Instructions to use xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale with Transformers:

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

pipe = pipeline("text-generation", model="xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale")
model = AutoModelForCausalLM.from_pretrained("xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale

SGLang

How to use xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale 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 "xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale" \
    --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": "xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale",
		"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 "xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale" \
        --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": "xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale with Docker Model Runner:
```
docker model run hf.co/xaobai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale
```

Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_vigilant_nightingale / model.safetensors

xaobai

rl-swarm: round 36199, agent amphibious_vigilant_nightingale

075cdad verified 10 months ago

Download

History Blame Contribute Delete

1.98 GB

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

Xet Pointer Details

( Raw pointer file )

Size of remote file:: 1.98 GB
SHA256:: fd304f5c2f2b708b661d88d86671eab875bc79bffa6b8e033571fd27c1ce0c5f

Git Large File Storage (LFS) replaces large files with text pointers inside Git, while storing the file contents on a remote server. More info.