Instructions to use XiangJinYu/Zhihu_Qwen2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiangJinYu/Zhihu_Qwen2 with Transformers:

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

pipe = pipeline("text-generation", model="XiangJinYu/Zhihu_Qwen2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XiangJinYu/Zhihu_Qwen2")
model = AutoModelForCausalLM.from_pretrained("XiangJinYu/Zhihu_Qwen2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiangJinYu/Zhihu_Qwen2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiangJinYu/Zhihu_Qwen2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiangJinYu/Zhihu_Qwen2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiangJinYu/Zhihu_Qwen2

SGLang

How to use XiangJinYu/Zhihu_Qwen2 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 "XiangJinYu/Zhihu_Qwen2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiangJinYu/Zhihu_Qwen2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "XiangJinYu/Zhihu_Qwen2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiangJinYu/Zhihu_Qwen2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiangJinYu/Zhihu_Qwen2 with Docker Model Runner:
```
docker model run hf.co/XiangJinYu/Zhihu_Qwen2
```

Model Details

Model Description

This fine-tuned model is the result of my adjustments on the Qwen2 model using Zhihu Q&A data, but it still has some issues with performance. I would not recommend using it or conducting similar social experiments, as they could lead to undesirable consequences.

Developed by: Jinyu(Isaac)
Language(s) (NLP): Chinese

Direct Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel

mode_path = '/root/autodl-tmp/qwen/Qwen2-7B-Instruct/'
lora_path = './output/Qwen2_instruct_Zhihu_pro_lora/checkpoint-1' #  checkpoint 地址

# 加载tokenizer
tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True)

# 加载模型
model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16, trust_remote_code=True).eval()

# 加载lora权重
model = PeftModel.from_pretrained(model, model_id=lora_path)

prompt = """
高中数学基础差，如何预习高等数学？
"""
inputs = tokenizer.apply_chat_template([{"role": "system", "content": "你是一个真实的人类。回答用户的问题，并在聊天回复中展现你的情绪，包括开心，幸福，愤怒，伤心，阴阳怪气等"},{"role": "user", "content": prompt}],
                                       add_generation_prompt=True,
                                       tokenize=True,
                                       return_tensors="pt",
                                       return_dict=True
                                       ).to('cuda')
gen_kwargs = {"max_length": 1000, "do_sample": True, "top_p": 0.8}
with torch.no_grad():
    outputs = model.generate(**inputs, **gen_kwargs)
    outputs = outputs[:, inputs['input_ids'].shape[1]:]
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Model Details

Model Description

Direct Use

Dataset used to train XiangJinYu/Zhihu_Qwen2