Instructions to use JunHowie/Qwen3-0.6B-GPTQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JunHowie/Qwen3-0.6B-GPTQ-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="JunHowie/Qwen3-0.6B-GPTQ-Int4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JunHowie/Qwen3-0.6B-GPTQ-Int4")
model = AutoModelForCausalLM.from_pretrained("JunHowie/Qwen3-0.6B-GPTQ-Int4")
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 JunHowie/Qwen3-0.6B-GPTQ-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JunHowie/Qwen3-0.6B-GPTQ-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JunHowie/Qwen3-0.6B-GPTQ-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JunHowie/Qwen3-0.6B-GPTQ-Int4

SGLang

How to use JunHowie/Qwen3-0.6B-GPTQ-Int4 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 "JunHowie/Qwen3-0.6B-GPTQ-Int4" \
    --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": "JunHowie/Qwen3-0.6B-GPTQ-Int4",
		"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 "JunHowie/Qwen3-0.6B-GPTQ-Int4" \
        --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": "JunHowie/Qwen3-0.6B-GPTQ-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JunHowie/Qwen3-0.6B-GPTQ-Int4 with Docker Model Runner:
```
docker model run hf.co/JunHowie/Qwen3-0.6B-GPTQ-Int4
```

license

by pytokusu - opened Sep 3, 2025

Discussion

pytokusu

Sep 3, 2025

Hello!

Please add Apache 2.0 license type to the model (as in the base model) so that we can use it normally.

Thanks in advance.

JunHowie

Owner Sep 3, 2025

I’m about to re-quantize the Qwen3 series models. Let’s start with the 0.6B Int4 version. Please check back in a few hours—I’ll have the new model weights ready along with the updated model card (including the license).

JunHowie

Owner Sep 3, 2025

Please check the model card. I’ve updated some notes and the license

JunHowie changed discussion status to closed Sep 3, 2025

pytokusu

Sep 3, 2025

Everything is great. Many thanks!

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