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https://github.com/huggingface/text-generation-inference.git
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a35fbdb925
* (fix) sliding window attention * (fix) flashinfer * (typo) collection link * Add window_size_left param ipex rocm * Update window size rocm flash decoding * fix: bump snapshots and improve exceed window test case * feat: add tests for image types and remove alpha from png * Upgrading `from_env` to get token from file when necessary + fix pali_gemma. * fix: add pillow dependency and bump lock+requirements * fix: bump org name in gemma3 test * Fix qwen2. --------- Co-authored-by: drbh <david.richard.holtz@gmail.com> Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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3.4 KiB
Supported Models
Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported.
- Deepseek V2
- Deepseek V3
- Idefics 2 (Multimodal)
- Idefics 3 (Multimodal)
- Llava Next (1.6) (Multimodal)
- Llama
- Phi 3
- Granite
- Gemma
- PaliGemma
- Gemma2
- Gemma3
- Gemma3 Text
- Cohere
- Dbrx
- Mamba
- Mistral
- Mixtral
- Gpt Bigcode
- Phi
- PhiMoe
- Baichuan
- Falcon
- StarCoder 2
- Qwen 2
- Qwen 2 VL
- Qwen 2.5 VL
- Opt
- T5
- Galactica
- SantaCoder
- Bloom
- Mpt
- Gpt2
- Gpt Neox
- Gptj
- Idefics (Multimodal)
- Mllama (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>