# Qwen2.5-32B-Instruct-FineTune

**Repository Path**: hf-models/Qwen2.5-32B-Instruct-FineTune

## Basic Information

- **Project Name**: Qwen2.5-32B-Instruct-FineTune
- **Description**: Mirror of https://huggingface.co/chengang12345/Qwen2.5-32B-Instruct-FineTune
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-02
- **Last Updated**: 2025-10-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

---
license: apache-2.0
---

针对Qwen2.5-32B-Intruct，采用 SFT方式做的微调，尝试提高在医学方向的能力。

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "chengang12345/Qwen2.5-3B-Instruct-FineTunee"

model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]