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

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

# 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)

# 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:

@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 on this repository.

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