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Use Fal for serverless cloud deployments with lightning-fast inference, autoscaling, and easy API access.

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Clone the repository

git clone https://github.com/Liquid4All/lfm-inference

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Deployment

cd fal

# run one-off server
fal run deploy-lfm2.py::serve

# run production server
fal deploy deploy-lfm2.py::serve --app-name lfm2-8b --auth private
The first run will require extra time to download the docker image and model weights.

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Test call

First, create an API key here. Then run the following cURL commands: 
export FAL_API_KEY=<your-fal-api-key>

# List deployed model
curl https://fal.run/<org-id>/<app-id>/v1/models -H "Authorization: Key $FAL_API_KEY"

# Query the deployed LFM model
curl -X POST https://fal.run/<org-id>/<app-id>/v1/chat/completions \
  -H "Authorization: Key $FAL_API_KEY" \
  --json '{
    "model": "LiquidAI/LFM2-8B-A1B",
    "messages": [
      {
        "role": "user",
        "content": "What is the melting temperature of silver?"
      }
    ],
    "max_tokens": 32,
    "temperature": 0
  }'
Fal endpoints expect the Key prefix in the Authorization header.