Instructions to use team-suzuki/Qwen3-4B-SFT-TEST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use team-suzuki/Qwen3-4B-SFT-TEST2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="team-suzuki/Qwen3-4B-SFT-TEST2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("team-suzuki/Qwen3-4B-SFT-TEST2")
model = AutoModelForCausalLM.from_pretrained("team-suzuki/Qwen3-4B-SFT-TEST2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use team-suzuki/Qwen3-4B-SFT-TEST2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "team-suzuki/Qwen3-4B-SFT-TEST2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "team-suzuki/Qwen3-4B-SFT-TEST2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/team-suzuki/Qwen3-4B-SFT-TEST2

SGLang

How to use team-suzuki/Qwen3-4B-SFT-TEST2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "team-suzuki/Qwen3-4B-SFT-TEST2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "team-suzuki/Qwen3-4B-SFT-TEST2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "team-suzuki/Qwen3-4B-SFT-TEST2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "team-suzuki/Qwen3-4B-SFT-TEST2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use team-suzuki/Qwen3-4B-SFT-TEST2 with Docker Model Runner:
```
docker model run hf.co/team-suzuki/Qwen3-4B-SFT-TEST2
```

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.

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

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Safetensors

Model size

0.9B params

Tensor type

F32