chore(deps): bump transformers from 4.33.3 to 4.36.0 in /site/utils/embeddings

[dependabot]

Bumps transformers from 4.33.3 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

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
  You can disable automated security fix PRs for this repo from the Security Alerts page.

[ajfisher]

@dependabot merge

…

On Thu, 21 Dec 2023, 08:12 dependabot[bot], ***@***.***> wrote: This automated pull request fixes a security vulnerability <https://github.com/ajfisher/ajfisher.me/security/dependabot/96> (critical severity). Learn more about Dependabot security updates <https://docs.github.com/github/managing-security-vulnerabilities/configuring-dependabot-security-updates>. ------------------------------ Bumps transformers <https://github.com/huggingface/transformers> from 4.33.3 to 4.36.0. Release notes *Sourced from transformers's releases <https://github.com/huggingface/transformers/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 <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: >>> 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://github.com/younesbelkada> in #27662 <https://redirect.github.com/huggingface/transformers/issues/27662> - [LLaVa] Some improvements by @​NielsRogge <https://github.com/NielsRogge> in #27895 <https://redirect.github.com/huggingface/transformers/issues/27895> The integration also includes BakLlava <https://github.com/SkunkworksAI/BakLLaVA> which is a Llava model trained with Mistral backbone. The mode is compatible with "image-to-text" pipeline: from transformers import pipelinefrom PIL import Image import requestsmodel_id = "llava-hf/llava-1.5-7b-hf"</tr></table> ... (truncated) Commits - 1466677 <huggingface/transformers@1466677> Release: v4.36.0 - accccdd <huggingface/transformers@accccdd> [Add Mixtral] Adds support for the Mixtral MoE (#27942 <https://redirect.github.com/huggingface/transformers/issues/27942>) - 0676d99 <huggingface/transformers@0676d99> [from_pretrained] Make from_pretrained fast again (#27709 <https://redirect.github.com/huggingface/transformers/issues/27709>) - 9f18cc6 <huggingface/transformers@9f18cc6> Fix SDPA dispatch & make SDPA CI compatible with torch<2.1.1 (#27940 <https://redirect.github.com/huggingface/transformers/issues/27940>) - 7ea21f1 <huggingface/transformers@7ea21f1> [LLaVa] Some improvements (#27895 <https://redirect.github.com/huggingface/transformers/issues/27895>) - 5e620a9 <huggingface/transformers@5e620a9> Fix SeamlessM4Tv2ModelIntegrationTest (#27911 <https://redirect.github.com/huggingface/transformers/issues/27911>) - e96c1de <huggingface/transformers@e96c1de> Skip UnivNetModelTest::test_multi_gpu_data_parallel_forward (#27912 <https://redirect.github.com/huggingface/transformers/issues/27912>) - 8d8970e <huggingface/transformers@8d8970e> [BEiT] Fix test (#27934 <https://redirect.github.com/huggingface/transformers/issues/27934>) - 235be08 <huggingface/transformers@235be08> [DETA] fix backbone freeze/unfreeze function (#27843 <https://redirect.github.com/huggingface/transformers/issues/27843>) - df5c5c6 <huggingface/transformers@df5c5c6> Fix typo (#27918 <https://redirect.github.com/huggingface/transformers/issues/27918>) - Additional commits viewable in compare view <huggingface/transformers@v4.33.3...v4.36.0> [image: Dependabot compatibility score] <https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores> Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase. ------------------------------ Dependabot commands and options You can trigger Dependabot actions by commenting on this PR: - @dependabot rebase will rebase this PR - @dependabot recreate will recreate this PR, overwriting any edits that have been made to it - @dependabot merge will merge this PR after your CI passes on it - @dependabot squash and merge will squash and merge this PR after your CI passes on it - @dependabot cancel merge will cancel a previously requested merge and block automerging - @dependabot reopen will reopen this PR if it is closed - @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency - @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the Security Alerts page <https://github.com/ajfisher/ajfisher.me/network/alerts>. ------------------------------ You can view, comment on, or merge this pull request online at: #180 Commit Summary - 64bca6f <64bca6f> chore(deps): bump transformers in /site/utils/embeddings File Changes (1 file <https://github.com/ajfisher/ajfisher.me/pull/180/files>) - *M* site/utils/embeddings/requirements.txt <https://github.com/ajfisher/ajfisher.me/pull/180/files#diff-56dbdfaef2bf8f7c4cbb8ecc8f0486446b937133ddbbf89f9b708244d7ecd521> (2) Patch Links: - https://github.com/ajfisher/ajfisher.me/pull/180.patch - https://github.com/ajfisher/ajfisher.me/pull/180.diff — Reply to this email directly, view it on GitHub <#180>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAA5DI7NNQF6HIAJ6UN3KUDYKNIFPAVCNFSM6AAAAABA5NZG7CVHI2DSMVQWIX3LMV43ASLTON2WKOZSGA2TCMRXGUZTCMY> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>