Text Generation
Transformers
Safetensors
qwen3
merged
lora
chinese
novel
conversational
text-generation-inference
Instructions to use wonzer/qwen3_lora_mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wonzer/qwen3_lora_mix with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wonzer/qwen3_lora_mix") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wonzer/qwen3_lora_mix") model = AutoModelForCausalLM.from_pretrained("wonzer/qwen3_lora_mix") 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
- vLLM
How to use wonzer/qwen3_lora_mix with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wonzer/qwen3_lora_mix" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wonzer/qwen3_lora_mix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/wonzer/qwen3_lora_mix
- SGLang
How to use wonzer/qwen3_lora_mix 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 "wonzer/qwen3_lora_mix" \ --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": "wonzer/qwen3_lora_mix", "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 "wonzer/qwen3_lora_mix" \ --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": "wonzer/qwen3_lora_mix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use wonzer/qwen3_lora_mix with Docker Model Runner:
docker model run hf.co/wonzer/qwen3_lora_mix
Qwen Novel Generation Model (LoRA Merged)
这是一个基于 Qwen/Qwen3-4B 和自定义LoRA适配器合并的模型,专门用于中文小说生成。
模型详情
- 基础模型: Qwen/Qwen3-4B
- 用途: 中文文本生成
- 语言: 中文
使用方法
from transformers import AutoModelForCausalLM, AutoTokenizer
# 加载模型和tokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/model-name")
tokenizer = AutoTokenizer.from_pretrained("your-username/model-name")
# 生成文本
def generate_story(prompt, max_length=500):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=0.7,
do_sample=True,
top_p=0.9,
repetition_penalty=1.1
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# 示例
story = generate_story("请写一个关于友情的故事:")
print(story)
生成示例
输入: "请写一个关于友情的故事:"
输出: [模型会生成相应的故事内容]
注意事项
- 这是一个合并后的完整模型,包含了LoRA的所有改进
- 适合用于中文创作场景
- 建议使用适当的生成参数以获得最佳效果
许可证
本模型遵循Apache 2.0许可证。
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