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𝐌𝐲 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐚𝐫𝐭𝐢𝐜𝐥𝐞! 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐒𝐩𝐞𝐜𝐭𝐫𝐮𝐦 🎯 
𝐌𝐲 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐚𝐫𝐭𝐢𝐜𝐥𝐞! 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐒𝐩𝐞𝐜𝐭𝐫𝐮𝐦 🎯 
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Full walkthrough on how to get started with Spectrum and TRL for efficient fine-tuning.
Full walkthrough on how to get started with Spectrum and TRL for efficient fine-tuning.
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📔 👣 
📔 👣 
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https://huggingface.co/blog/anakin87/spectrum
https://huggingface.co/blog/anakin87/spectrum
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Looking to fine-tune Language Models efficiently and save on computational resources?
Looking to fine-tune Language Models efficiently and save on computational resources?
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One popular method is QLoRa, which quantizes the original model and trains low-rank adapters on top.
One popular method is QLoRa, which quantizes the original model and trains low-rank adapters on top.
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It's quite effective and uses less GPU than full fine-tuning.
It's quite effective and uses less GPU than full fine-tuning.
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However, QLoRa applies Low-Rank Adaptation uniformly across the entire model.
However, QLoRa applies Low-Rank Adaptation uniformly across the entire model.
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What if we could identify the most informative layers and only fine-tune those? 🤔
What if we could identify the most informative layers and only fine-tune those? 🤔
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This is exactly what Spectrum does! 👇
This is exactly what Spectrum does! 👇
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🔬 Spectrum analyzes the weight matrices for all layers in a Language Model and calculates a Signal to Noise Ratio (SNR) for each one.
🔬 Spectrum analyzes the weight matrices for all layers in a Language Model and calculates a Signal to Noise Ratio (SNR) for each one.
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(It uses Random Matrix Theory and Marchenko-Pastur distribution to distinguish signal from noise.)
(It uses Random Matrix Theory and Marchenko-Pastur distribution to distinguish signal from noise.)
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🎯 Based on a chosen percentage (say, 25%), Spectrum selects the most informative layers of each type (mlp.down_proj, self_attn.o_proj, etc.).
🎯 Based on a chosen percentage (say, 25%), Spectrum selects the most informative layers of each type (mlp.down_proj, self_attn.o_proj, etc.).
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You can then ❄️ freeze the rest of the model and focus your 🏋️‍♂️ training on the chosen layers.
You can then ❄️ freeze the rest of the model and focus your 🏋️‍♂️ training on the chosen layers.
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🏆 Results/Evaluation
🏆 Results/Evaluation
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- Spectrum is competitive with full fine-tuning and beats QLoRA on benchmarks.
- Spectrum is competitive with full fine-tuning and beats QLoRA on benchmarks.
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- While QLoRA is more memory-efficient on a single GPU, Spectrum shines in distributed training setups.
- While QLoRA is more memory-efficient on a single GPU, Spectrum shines in distributed training setups.
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- Great models trained with Spectrum: Dolphin models, Llama 3.1 Storm, numerous models by VAGO Solutions...
- 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.
For a practical guide, check out the article above.
𝐌𝐲 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐚𝐫𝐭𝐢𝐜𝐥𝐞! 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐒𝐩𝐞𝐜𝐭𝐫𝐮𝐦 🎯 

Full walkthrough on how to get started with Spectrum and TRL for efficient fine-tuning.
📔 👣 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? 🤔

This is exactly what Spectrum does! 👇

🔬 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.)

🎯 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 ❄️ freeze the rest of the model and focus your 🏋️‍♂️ training on the chosen layers.


🏆 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.
👀
John6666 osanseviero
🚀
gsarti