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This PR contains the following updates:
==4.30.0
->==4.36.0
GitHub Vulnerability Alerts
CVE-2023-7018
Deserialization of Untrusted Data in GitHub repository huggingface/transformers prior to 4.36.
CVE-2023-6730
Deserialization of Untrusted Data in GitHub repository huggingface/transformers prior to 4.36.0.
Release Notes
huggingface/transformers (transformers)
### [`v4.36.0`](https://togithub.com/huggingface/transformers/releases/tag/v4.36.0): v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2, AMD ROCm, F.sdpa wide-spread support [Compare Source](https://togithub.com/huggingface/transformers/compare/v4.35.2...v4.36.0) #### New model additions ##### Mixtral Mixtral is the new open-source model from Mistral AI announced by the blogpost [Mixtral of Experts](https://mistral.ai/news/mixtral-of-experts/). The model has been proven to have comparable capabilities to Chat-GPT according to the benchmark results shared on the release blogpost. The architecture is a sparse Mixture of Experts with Top-2 routing strategy, similar as `NllbMoe` architecture in transformers. You can use it through `AutoModelForCausalLM` interface: ```py >>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B", torch_dtype=torch.float16, device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B") >>> prompt = "My favourite condiment is" >>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device) >>> model.to(device) >>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) >>> tokenizer.batch_decode(generated_ids)[0] ``` The model is compatible with existing optimisation tools such Flash Attention 2, `bitsandbytes` and PEFT library. The checkpoints are release under [`mistralai`](https://huggingface.co/mistralai) organisation on the Hugging Face Hub. ##### Llava / BakLlava Llava is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions. The Llava model was proposed in [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee. - \[`Llava`] Add Llava to transformers by [@younesbelkada](https://togithub.com/younesbelkada) in [#27662](https://togithub.com/huggingface/transformers/issues/27662) - \[LLaVa] Some improvements by [@NielsRogge](https://togithub.com/NielsRogge) in [#27895](https://togithub.com/huggingface/transformers/issues/27895) The integration also includes [`BakLlava`](https://togithub.com/SkunkworksAI/BakLLaVA) which is a Llava model trained with Mistral backbone. The mode is compatible with `"image-to-text"` pipeline: ```py from 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) url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = "USER:Configuration
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