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be22f80 892a66c c471409 be22f80 | 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 | import torch
import gradio as gr
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from utils import EN_US
ZH2EN = {
"有算力的可自行克隆至本地或复刻至购买了 GPU 环境的账号测试": "If you have computing power, you can test by cloning to local or forking to an account with purchased GPU environment",
"⚙️ 参数设置": "⚙️ Parameters",
"系统提示词": "System prompt",
"最大 token 数": "Max new tokens",
"温度参数": "Temperature",
"Top-K 采样": "Top K sampling",
"Top-P 采样": "Top P sampling",
"重复性惩罚": "Repetition penalty",
}
def _L(zh_txt: str):
return ZH2EN[zh_txt] if EN_US else zh_txt
MODEL_ID = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
MODEL_NAME = MODEL_ID.split("/")[-1]
CONTEXT_LENGTH = 16000
DESCRIPTION = (
f"This is a HuggingFace deployment instance of {MODEL_NAME} model, if you have computing power, you can test by cloning to local or forking to an account with purchased GPU environment"
if EN_US
else f"当前仅提供 {MODEL_NAME} 模型的 ModelScope 版部署实例，有算力的可自行克隆至本地或复刻至购买了 GPU 环境的账号测试"
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device == torch.device("cuda"):
if EN_US:
import huggingface_hub
MODEL_DIR = huggingface_hub.snapshot_download(
MODEL_ID, cache_dir="./__pycache__"
)
else:
import modelscope
MODEL_DIR = modelscope.snapshot_download(MODEL_ID, cache_dir="./__pycache__")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, device_map="auto")
def predict(msg, history, prompt, temper, max_tokens, top_k, repeat_penalty, top_p):
# Format history with a given chat template
stop_tokens = ["<|endoftext|>", "<|im_end|>", "|im_end|"]
instruction = "<|im_start|>system\n" + prompt + "\n<|im_end|>\n"
for user, assistant in history:
instruction += f"<|im_start|>user\n{user}\n<|im_end|>\n<|im_start|>assistant\n{assistant}\n<|im_end|>\n"
instruction += f"<|im_start|>user\n{msg}\n<|im_end|>\n<|im_start|>assistant\n"
try:
if device == torch.device("cpu"):
raise EnvironmentError(
_L("有算力的可自行克隆至本地或复刻至购买了 GPU 环境的账号测试")
)
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=True,
skip_special_tokens=True,
)
enc = tokenizer(instruction, return_tensors="pt", padding=True, truncation=True)
input_ids, attention_mask = enc.input_ids, enc.attention_mask
if input_ids.shape[1] > CONTEXT_LENGTH:
input_ids = input_ids[:, -CONTEXT_LENGTH:]
attention_mask = attention_mask[:, -CONTEXT_LENGTH:]
generate_kwargs = dict(
input_ids=input_ids.to(device),
attention_mask=attention_mask.to(device),
streamer=streamer,
do_sample=True,
temperature=temper,
max_new_tokens=max_tokens,
top_k=top_k,
repetition_penalty=repeat_penalty,
top_p=top_p,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
except Exception as e:
streamer = f"{e}"
outputs = []
for new_token in streamer:
outputs.append(new_token)
if new_token in stop_tokens:
break
yield "".join(outputs)
def DeepSeek_R1_Qwen_7B():
with gr.Accordion(label=_L("⚙️ 参数设置"), open=False) as ds_acc:
prompt = gr.Textbox(
"You are a useful assistant. first recognize user request and then reply carfuly and thinking",
label=_L("系统提示词"),
)
temper = gr.Slider(0, 1, 0.6, label=_L("温度参数"))
maxtoken = gr.Slider(0, 32000, 10000, label=_L("最大 token 数"))
topk = gr.Slider(1, 80, 40, label=_L("Top-K 采样"))
repet = gr.Slider(0, 2, 1.1, label=_L("重复性惩罚"))
topp = gr.Slider(0, 1, 0.95, label=_L("Top-P 采样"))
return gr.ChatInterface(
predict,
description=DESCRIPTION,
additional_inputs_accordion=ds_acc,
additional_inputs=[prompt, temper, maxtoken, topk, repet, topp],
).queue()
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