Bump peft from 0.14.0 to 0.17.1

[dependabot]

Bumps peft from 0.14.0 to 0.17.1.

Release notes

Sourced from peft's releases.

0.17.1

This patch release contains a few fixes (via #2710) for the newly introduced target_parameters feature, which allows LoRA to target nn.Parameters directly (useful for mixture of expert layers). Most notably:

• PEFT no longer removes possibly existing parametrizations from the parameter.
• Adding multiple adapters (via model.add_adapter or model.load_adapter) did not work correctly. Since a solution is not trivial, PEFT now raises an error to prevent this situation.

0.17.0: SHiRA, MiSS, LoRA for MoE, and more

Highlights

New Methods

SHiRA

@​kkb-code contributed Sparse High Rank Adapters (SHiRA, paper) which promise to offer a potential gain in performance over LoRAs - especially the concept loss when using multiple adapters is improved. Since the adapters only train on 1-2% of the weights and are inherently sparse, switching between adapters may be cheaper than with LoRAs. (#2584)

MiSS

@​JL-er added a new PEFT method, MiSS (Matrix Shard Sharing) in #2604. This method is an evolution of Bone, which, according to our PEFT method comparison benchmark, gives excellent results when it comes to performance and memory efficiency. If you haven't tried it, you should do so now.

At the same time, Bone will be deprecated in favor of MiSS and will be removed in PEFT v0.19.0. If you already have a Bone checkpoint, you can use scripts/convert-bone-to-miss.py to convert it into a MiSS checkpoint and proceed with training using MiSS.

Enhancements

LoRA for nn.Parameter

LoRA is now able to target nn.Parameter directly (#2638, #2665)! Ever had this complicated nn.Module with promising parameters inside but it was too custom to be supported by your favorite fine-tuning library? No worries, now you can target nn.Parameters directly using the target_parameters config attribute which works similarly to target_modules.

This option can be especially useful for models with Mixture of Expert (MoE) layers, as those often use nn.Parameters directly and cannot be targeted with target_modules. For example, for the Llama4 family of models, use the following config to target the MoE weights:

config = LoraConfig(
    ...,
    target_modules=[],  # <= prevent targeting any modules
    target_parameters=["feed_forward.experts.down_proj", "feed_forward.experts.gate_up_proj"],
)

Note that this feature is still experimental as it comes with a few caveats and therefore might change in the future. Also, MoE weights with many experts can be quite huge, so expect a higher memory usage than compared to targeting normal nn.Linear layers.

Injecting adapters based on a state_dict

Sometimes, it is possible that there is a PEFT adapter checkpoint but the corresponding PEFT config is not known for whatever reason. To inject the PEFT layers for this checkpoint, you would usually have to reverse-engineer the corresponding PEFT config, most notably the target_modules argument, based on the state_dict from the checkpoint. This can be cumbersome and error prone. To avoid this, it is also possible to call inject_adapter_in_model and pass the loaded state_dict as an argument:

from safetensors.torch import load_file
from peft import LoraConfig, inject_adapter_in_model
model = ...
</tr></table>

... (truncated)

Commits

• 53c25fe Release: 0.17.1 changes (#2739)
• 48f6493 Release 0.17.0 (#2691)
• 337be05 ENH: Adapter injection based on state_dict (#2637)
• bb4fb50 FEAT Add MiSS as a replacement for Bone. (#2604)
• a91ec33 Fix not detecting regex-targeted embedding layer (#2649)
• 25e5c6b FIX Missing device map for facebook/opt-125m (#2675)
• 5e00266 TST: Add more HF Hub model caching (#2682)
• 46ae69a FIX Small fixes to target_parameters (#2677)
• 1c853ea Fix trainable tokens with fsdp (#2681)
• c11a9df FIX Failing target_parameters param usage count (#2676)
• Additional commits viewable in compare view

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