Instructions to use RenaudGaudron/Qwen3-0.6B-Connect4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RenaudGaudron/Qwen3-0.6B-Connect4 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B-Base")
model = PeftModel.from_pretrained(base_model, "RenaudGaudron/Qwen3-0.6B-Connect4")

Transformers

How to use RenaudGaudron/Qwen3-0.6B-Connect4 with Transformers:

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

pipe = pipeline("text-generation", model="RenaudGaudron/Qwen3-0.6B-Connect4")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RenaudGaudron/Qwen3-0.6B-Connect4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RenaudGaudron/Qwen3-0.6B-Connect4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RenaudGaudron/Qwen3-0.6B-Connect4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RenaudGaudron/Qwen3-0.6B-Connect4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RenaudGaudron/Qwen3-0.6B-Connect4

SGLang

How to use RenaudGaudron/Qwen3-0.6B-Connect4 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 "RenaudGaudron/Qwen3-0.6B-Connect4" \
    --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": "RenaudGaudron/Qwen3-0.6B-Connect4",
		"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 "RenaudGaudron/Qwen3-0.6B-Connect4" \
        --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": "RenaudGaudron/Qwen3-0.6B-Connect4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RenaudGaudron/Qwen3-0.6B-Connect4 with Docker Model Runner:
```
docker model run hf.co/RenaudGaudron/Qwen3-0.6B-Connect4
```

Qwen3-0.6B-Connect4 / adapter_model.safetensors

RenaudGaudron

Upload 4 files (#1)

09015e3 verified 11 months ago

Download

History Blame

162 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d86c94cbb89704e64639859d3036a783ef8de861ee9ba16c021ff1a8de67a8d3
Size of remote file:: 162 MB
SHA256:: b66474a92f61223dcbd73507a84ea9d78a35e3808edbaf650e1706578d852eac

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.