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chore(deps): update dependency transformers to v4.43.1 #1330

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merged 1 commit into from
Jul 23, 2024
Merged

chore(deps): update dependency transformers to v4.43.1 #1330

merged 1 commit into from
Jul 23, 2024

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@renovate renovate bot commented Jul 23, 2024 •
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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
transformers ==4.42.4 -> ==4.43.1 age adoption passing confidence

Release Notes

huggingface/transformers (transformers)

v4.43.1: : Patch release

Compare Source

v4.43.0: : Llama 3.1, Chameleon, ZoeDepth, Hiera

Compare Source

Llama

The Llama 3.1 models are released by Meta and come in three flavours: 8B, 70B, and 405B.

To get an overview of Llama 3.1, please visit the Hugging Face announcement blog post.

We release a repository of llama recipes to showcase usage for inference, total and partial fine-tuning of the different variants.

image

Chameleon

The Chameleon model was proposed in Chameleon: Mixed-Modal Early-Fusion Foundation Models by META AI Chameleon Team. Chameleon is a Vision-Language Model that use vector quantization to tokenize images which enables the model to generate multimodal output. The model takes images and texts as input, including an interleaved format, and generates textual response.

ZoeDepth

The ZoeDepth model was proposed in ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth by Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller. ZoeDepth extends the DPT framework for metric (also called absolute) depth estimation. ZoeDepth is pre-trained on 12 datasets using relative depth and fine-tuned on two domains (NYU and KITTI) using metric depth. A lightweight head is used with a novel bin adjustment design called metric bins module for each domain. During inference, each input image is automatically routed to the appropriate head using a latent classifier.

Hiera

Hiera was proposed in Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles by Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer

The paper introduces “Hiera,” a hierarchical Vision Transformer that simplifies the architecture of modern hierarchical vision transformers by removing unnecessary components without compromising on accuracy or efficiency. Unlike traditional transformers that add complex vision-specific components to improve supervised classification performance, Hiera demonstrates that such additions, often termed “bells-and-whistles,” are not essential for high accuracy. By leveraging a strong visual pretext task (MAE) for pretraining, Hiera retains simplicity and achieves superior accuracy and speed both in inference and training across various image and video recognition tasks. The approach suggests that spatial biases required for vision tasks can be effectively learned through proper pretraining, eliminating the need for added architectural complexity.

Agents

Our ReactAgent has a specific way to return its final output: it calls the tool final_answer, added to the user-defined toolbox upon agent initialization, with the answer as the tool argument. We found that even for a one-shot agent like CodeAgent, using a specific final_answer tools helps the llm_engine find what to return: so we generalized the final_answer tool for all agents.

  • Adds final answer tool for all agents by @​aymeric-roucher in #​31703

  • Code agent: allow function persistence between steps by @​aymeric-roucher in #​31769
    👉 Now if your code-based agent (like ReactCodeAgent) defines a function at step 1, it will remember the function definition indefinitely. This means your agent can create its own tools for later re-use!

  • Agents planning by @​aymeric-roucher in #​31702
    👉 This is a transformative PR: it allows the agent to regularly run a specific step for planning its actions in advance. This gets activated if you set an int for planning_interval upon agent initialization. At step 0, a first plan will be done. At later steps (like steps 3, 6, 9 if you set planning_interval=3 ), this plan will be updated by the agent depending on the history of previous steps. More detail soon!
    Selon si on a merge d'ici là on pourra rajouter:

  • Add stream messages from agent run for gradio chatbot by @​freddyaboulton and @​aymeric-roucher in #​32142
    👉 New method stream_to_gradio runs your agent and streams the output the run to gradio messages, to easily visualize the run in a gradio chatbot! (

  • Adds final answer tool for all agents by @​aymeric-roucher in #​31703

  • Code agent: allow function persistence between steps by @​aymeric-roucher in #​31769

  • Agents planning by @​aymeric-roucher in #​31702

Notable changes to the codebase

A significant RoPE refactor was done to make it model agnostic and more easily adaptable to any architecture.
It is only applied to Llama for now but will be applied to all models using RoPE over the coming days.

Breaking changes

TextGenerationPipeline and tokenizer kwargs

🚨🚨 This PR changes the code to rely on the tokenizer's defaults when these flags are unset. This means some models using TextGenerationPipeline previously did not add a <bos> by default, which (negatively) impacted their performance. In practice, this is a breaking change.

Example of a script changed as a result of this PR:

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it", torch_dtype=torch.bfloat16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(pipe("Foo bar"))
  • 🚨🚨 TextGenerationPipeline: rely on the tokenizer default kwargs by @​gante in #​31747

Bugfixes and improvements

Significant community contributions

The following contributors have made significant changes to the library over the last release:

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Enabled.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate bot force-pushed the renovate/transformers-4.x branch from af3c5a5 to f20c1e9 Compare July 23, 2024 20:36
@renovate renovate bot changed the title chore(deps): update dependency transformers to v4.43.0 chore(deps): update dependency transformers to v4.43.1 Jul 23, 2024
@renovate renovate bot merged commit b24c78d into dev Jul 23, 2024
2 checks passed
@renovate renovate bot deleted the renovate/transformers-4.x branch July 23, 2024 21:11
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