Using Hugging Face Inference Endpoints or TGI as Backend

An alternative to launching the evaluation locally is to serve the model on a TGI-compatible server/container and then run the evaluation by sending requests to the server. The command is the same as before, except you specify a path to a YAML configuration file (detailed below):

lighteval endpoint {tgi,inference-endpoint} \
    "/path/to/config/file" \
    <task_parameters>

There are two types of configuration files that can be provided for running on the server:

Hugging Face Inference Endpoints

To launch a model using Hugging Face’s Inference Endpoints, you need to provide the following file: endpoint_model.yaml. Lighteval will automatically deploy the endpoint, run the evaluation, and finally delete the endpoint (unless you specify an endpoint that was already launched, in which case the endpoint won’t be deleted afterwards).

Configuration File Example

model_parameters:
    reuse_existing: false # If true, ignore all params in instance, and don't delete the endpoint after evaluation
    # endpoint_name: "llama-2-7B-lighteval" # Needs to be lowercase without special characters
    model_name: "meta-llama/Llama-2-7b-hf"
    revision: "main"  # Defaults to "main"
    dtype: "float16" # Can be any of "awq", "eetq", "gptq", "4bit" or "8bit" (will use bitsandbytes), "bfloat16" or "float16"
    accelerator: "gpu"
    region: "eu-west-1"
    vendor: "aws"
    instance_type: "nvidia-a10g"
    instance_size: "x1"
    framework: "pytorch"
    endpoint_type: "protected"
    namespace: null # The namespace under which to launch the endpoint. Defaults to the current user's namespace
    image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models.
    env_vars: null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048`

Text Generation Inference (TGI)

To use a model already deployed on a TGI server, for example on Hugging Face’s serverless inference.

Configuration File Example

model_parameters:
    inference_server_address: ""
    inference_server_auth: null
    model_id: null # Optional, only required if the TGI container was launched with model_id pointing to a local directory

Key Parameters

Hugging Face Inference Endpoints

Model Configuration

• model_name: The Hugging Face model ID to deploy
• revision: Model revision (defaults to “main”)
• dtype: Data type for model weights (“float16”, “bfloat16”, “4bit”, “8bit”, etc.)
• framework: Framework to use (“pytorch”, “tensorflow”)

Infrastructure Settings

• accelerator: Hardware accelerator (“gpu”, “cpu”)
• region: AWS region for deployment
• vendor: Cloud vendor (“aws”, “azure”, “gcp”)
• instance_type: Instance type (e.g., “nvidia-a10g”, “nvidia-t4”)
• instance_size: Instance size (“x1”, “x2”, etc.)

Endpoint Configuration

• endpoint_type: Endpoint access level (“public”, “protected”, “private”)
• namespace: Organization namespace for deployment
• reuse_existing: Whether to reuse an existing endpoint
• endpoint_name: Custom endpoint name (lowercase, no special characters)

Advanced Settings

• image_url: Custom Docker image URL
• env_vars: Environment variables for the endpoint

Text Generation Inference (TGI)

Server Configuration

• inference_server_address: URL of the TGI server
• inference_server_auth: Authentication credentials
• model_id: Model identifier (if using local model directory)

Usage Examples

Deploying a New Inference Endpoint

lighteval endpoint inference-endpoint \
    "configs/endpoint_model.yaml" \
    "lighteval|gsm8k|0"

Using an Existing TGI Server

lighteval endpoint tgi \
    "configs/tgi_server.yaml" \
    "lighteval|gsm8k|0"

Reusing an Existing Endpoint

model_parameters:
    reuse_existing: true
    endpoint_name: "my-existing-endpoint"
    # Other parameters will be ignored when reuse_existing is true

Cost Management

Inference Endpoints

• Endpoints are automatically deleted after evaluation (unless reuse_existing: true)
• Costs are based on instance type and runtime
• Monitor usage in the Hugging Face billing dashboard

TGI Servers

• No additional costs beyond your existing server infrastructure
• Useful for cost-effective evaluation of already-deployed models

Troubleshooting

Common Issues

1. Endpoint Deployment Failures: Check instance availability in your region
2. Authentication Errors: Ensure proper Hugging Face token permissions
3. Model Loading Errors: Verify model name and revision are correct
4. Resource Constraints: Choose appropriate instance type for your model size

Performance Tips

• Use appropriate instance types for your model size
• Consider using quantized models (4bit, 8bit) for cost savings
• Reuse existing endpoints for multiple evaluations
• Use serverless TGI for cost-effective evaluation

Error Handling

Common error messages and solutions:

• “Instance not available”: Try a different region or instance type
• “Model not found”: Check the model name and revision
• “Insufficient permissions”: Verify your Hugging Face token has endpoint deployment permissions
• “Endpoint already exists”: Use reuse_existing: true or choose a different endpoint name

For more detailed information about Hugging Face Inference Endpoints, see the official documentation.