Instructions to use Thomaschtl/qwen3-06b-qat-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thomaschtl/qwen3-06b-qat-test with Transformers:

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

pipe = pipeline("text-generation", model="Thomaschtl/qwen3-06b-qat-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Thomaschtl/qwen3-06b-qat-test", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Thomaschtl/qwen3-06b-qat-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thomaschtl/qwen3-06b-qat-test"
# 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/qwen3-06b-qat-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Thomaschtl/qwen3-06b-qat-test

SGLang

How to use Thomaschtl/qwen3-06b-qat-test 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/qwen3-06b-qat-test" \
    --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/qwen3-06b-qat-test",
		"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/qwen3-06b-qat-test" \
        --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/qwen3-06b-qat-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Thomaschtl/qwen3-06b-qat-test with Docker Model Runner:
```
docker model run hf.co/Thomaschtl/qwen3-06b-qat-test
```

Thomaschtl commited on Jun 3, 2025

Commit

c23b8e7

verified ·

1 Parent(s): 4f44cfb

Upload .ipynb_checkpoints/quantization_config-checkpoint.json with huggingface_hub

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Files changed (1) hide show

.ipynb_checkpoints/quantization_config-checkpoint.json +9 -0

.ipynb_checkpoints/quantization_config-checkpoint.json ADDED Viewed

	@@ -0,0 +1,9 @@

+{
+  "quantization_method": "qat",
+  "framework": "neural_compressor",
+  "base_model": "Qwen/Qwen3-0.6B",
+  "optimizer": "AdamW",
+  "learning_rate": 5e-05,
+  "epochs": 1,
+  "batch_size": 2
+}