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caikit / caikit-nlp

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Bump peft from 0.6.0 to 0.8.1 #312

Closed dependabot[bot] closed 7 months ago

dependabot[bot] commented 7 months ago

Bumps peft from 0.6.0 to 0.8.1.

Release notes

Sourced from peft's releases.

Patch Release v0.8.1

This is a small patch release of PEFT that should:

What's Changed

Full Changelog: https://github.com/huggingface/peft/compare/v0.8.0...v0.8.1

v0.8.0: Poly PEFT method, LoRA improvements, Documentation improvements and more

Highlights

Poly PEFT method

Parameter-efficient fine-tuning (PEFT) for cross-task generalization consists of pre-training adapters on a multi-task training set before few-shot adaptation to test tasks. Polytropon [Ponti et al., 2023] (𝙿𝚘𝚕𝚢) jointly learns an inventory of adapters and a routing function that selects a (variable-size) subset of adapters for each task during both pre-training and few-shot adaptation. To put simply, you can think of it as Mixture of Expert Adapters. 𝙼𝙷𝚁 (Multi-Head Routing) combines subsets of adapter parameters and outperforms 𝙿𝚘𝚕𝚢 under a comparable parameter budget; by only fine-tuning the routing function and not the adapters (𝙼𝙷𝚁-z) they achieve competitive performance with extreme parameter efficiency.

LoRA improvements

Now, you can specify all-linear to target_modules param of LoraConfig to target all the linear layers which has shown to perform better in QLoRA paper than only targeting query and valuer attention layers

Embedding layers of base models are now automatically saved when the embedding layers are resized when fine-tuning with PEFT approaches like LoRA. This enables extending the vocabulary of tokenizer to include special tokens. This is a common use-case when doing the following:

  1. Instruction finetuning with new tokens being added such as to properly format the conversations
  2. Finetuning on a specific language wherein language specific tokens are added, e.g., Korean tokens being added to vocabulary for finetuning LLM on Korean datasets.
  3. Instruction finetuning to return outputs in a certain format to enable agent behaviour of new tokens such as . A good blogpost to learn more about this https://www.philschmid.de/fine-tune-llms-in-2024-with-trl.

New option use_rslora in LoraConfig. Use it for ranks greater than 32 and see the increase in fine-tuning performance (same or better performance for ranks lower than 32 as well).

Documentation improvements

... (truncated)

Commits


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dependabot[bot] commented 7 months ago

Superseded by #313.