๐Œ๐ฒ ๐Ÿ๐ข๐ซ๐ฌ๐ญ ๐œ๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐š๐ซ๐ญ๐ข๐œ๐ฅ๐ž! ๐’๐ž๐ฅ... | ๐Œ๐ฒ ๐Ÿ๐ข๐ซ๐ฌ๐ญ ๐œ๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐š๐ซ๐ญ๐ข๐œ๐ฅ๐ž! ๐’๐ž๐ฅ...
๐Œ๐ฒ ๐Ÿ๐ข๐ซ๐ฌ๐ญ ๐œ๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐š๐ซ๐ญ๐ข๐œ๐ฅ๐ž! ๐’๐ž๐ฅ๐ž๐œ๐ญ๐ข๐ฏ๐ž ๐Ÿ๐ข๐ง๐ž-๐ญ๐ฎ๐ง๐ข๐ง๐  ๐ฐ๐ข๐ญ๐ก ๐’๐ฉ๐ž๐œ๐ญ๐ซ๐ฎ๐ฆ ๐ŸŽฏ

Full walkthrough on how to get started with Spectrum and TRL for efficient fine-tuning.
๐Ÿ“” ๐Ÿ‘ฃ https://huggingface.co/blog/anakin87/spectrum

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Looking to fine-tune Language Models efficiently and save on computational resources?

One popular method is QLoRa, which quantizes the original model and trains low-rank adapters on top.
It's quite effective and uses less GPU than full fine-tuning.

However, QLoRa applies Low-Rank Adaptation uniformly across the entire model.

What if we could identify the most informative layers and only fine-tune those? ๐Ÿค”

This is exactly what Spectrum does! ๐Ÿ‘‡

๐Ÿ”ฌ Spectrum analyzes the weight matrices for all layers in a Language Model and calculates a Signal to Noise Ratio (SNR) for each one.
(It uses Random Matrix Theory and Marchenko-Pastur distribution to distinguish signal from noise.)

๐ŸŽฏ Based on a chosen percentage (say, 25%), Spectrum selects the most informative layers of each type (mlp.down_proj, self_attn.o_proj, etc.).

You can then โ„๏ธ freeze the rest of the model and focus your ๐Ÿ‹๏ธโ€โ™‚๏ธ training on the chosen layers.


๐Ÿ† Results/Evaluation
- Spectrum is competitive with full fine-tuning and beats QLoRA on benchmarks.
- While QLoRA is more memory-efficient on a single GPU, Spectrum shines in distributed training setups.
- Great models trained with Spectrum: Dolphin models, Llama 3.1 Storm, numerous models by VAGO Solutions...

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For a practical guide, check out the article above. Selective fine-tuning of Language Models with Spectrum