Troubleshooting#

Common issues and frequently asked questions for Ray Serve LLM.

Frequently asked questions#

How do I use gated Hugging Face models?#

You can use runtime_env to specify the env variables that are required to access the model. To get the deployment options, you can use the get_deployment_options method on the LLMServer class. Each deployment class has its own get_deployment_options method.

from ray import serve
from ray.serve.llm import LLMConfig
from ray.serve.llm.deployment import LLMServer
from ray.serve.llm.ingress import OpenAiIngress
from ray.serve.llm.builders import build_openai_app

import os

llm_config = LLMConfig(
    model_loading_config=dict(
        model_id="llama-3-8b-instruct",
        model_source="meta-llama/Meta-Llama-3-8B-Instruct",
    ),
    deployment_config=dict(
        autoscaling_config=dict(
            min_replicas=1, max_replicas=2,
        )
    ),
    # Pass the desired accelerator type (e.g., A10G, L4, etc.)
    accelerator_type="A10G",
    runtime_env=dict(
        env_vars=dict(
            HF_TOKEN=os.environ["HF_TOKEN"]
        )
    ),
)

app = build_openai_app({"llm_configs": [llm_config]})
serve.run(app, blocking=True)

Why is downloading the model so slow?#

If you’re using Hugging Face models, you can enable fast download by setting HF_HUB_ENABLE_HF_TRANSFER and installing pip install hf_transfer.

from ray import serve
from ray.serve.llm import LLMConfig
from ray.serve.llm.deployment import LLMServer
from ray.serve.llm.ingress import OpenAiIngress
from ray.serve.llm.builders import build_openai_app
import os

llm_config = LLMConfig(
    model_loading_config=dict(
        model_id="llama-3-8b-instruct",
        model_source="meta-llama/Meta-Llama-3-8B-Instruct",
    ),
    deployment_config=dict(
        autoscaling_config=dict(
            min_replicas=1, max_replicas=2,
        )
    ),
    # Pass the desired accelerator type (e.g., A10G, L4, etc.)
    accelerator_type="A10G",
    runtime_env=dict(
        env_vars=dict(
            HF_TOKEN=os.environ["HF_TOKEN"],
            HF_HUB_ENABLE_HF_TRANSFER="1"
        )
    ),
)

# Deploy the application
app = build_openai_app({"llm_configs": [llm_config]})
serve.run(app, blocking=True)

Get help#

If you encounter issues not covered in this guide:

See also#