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https://github.com/huggingface/text-generation-inference.git
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93a7042d7e
* feat: support phi3.5 moe model loading * fix: prefer llama base model and improve rotary logic * feat: return reasonable generation and add integration test * fix: run lint and update docs * fix: rerun lint for openapi docs * fix: prefer do_sample false unless temp is set by user, and update chat tests * fix: small typo adjustments * fix: consolidate long rope paths * fix: revert greedy by default and test changes * Vendor configuration so that we don't have to `trust_remote_code` * Use SparseMoELayer * Add support for dense MoE * Some type annotations * Add the usual model tests * Ruff. --------- Co-authored-by: Daniël de Kok <me@danieldk.eu> Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models (VLMs & LLMs) are supported.
Supported Models
- Deepseek V2
- Idefics 2 (Multimodal)
- Llava Next (1.6) (Multimodal)
- Llama
- Phi 3
- Gemma
- PaliGemma
- Gemma2
- Cohere
- Dbrx
- Mamba
- Mistral
- Mixtral
- Gpt Bigcode
- Phi
- PhiMoe
- Baichuan
- Falcon
- StarCoder 2
- Qwen 2
- Opt
- T5
- Galactica
- SantaCoder
- Bloom
- Mpt
- Gpt2
- Gpt Neox
- Gptj
- Idefics (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:
# 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.
text-generation-launcher --model-id <PATH-TO-LOCAL-BLOOM>