pypi peft 0.8.0
v0.8.0: Poly PEFT method, LoRA improvements, Documentation improvements and more
on Python PyPI

latest releases: 0.13.2, 0.13.1, 0.13.0...
10 months ago

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] (π™Ώπš˜πš•πš’) 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.
π™Όπ™·πš (Multi-Head Routing) combines subsets of adapter parameters and outperforms π™Ώπš˜πš•πš’ under a comparable parameter budget; by only fine-tuning the routing function and not the adapters (π™Όπ™·πš-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

  • Add an option 'ALL' to include all linear layers as target modules by @SumanthRH in #1295

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 <|user|>, <|assistant|>, <|system|>, <|im_end|>, <|im_start|>, </s>, <s> 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 <|FUNCTIONS|>, <|BROWSE|>, <|TEXT2IMAGE|>, <|ASR|>, <|TTS|>, <|GENERATECODE|>, <|RAG|>.
    A good blogpost to learn more about this https://www.philschmid.de/fine-tune-llms-in-2024-with-trl.
  • save the embeddings even when they aren't targetted but resized by @pacman100 in #1383

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

  • Refactoring and updating of the concept guides. [docs] Concept guides by @stevhliu in #1269
  • Improving task guides to focus more on how to use different PEFT methods and related nuances instead of focusing more on different type of tasks. It condenses the individual guides into a single one to highlight the commonalities and differences, and to refer to existing docs to avoid duplication. [docs] Task guides by @stevhliu in #1332
  • DOC: Update docstring for the config classes by @BenjaminBossan in #1343
  • LoftQ: edit README.md and example files by @yxli2123 in #1276
  • [Docs] make add_weighted_adapter example clear in the docs. by @sayakpaul in #1353
  • DOC Add PeftMixedModel to API docs by @BenjaminBossan in #1354
  • [docs] Docstring link by @stevhliu in #1356
  • QOL improvements and doc updates by @pacman100 in #1318
  • Doc about AdaLoraModel.update_and_allocate by @kuronekosaiko in #1341
  • DOC: Improve target modules description by @BenjaminBossan in #1290
  • DOC Troubleshooting for unscaling error with fp16 by @BenjaminBossan in #1336
  • DOC Extending the vocab and storing embeddings by @BenjaminBossan in #1335
  • Improve documentation for the all-linear flag by @SumanthRH in #1357
  • Fix various typos in LoftQ docs. by @arnavgarg1 in #1408

What's Changed

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Full Changelog: v0.7.1...v0.8.0

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