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ForgeWorks
/
ForgePlex-M2-9M

Text Generation
Transformers
Safetensors
English
forgeplex_m2
language-model
forgeplex
forgeworks
rope
swiglu
gqa
attn-output-gate
refresh-gate
custom_code
Model card Files Files and versions
xet
Community

Instructions to use ForgeWorks/ForgePlex-M2-9M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ForgeWorks/ForgePlex-M2-9M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="ForgeWorks/ForgePlex-M2-9M", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("ForgeWorks/ForgePlex-M2-9M", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use ForgeWorks/ForgePlex-M2-9M with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "ForgeWorks/ForgePlex-M2-9M"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "ForgeWorks/ForgePlex-M2-9M",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/ForgeWorks/ForgePlex-M2-9M
  • SGLang

    How to use ForgeWorks/ForgePlex-M2-9M 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 "ForgeWorks/ForgePlex-M2-9M" \
        --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": "ForgeWorks/ForgePlex-M2-9M",
    		"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 "ForgeWorks/ForgePlex-M2-9M" \
            --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": "ForgeWorks/ForgePlex-M2-9M",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use ForgeWorks/ForgePlex-M2-9M with Docker Model Runner:

    docker model run hf.co/ForgeWorks/ForgePlex-M2-9M
ForgePlex-M2-9M
40.1 MB
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  • 1 contributor
History: 6 commits
Jsandero's picture
Jsandero
Update README.md
65c4674 verified about 1 hour ago
  • .gitattributes
    1.52 kB
    initial commit about 2 hours ago
  • .gitignore
    35 Bytes
    Add ForgePlex-M2-9M weights (peak Index 8.51 @ 452800, ~9.95M) about 1 hour ago
  • README.md
    2.79 kB
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  • __init__.py
    223 Bytes
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  • config.json
    845 Bytes
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  • configuration_forgeplex_m2.py
    1.98 kB
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  • generation_config.json
    177 Bytes
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  • model.safetensors
    39.8 MB
    xet
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  • modeling_forgeplex_m2.py
    15.7 kB
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  • special_tokens_map.json
    129 Bytes
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  • tokenizer.json
    263 kB
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  • tokenizer_config.json
    712 Bytes
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  • usage.py
    678 Bytes
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