llama3-42b-v0

Maintainer: chargoddard

Total Score

111

Last updated 5/28/2024

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The llama3-42b-v0 model is a pruned version of Meta's Llama 3 70B foundation model. It was created by chargoddard using the methodology described in The Unreasonable Ineffectiveness of the Deeper Layers to prune the base Llama 3 model down to 42B parameters. The model was then further trained on around 100M tokens from the JeanKaddour/minipile dataset using QLoRA. This pruned model is intended to be used as an untrained foundation, with appropriate prompts, as injecting random noise into the latent space will produce "deranged results".

Model inputs and outputs

Inputs

  • The llama3-42b-v0 model accepts text input only.

Outputs

  • The model generates text and code output.

Capabilities

The llama3-42b-v0 model has been evaluated on a variety of benchmarks, including MMLU, Winogrande, and HellaSwag, where it achieves respectable performance. However, the maintainer notes that the model is still being evaluated and may exhibit "incredibly dumb" behavior, so it should be treated as an untrained foundation model.

What can I use it for?

Given the model's status as an untrained foundation, it is likely most useful for researchers and developers looking to experiment with pruning techniques or continue pre-training on additional data. The maintainer cautions against using the model with Llama 3's instruction format, as this will lead to "deranged results". Instead, users should focus on developing appropriate prompts to leverage the model's capabilities.

Things to try

Developers interested in exploring the llama3-42b-v0 model could try fine-tuning it on specific downstream tasks or datasets to evaluate its performance. Additionally, experimenting with different pruning techniques and training regimes could yield interesting insights about the model's behavior and potential.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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