# 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 {class}`LLMServer <ray.serve.llm.deployment.LLMServer>` class. Each deployment class has its own `get_deployment_options` method.

```python
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`.

```python
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:

- [Ray GitHub Issues](https://github.com/ray-project/ray/issues) - Report bugs or request features
- [Ray Slack](https://ray-distributed.slack.com) - Get help from the community
- [Ray Discourse Forum](https://discuss.ray.io) - Ask questions and share knowledge

## See also

- {doc}`Quickstart examples <quick-start>`
- {doc}`Examples <examples>`