Instructions to use silk-road/ChatHaruhi_RolePlaying_qwen_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use silk-road/ChatHaruhi_RolePlaying_qwen_7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="silk-road/ChatHaruhi_RolePlaying_qwen_7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("silk-road/ChatHaruhi_RolePlaying_qwen_7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use silk-road/ChatHaruhi_RolePlaying_qwen_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "silk-road/ChatHaruhi_RolePlaying_qwen_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silk-road/ChatHaruhi_RolePlaying_qwen_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/silk-road/ChatHaruhi_RolePlaying_qwen_7b

SGLang

How to use silk-road/ChatHaruhi_RolePlaying_qwen_7b 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 "silk-road/ChatHaruhi_RolePlaying_qwen_7b" \
    --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": "silk-road/ChatHaruhi_RolePlaying_qwen_7b",
		"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 "silk-road/ChatHaruhi_RolePlaying_qwen_7b" \
        --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": "silk-road/ChatHaruhi_RolePlaying_qwen_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use silk-road/ChatHaruhi_RolePlaying_qwen_7b with Docker Model Runner:
```
docker model run hf.co/silk-road/ChatHaruhi_RolePlaying_qwen_7b
```

本脚本是对千问7B模型的微调和测试，使得Qwen 7B能够有角色扮演的能力

This script fine-tunes and tests the Qwen 7B model to give Qwen 7B the capability of role playing.

项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya

118K训练数据由李鲁鲁收集，
模型是由豆角训练的
Qwen inference代码由米唯实编写，并接入Chatharuhi，目前进行本模型维护和bug解决
李鲁鲁编写了ChatHaruhi内部的prompt组织函数

A Harry Potter test see in https://github.com/LC1332/Chat-Haruhi-Suzumiya/blob/main/notebook/Harry_Potter_test_on_Qwen7B.ipynb

使用方法

载入函数

from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("silk-road/ChatHaruhi_RolePlaying_qwen_7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("silk-road/ChatHaruhi_RolePlaying_qwen_7b", device_map="auto", trust_remote_code=True)
model = model.eval()

具体看https://github.com/LC1332/Chat-Haruhi-Suzumiya/blob/main/notebook/ChatHaruhi_x_Qwen7B.ipynb 这个notebook

from ChatHaruhi import ChatHaruhi

chatbot = ChatHaruhi( role_name = 'haruhi', max_len_story = 1000 )

prompt = chatbot.generate_prompt(role='阿虚', text = '我看新一年的棒球比赛要开始了！我们要去参加吗？')

response, history = model.chat(tokenizer, prompt, history=[])
print(response)

chatbot.append_response(response)

目前支持 role_name

role_from_hf

role_from_jsonl

多种角色格式载入。

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