![]() SeamlessM4T-v2 is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. Prompt = "USER: \nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud \nASSISTANT:" outputs = pipe( image, prompt = prompt, generate_kwargs =)Īnd you can find all Llava weights under llava-hf organisation on the Hub. The mode is compatible with "image-to-text" pipeline:įrom transformers import pipeline from PIL import Image import requests model_id = "llava-hf/llava-1.5-7b-hf" pipe = pipeline( "image-to-text", model = model_id) The integration also includes BakLlava which is a Llava model trained with Mistral backbone. Add Llava to transformers by in #27662.The Llava model was proposed in Improved Baselines with Visual Instruction Tuning by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions. It is an auto-regressive language model, based on the transformer architecture. Llava is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. The checkpoints are release under mistralai organisation on the Hugging Face Hub. The model is compatible with existing optimisation tools such Flash Attention 2, bitsandbytes and PEFT library. generate( ** model_inputs, max_new_tokens = 100, do_sample = True) > prompt = "My favourite condiment is" > model_inputs = tokenizer(, return_tensors = "pt"). ![]() from_pretrained( "mistralai/Mistral-8x7B") from_pretrained( "mistralai/Mixtral-8x7B", torch_dtype = torch. import torch > from transformers import AutoModelForCausalLM, AutoTokenizer > model = AutoModelForCausalLM.
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