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aixk
/
fastplus-42m

Text Generation
Transformers
Safetensors
English
Korean
fastplus_40m
Model card Files Files and versions
xet
Community

Instructions to use aixk/fastplus-42m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use aixk/fastplus-42m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="aixk/fastplus-42m")
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("aixk/fastplus-42m", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use aixk/fastplus-42m with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "aixk/fastplus-42m"
    # 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-42m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/aixk/fastplus-42m
  • SGLang

    How to use aixk/fastplus-42m 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-42m" \
        --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-42m",
    		"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-42m" \
            --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-42m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use aixk/fastplus-42m with Docker Model Runner:

    docker model run hf.co/aixk/fastplus-42m
fastplus-42m
759 MB
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  • 1 contributor
History: 45 commits
aixk's picture
aixk
Create README.md
d675d8c verified about 1 month ago
  • checkpoint-800
    FastPlus40m Backup checkpoint-800 at global step 800 about 1 month ago
  • checkpoint-968
    FastPlus40m Backup checkpoint-968 at global step 968 about 1 month ago
  • .gitattributes
    1.65 kB
    Upload model_fastplus_40m_4bit.vlite with huggingface_hub about 1 month ago
  • README.md
    14.2 kB
    Create README.md about 1 month ago
  • config.json
    436 Bytes
    FastPlus-40M Relativistic SVO Dynamic-Soft-Pooling Model Train Completed Safely about 1 month ago
  • generation_config.json
    132 Bytes
    FastPlus-40M Relativistic SVO Dynamic-Soft-Pooling Model Train Completed Safely about 1 month ago
  • model.safetensors
    160 MB
    xet
    FastPlus-40M Relativistic SVO Dynamic-Soft-Pooling Model Train Completed Safely about 1 month ago
  • model_fastplus_40m.vlite
    164 MB
    xet
    Upload model_fastplus_40m.vlite with huggingface_hub about 1 month ago
  • model_fastplus_40m_4bit.vlite
    21.6 MB
    xet
    Upload model_fastplus_40m_4bit.vlite with huggingface_hub about 1 month ago
  • tokenizer.json
    524 kB
    Upload folder using huggingface_hub about 1 month ago
  • tokenizer_config.json
    289 Bytes
    Upload folder using huggingface_hub about 2 months ago
  • training_args.bin
    5.2 kB
    xet
    FastPlus-40M Relativistic SVO Dynamic-Soft-Pooling Model Train Completed Safely about 1 month ago