README.md

---
library_name: transformers
model_name: Qwen3-0.6B-Alpaca
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
datasets:
- RaagulQB/alpaca_coding_dataset_full
- tatsu-lab/alpaca
base_model:
- Qwen/Qwen3-0.6B-Base
---

# Model Card for Qwen3-0.6B-Alpaca

This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name='wesjos/Qwen3-0.6B-Alpaca'

model=AutoModelForCausalLM.from_pretrained(model_name)
tokenizer=AutoTokenizer.from_pretrained(model_name)

alpaca_prompt = """"Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
"""

inputs = tokenizer(
[
    alpaca_prompt.format(
        "完成以下代码要求", # instruction
        "使用python写一个transformer神经网络" #Input
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True,temperature=0.6,do_sample=True,top_p=0.95,top_k=20)
print(tokenizer.batch_decode(outputs)[0])
```

## Training procedure

 


This model was trained with SFT.

### Framework versions

- TRL: 0.23.0
- Transformers: 4.57.1
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.22.1

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```