Updating the doc (we keep the list actually).

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Nicolas Patry 2024-10-14 15:19:02 +02:00
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"description": "Hugging Face Text Generation Inference API" "description": "Hugging Face Text Generation Inference API"
} }
] ]
} }

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@ -34,4 +34,18 @@ Text Generation Inference enables serving optimized models. The following sectio
- [Idefics](https://huggingface.co/HuggingFaceM4/idefics-9b) (Multimodal) - [Idefics](https://huggingface.co/HuggingFaceM4/idefics-9b) (Multimodal)
If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models. Read more about [Non-core Model Serving](../basic_tutorials/non_core_models).
If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models:
```python
# for causal LMs/text-generation models
AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
# or, for text-to-text generation models
AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")
```
If you wish to serve a supported model that already exists on a local folder, just point to the local folder.
```bash
text-generation-launcher --model-id <PATH-TO-LOCAL-BLOOM>
```

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@ -9,6 +9,8 @@ TEMPLATE = """
Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported. Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported.
SUPPORTED_MODELS
If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models: If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models: