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d18ed5cfc5
* Working loading state. * Preprocessing. * Working state ? (Broke idefics1 temporarily). * Cleaner condition. * Fix idefics. * Updating config, removing TODO * Mllama * Ugrade transformers 4.45 * Flashing mllama. * Starting to get there. * Working state. * Integrations tests for mllama (cutting to 10 tokens because there seems' to be instability after (meaning size of the batch matters. * Updating model link. * Earlier assert. * Fix vlm ? * remove log. * Force ignore all images but last. * Default dtype bfloat16. * Update integration test after switch to bf16. * Remove dead code. * Removed dead code. * Upgrade the flake to latest transformers/tokenizers * Move to hf tgi-nix * Upgrade to 0.5.0
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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)
- 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>