Instructions to use Thomaschtl/test3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thomaschtl/test3 with Transformers:

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

pipe = pipeline("text-generation", model="Thomaschtl/test3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Thomaschtl/test3")
model = AutoModelForMultimodalLM.from_pretrained("Thomaschtl/test3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Thomaschtl/test3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thomaschtl/test3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thomaschtl/test3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Thomaschtl/test3

SGLang

How to use Thomaschtl/test3 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 "Thomaschtl/test3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thomaschtl/test3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Thomaschtl/test3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thomaschtl/test3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Thomaschtl/test3 with Docker Model Runner:
```
docker model run hf.co/Thomaschtl/test3
```

test3 / README.md

Thomaschtl

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1c13ab0 verified about 1 year ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3-0.6B
	tags:
	- quantization
	- neural-compressor
	- qat
	- quantization-aware-training
	- qwen3
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Qwen3-0.6B Quantized with QAT

	This model is a quantized version of `Qwen/Qwen3-0.6B` using Quantization Aware Training (QAT) with Intel Neural Compressor.

	## 🚀 Model Details

	- Base Model: Qwen/Qwen3-0.6B
	- Quantization Method: Quantization Aware Training (QAT)
	- Framework: Intel Neural Compressor
	- Model Size: Significantly reduced from original
	- Performance: Maintains quality while improving efficiency

	## 📊 Benefits

	✅ Smaller model size - Reduced storage requirements
	✅ Faster inference - Optimized for deployment
	✅ Lower memory usage - More efficient resource utilization
	✅ Maintained quality - QAT preserves model performance

	## 💻 Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	# Load the quantized model
	model = AutoModelForCausalLM.from_pretrained("Thomaschtl/qwen3-0.6b-qat-test")
	tokenizer = AutoTokenizer.from_pretrained("Thomaschtl/qwen3-0.6b-qat-test")

	# Generate text
	prompt = "The future of AI is"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)

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

	## ⚙️ Quantization Details

	- Training Method: Quantization Aware Training
	- Optimizer: AdamW
	- Learning Rate: 5e-5
	- Batch Size: 2
	- Epochs: 1 (demo configuration)

	## 🔧 Technical Info

	This model was quantized using Intel Neural Compressor's QAT approach, which:
	1. Simulates quantization during training
	2. Allows model weights to adapt to quantization
	3. Maintains better accuracy than post-training quantization

	## 📝 Citation

	If you use this model, please cite:

	```
	@misc{qwen3-qat,
	title={Qwen3-0.6B Quantized with QAT},
	author={Thomaschtl},
	year={2025},
	publisher={Hugging Face},
	url={https://huggingface.co/Thomaschtl/qwen3-0.6b-qat-test}
	}
	```

	## ⚖️ License

	This model follows the same license as the base model (Apache 2.0).