DEMO_MODEL") and os.environ.get("DEMO_TEMPLATE"): # load demo model
model_name_or_path = os.environ.get("DEMO_MODEL")
template = os.environ.get("DEMO_TEMPLATE")
infer_backend = os.environ.get("DEMO_BACKEND", "huggingface")
super().__init__(
dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
)
@property
def loaded(self) -> bool:
return self.engine is not None
def load_model(self, data) -> Generator[str, None, None]:
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
finetuning_type, checkpoint_path = get("top.finetuning_type"), get("top.checkpoint_path")
user_config = load_config()
error = ""
if self.loaded:
error = ALERTS["err_exists"][lang]
elif not model_name:
error = ALERTS["err_no_model"][lang]
elif not model_path:
error = ALERTS["err_no_path"][lang]
elif self.demo_mode:
error = ALERTS["err_demo"][lang]
if error:
gr.Warning(error)
yield error
return
yield ALERTS["info_loading"][lang]
args = dict(
model_name_or_path=model_path,
cache_dir=user_config.get("cache_dir", None),
finetuning_type=finetuning_type,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") != "none" else None,
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
enable_liger_kernel=(get("top.booster") == "liger_kernel"),
infer_backend=get("infer.infer_backend"),
infer_dtype=get("infer.infer_dtype"),
trust_remote_code=True,
)
# checkpoints
if checkpoint_path:
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in checkpoint_path]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, checkpoint_path)
# quantization
if get("top.quantization_bit") != "none":
args["quantization_bit"] = int(get("top.quantization_bit"))
args["quantization_method"] = get("top.quantization_method")
args["double_quantization"] = not is_torch_npu_available()
super().__init__(args)
yield ALERTS["info_loaded"][lang]
def unload_model(self, data) -> Generator[str, None, None]:
lang = data[self.manager.get_elem_by_id("top.lang")]
if self.demo_mode:
gr.Warning(ALERTS["err_demo"][lang])
yield ALERTS["err_demo"][lang]
return
yield ALERTS["info_unloading"][lang]
self.engine = None
torch_gc()
yield ALERTS["info_unloaded"][lang]
@staticmethod
def append(
chatbot: List[Dict[str, str]],
messages: List[Dict[str, str]],
role: str,
query: str,
) -> Tuple[List[Dict[str, str]], List[Dict[str, str]], str]:
r"""
Adds the user input to chatbot.
Inputs: infer.chatbot, infer.messages, infer.role, infer.query
Output: infer.chatbot, infer.messages
"""
return chatbot + [{"role": "user", "content": query}], messages + [{"role": role, "content": query}], ""
def stream(
self,
chatbot: List[Dict[str, str]],
messages: List[Dict[str, str]],
lang: str,
system: str,
tools: str,
image: Optional[Any],
video: Optional[Any],
audio: Optional[Any],
max_new_tokens: int,
top_p: float,
temperature: float,
) -> Generator[Tuple[List[Dict[str, str]], List[Dict[str, str]]], None, None]:
r"""
Generates output text in stream.
Inputs: infer.chatbot, infer.messages, infer.system, infer.tools, infer.image, infer.video, ...
Output: infer.chatbot, infer.messages
"""
chatbot.append({"role": "assistant", "content": ""})
response = ""
for new_text in self.stream_chat(
messages,
system,
tools,
images=[image] if image else None,
videos=[video] if video else None,
audios=[audio] if audio else None,
max_new_tokens=max_new_tokens,
top_p=top_p,
temperature=temperature,
):
response += new_text
if tools:
result = self.engine.template.extract_tool(response)
else:
result = response
if isinstance(result, list):
tool_calls = [{"name": tool.name, "arguments": json.loads(tool.arguments)} for tool in result]
tool_calls = json.dumps(tool_calls, ensure_ascii=False)
output_messages = messages + [{"role": Role.FUNCTION.value, "content": tool_calls}]
bot_text = "```json\n" + tool_calls + "\n```"
else:
output_messages = messages + [{"role": Role.ASSISTANT.value, "content": result}]
bot_text = _format_response(result, lang, self.engine.template.thought_words)
chatbot[-1] = {"role": "assistant", "content": bot_text}
yield chatbot, output_messages
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