Text Generation
Transformers
Safetensors
qwen2
rl-swarm
genrl-swarm
grpo
gensyn
I am shiny_stinging_flea
text-generation-inference
Instructions to use Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea") model = AutoModelForCausalLM.from_pretrained("Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea
- SGLang
How to use Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea 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 "Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea" \ --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": "Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea", "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 "Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea" \ --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": "Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea with Docker Model Runner:
docker model run hf.co/Phoenix075/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shiny_stinging_flea
- Xet hash:
- e7474bcb37b792c720626af68f35500f6510fcc79ea90c12e8c1576ad9647595
- Size of remote file:
- 1.98 GB
- SHA256:
- 750032a13c9e6ad85b3bd5a9d7b1f725d61fb24ae9baebe0315826884e99118a
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