Instructions to use aixk/fastplus-44m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aixk/fastplus-44m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aixk/fastplus-44m")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("aixk/fastplus-44m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aixk/fastplus-44m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aixk/fastplus-44m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aixk/fastplus-44m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aixk/fastplus-44m
- SGLang
How to use aixk/fastplus-44m 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 "aixk/fastplus-44m" \ --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": "aixk/fastplus-44m", "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 "aixk/fastplus-44m" \ --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": "aixk/fastplus-44m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aixk/fastplus-44m with Docker Model Runner:
docker model run hf.co/aixk/fastplus-44m
- Xet hash:
- f5b85b3de2d8505549eb638325afc52df3fe1fa2ba27ab415f839719340f2cf3
- Size of remote file:
- 179 MB
- SHA256:
- 48599f975b0c09ffa819ad9c19841a1381f0df30b39cf3f148802fc3bf857e62
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