Bump transformers from 4.23.1 to 4.36.0 in /exa/models/unilm-master/xdoc/fine_tuning/squad

[dependabot]

Bumps transformers from 4.23.1 to 4.36.0.

Release notes

Sourced from transformers's releases.

v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2, AMD ROCm, F.sdpa wide-spread support

New model additions

Mixtral

Mixtral is the new open-source model from Mistral AI announced by the blogpost 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:

>>> 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 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 by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.

• [Llava] Add Llava to transformers by @​younesbelkada in #27662
• [LLaVa] Some improvements by @​NielsRogge in #27895

The integration also includes BakLlava which is a Llava model trained with Mistral backbone.

The mode is compatible with "image-to-text" pipeline:

from transformers import pipeline
from PIL import Image    
import requests
model_id = "llava-hf/llava-1.5-7b-hf"
</tr></table>

... (truncated)

Commits

• 1466677 Release: v4.36.0
• accccdd [Add Mixtral] Adds support for the Mixtral MoE (#27942)
• 0676d99 [from_pretrained] Make from_pretrained fast again (#27709)
• 9f18cc6 Fix SDPA dispatch & make SDPA CI compatible with torch<2.1.1 (#27940)
• 7ea21f1 [LLaVa] Some improvements (#27895)
• 5e620a9 Fix SeamlessM4Tv2ModelIntegrationTest (#27911)
• e96c1de Skip UnivNetModelTest::test_multi_gpu_data_parallel_forward (#27912)
• 8d8970e [BEiT] Fix test (#27934)
• 235be08 [DETA] fix backbone freeze/unfreeze function (#27843)
• df5c5c6 Fix typo (#27918)
• Additional commits viewable in compare view

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