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Bump transformers from 2.5.1 to 4.30.0
Bumps transformers from 2.5.1 to 4.30.0.
Release notes
Sourced from transformers's releases.
v4.30.0: 100k, Agents improvements, Safetensors core dependency, Swiftformer, Autoformer, MobileViTv2, timm-as-a-backbone
100k
Transformers has just reached 100k stars on GitHub, and to celebrate we wanted to highlight 100 projects in the vicinity of
transformers
and we have decided to create an awesome-transformers page to do just that.We accept PRs to add projects to the list!
- Top 100 by
@LysandreJik
in #22912- Add LlamaIndex to awesome-transformers.md by
@ravi03071991
in #23484- add cleanlab to awesome-transformers tools list by
@jwmueller
in #234404-bit quantization and QLoRA
By leveraging the
bitsandbytes
library by@TimDettmers
, we add 4-bit support totransformers
models!
- 4-bit QLoRA via bitsandbytes (4-bit base model + LoRA) by
@TimDettmers
in #23479Agents
The Agents framework has been improved and continues to be stabilized. Among bug fixes, here are the important new features that were added:
- Local agent capabilities, to load a generative model directly from
transformers
instead of relying on APIs.- Prompts are now hosted on the Hub, which means that anyone can fork the prompts and update them with theirs, to let other community contributors re-use them
- We add an
AzureOpenAiAgent
class to support Azure OpenAI agents.
- Add local agent by
@sgugger
in #23438- Enable prompts on the Hub by
@sgugger
in #23662- Add AzureOpenAiAgent by
@sgugger
in #24058Safetensors
The
safetensors
library is a safe serialization framework for machine learning tensors. It has been audited and will become the default serialization framework for several organizations (Hugging Face, EleutherAI, Stability AI).It has now become a core dependency of
transformers
.New models
Swiftformer
The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called ‘SwiftFormer’ is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.
- Add swiftformer by
@shehanmunasinghe
in #22686Autoformer
This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.
... (truncated)
Commits
fe861e5
[GPT2
] Add correct keys on_keys_to_ignore_on_load_unexpected
on all chil...b3e27a8
Update the pin on Accelerate (#24110)53e1f5c
[Trainer
] Correct behavior of_load_best_model
for PEFT models (#24103)17db177
reset accelerate env variables after each test (#24107)905892f
Release: v4.30.0c3572e6
Add AzureOpenAiAgent (#24058)5eb3d3c
Up pinned accelerate version (#24089)d1c039e
fix accelerator prepare during eval only mode (#24014)2c887cf
Do not prepare lr scheduler as it as the right number of steps (#24088)12298cb
fix executable batch size issue (#24067)- Additional commits viewable in compare view
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