Bump transformers from 4.34.0 to 4.36.0 in /backend/vector_store #43

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dependabot[bot] wants to merge 1 commit from dependabot/pip/backend/vector_store/transformers-4.36.0 into main

dependabot[bot] commented

2023-12-24 17:17:42 +01:00

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Bumps transformers from 4.34.0 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)
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Bumps [transformers](https://github.com/huggingface/transformers) from 4.34.0 to 4.36.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/huggingface/transformers/releases">transformers's releases</a>.</em></p> <blockquote> <h2>v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2, AMD ROCm, F.sdpa wide-spread support</h2> <h2>New model additions</h2> <h3>Mixtral</h3> <p>Mixtral is the new open-source model from Mistral AI announced by the blogpost <a href="https://mistral.ai/news/mixtral-of-experts/">Mixtral of Experts</a>. The model has been proven to have comparable capabilities to Chat-GPT according to the benchmark results shared on the release blogpost.</p>  <p>The architecture is a sparse Mixture of Experts with Top-2 routing strategy, similar as <code>NllbMoe</code> architecture in transformers. You can use it through <code>AutoModelForCausalLM</code> interface:</p> <pre lang="py"><code>>>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer <p>>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B", torch_dtype=torch.float16, device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B")</p> <p>>>> prompt = "My favourite condiment is"</p> <p>>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device) >>> model.to(device)</p> <p>>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) >>> tokenizer.batch_decode(generated_ids)[0] </code></pre></p> <p>The model is compatible with existing optimisation tools such Flash Attention 2, <code>bitsandbytes</code> and PEFT library. The checkpoints are release under <a href="https://huggingface.co/mistralai"><code>mistralai</code></a> organisation on the Hugging Face Hub.</p> <h3>Llava / BakLlava</h3> <p>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.</p>  <p>The Llava model was proposed in <a href="https://arxiv.org/pdf/2310.03744">Improved Baselines with Visual Instruction Tuning</a> by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.</p> <ul> <li>[<code>Llava</code>] Add Llava to transformers by <a href="https://github.com/younesbelkada"><code>@younesbelkada</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/27662">#27662</a></li> <li>[LLaVa] Some improvements by <a href="https://github.com/NielsRogge"><code>@NielsRogge</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/27895">#27895</a></li> </ul> <p>The integration also includes <a href="https://github.com/SkunkworksAI/BakLLaVA"><code>BakLlava</code></a> which is a Llava model trained with Mistral backbone.</p> <p>The mode is compatible with <code>"image-to-text"</code> pipeline:</p> <pre lang="py"><code>from transformers import pipeline from PIL import Image import requests <p>model_id = "llava-hf/llava-1.5-7b-hf" </tr></table> </code></pre></p> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/huggingface/transformers/commit/14666775a296a76c88e1aa686a9547f393d322e2"><code>1466677</code></a> Release: v4.36.0</li> <li><a href="https://github.com/huggingface/transformers/commit/accccdd0087263a1e494e9c9ec30a43043ff3905"><code>accccdd</code></a> [<code>Add Mixtral</code>] Adds support for the Mixtral MoE (<a href="https://redirect.github.com/huggingface/transformers/issues/27942">#27942</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/0676d992a5c1f6107a611018494ec952613a4d7f"><code>0676d99</code></a> [<code>from_pretrained</code>] Make from_pretrained fast again (<a href="https://redirect.github.com/huggingface/transformers/issues/27709">#27709</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/9f18cc6df0b7e0d50f78b9e9fcb3aafa7b5160fe"><code>9f18cc6</code></a> Fix SDPA dispatch & make SDPA CI compatible with torch<2.1.1 (<a href="https://redirect.github.com/huggingface/transformers/issues/27940">#27940</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/7ea21f1f035d683cc39a0c0f4b2605175e1dcfdf"><code>7ea21f1</code></a> [LLaVa] Some improvements (<a href="https://redirect.github.com/huggingface/transformers/issues/27895">#27895</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/5e620a92cf7e6c312435db86ec55e13b75dece75"><code>5e620a9</code></a> Fix <code>SeamlessM4Tv2ModelIntegrationTest</code> (<a href="https://redirect.github.com/huggingface/transformers/issues/27911">#27911</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/e96c1de1913c307fddcb3e5881388a6dbb5b00b1"><code>e96c1de</code></a> Skip <code>UnivNetModelTest::test_multi_gpu_data_parallel_forward</code> (<a href="https://redirect.github.com/huggingface/transformers/issues/27912">#27912</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/8d8970efdd0e21b54f1c82dec21e8a5eeba609a1"><code>8d8970e</code></a> [BEiT] Fix test (<a href="https://redirect.github.com/huggingface/transformers/issues/27934">#27934</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/235be08569000a5361354f766972e653212bf0d3"><code>235be08</code></a> [DETA] fix backbone freeze/unfreeze function (<a href="https://redirect.github.com/huggingface/transformers/issues/27843">#27843</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/df5c5c62ae253055336f5bb0828ca8e3e15ab6bd"><code>df5c5c6</code></a> Fix typo (<a href="https://redirect.github.com/huggingface/transformers/issues/27918">#27918</a>)</li> <li>Additional commits viewable in <a href="https://github.com/huggingface/transformers/compare/v4.34.0...v4.36.0">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=transformers&package-manager=pip&previous-version=4.34.0&new-version=4.36.0)](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-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> 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. 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dependabot[bot] commented

2024-01-09 22:09:34 +01:00

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Dependabot couldn't find a requirements.txt. Because of this, Dependabot cannot update this pull request.

joshniemela commented

2024-01-10 15:32:33 +01:00

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VDB no longer uses python

dependabot[bot] commented

2024-01-10 15:32:36 +01:00

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If you change your mind, just re-open this PR and I'll resolve any conflicts on it.

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