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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- wangrui6/Zhihu-KOL |
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language: |
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- zh |
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--- |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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. |
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- **Developed by:** Jinyu(Isaac) |
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- **Language(s) (NLP):** Chinese |
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### Direct Use |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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from peft import PeftModel |
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mode_path = '/root/autodl-tmp/qwen/Qwen2-7B-Instruct/' |
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lora_path = './output/Qwen2_instruct_Zhihu_pro_lora/checkpoint-1' # checkpoint 地址 |
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# 加载tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True) |
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# 加载模型 |
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model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16, trust_remote_code=True).eval() |
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# 加载lora权重 |
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model = PeftModel.from_pretrained(model, model_id=lora_path) |
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prompt = """ |
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高中数学基础差，如何预习高等数学？ |
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""" |
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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}], |
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add_generation_prompt=True, |
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tokenize=True, |
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return_tensors="pt", |
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return_dict=True |
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).to('cuda') |
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gen_kwargs = {"max_length": 1000, "do_sample": True, "top_p": 0.8} |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, **gen_kwargs) |
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outputs = outputs[:, inputs['input_ids'].shape[1]:] |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
