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Supercool Weekend Read🤖
Supercool Weekend Read🤖
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Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.
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Details on Techniques (Simplified):
Details on Techniques (Simplified):
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They started off with a large pre-trained language model (15B params), then:
They started off with a large pre-trained language model (15B params), then:
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1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.
1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.
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2. Pruned (remove) less important parts of the model to reduce its size.
2. Pruned (remove) less important parts of the model to reduce its size.
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3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.
3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.
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4. Used a lightweight neural architecture search to find the best configuration for the pruned model.
4. Used a lightweight neural architecture search to find the best configuration for the pruned model.
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5. Repeated this process iteratively to create even smaller models.
5. Repeated this process iteratively to create even smaller models.
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Cool, giving it a try this weekend 😎
Cool, giving it a try this weekend 😎
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Code: 
Code: 
link
https://github.com/NVlabs/Minitron
https://github.com/NVlabs/Minitron
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Paper: 
Paper: 
link
https://arxiv.org/abs/2407.14679
https://arxiv.org/abs/2407.14679
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Demo: 
Demo: 
resource
https://huggingface.co/spaces/nvidia/minitron
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nvidia/minitron
https://huggingface.co/spaces/nvidia/minitron
Supercool Weekend Read🤖
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.

Details on Techniques (Simplified):
They started off with a large pre-trained language model (15B params), then:

1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.

2. Pruned (remove) less important parts of the model to reduce its size.

3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.

4. Used a lightweight neural architecture search to find the best configuration for the pruned model.

5. Repeated this process iteratively to create even smaller models.

Cool, giving it a try this weekend 😎
Code: https://github.com/NVlabs/Minitron
Paper: https://arxiv.org/abs/2407.14679
Demo: https://huggingface.co/spaces/nvidia/minitron
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