Instructions to use Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake with Transformers:

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

pipe = pipeline("text-generation", model="Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake")
model = AutoModelForCausalLM.from_pretrained("Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake

SGLang

How to use Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake 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 "Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake" \
    --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": "Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake",
		"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 "Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake" \
        --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": "Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake with Docker Model Runner:
```
docker model run hf.co/Daviddforth/Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake
```

Qwen3-0.6B-Gensyn-Swarm-omnivorous_smooth_snake / model.safetensors

Daviddforth

rl-swarm: round 36875, agent omnivorous_smooth_snake

8208763 verified 11 months ago

Download

History Blame Contribute Delete

2.38 GB

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:: 40bab8503b4707fe6eab2c841bd2b100ecd7355617875d11ab5bcafcae4671e3
Size of remote file:: 2.38 GB
SHA256:: 0013beb29261fb78ef2f1932eece31c07e1376794000cb11119d923330bcef1a

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