---
license: other
base_model: Qwen
tags:
- qwen3
- instruction-tuning
- chat
- transformers
- pytorch
- safetensors
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
---

# Qwen3-4B-SFT-TEST2

## Model Description

Qwen3-4B-SFT-TEST2 is a language model fine-tuned for improved performance on various natural language understanding and generation tasks.

## Model Details

- **Model Name**: Qwen3-4B-SFT-TEST2
- **Base Model**: Qwen
- **Architecture**: Qwen3ForCausalLM
- **Parameters**: ~2B
- **Model Type**: qwen3
- **Total Size**: 3.4GB
- **Upload Date**: 2025-08-18

### Model Architecture

- **Hidden Size**: 2560
- **Number of Layers**: 36
- **Attention Heads**: 32
- **Vocabulary Size**: 151936
- **Max Position Embeddings**: 40960

## Files

This repository contains:

- **SafeTensors format**: Optimized for fast loading and reduced memory usage
- **Tokenizer**: Included for text processing

## Usage

### Loading the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "team-suzuki/Qwen3-4B-SFT-TEST2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)
```

### Text Generation

```python
# Prepare input
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

# Decode output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Chat Format (if applicable)

```python
# For instruction-tuned models
messages = [
    {"role": "user", "content": "What is the capital of Japan?"}
]

# Apply chat template if available
if hasattr(tokenizer, 'apply_chat_template'):
    formatted_input = tokenizer.apply_chat_template(
        messages, 
        add_generation_prompt=True,
        return_tensors="pt"
    )
else:
    formatted_input = tokenizer("User: What is the capital of Japan?\nAssistant:", return_tensors="pt")

# Generate response
outputs = model.generate(
    formatted_input,
    max_new_tokens=100,
    temperature=0.7,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Training Details

- **Training Data**: [Specify training dataset if known]
- **Fine-tuning Method**: [Specify fine-tuning approach]
- **Training Framework**: PyTorch + Transformers
- **Hardware**: [Specify if known]

## Evaluation

[Add evaluation results if available]

## Limitations and Biases

- This model may exhibit biases present in the training data
- Performance may vary across different domains and languages
- Always verify outputs for accuracy and appropriateness

## Ethical Considerations

- Use responsibly and in accordance with applicable laws and regulations
- Be aware of potential biases and limitations
- Consider the impact of generated content

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{qwen3_4b_sft_test2,
  title={Qwen3-4B-SFT-TEST2: A Fine-tuned Language Model},
  author={[Your Name/Organization]},
  year={2025},
  url={https://huggingface.co/team-suzuki/Qwen3-4B-SFT-TEST2}
}
```

## License

This model is released under the other license. Please see the license file for more details.

## Contact

For questions or issues, please [open an issue](https://huggingface.co/team-suzuki/Qwen3-4B-SFT-TEST2/discussions) on this repository.

---

*This model card was automatically generated. Please update with specific details about your model.*