--- license: llama3 datasets: - JeanKaddour/minipile language: - en tags: - axolotl - mergekit - llama --- > 🚨 THIS IS A BASE MODEL 🚨 > > This model is pruned from the base Llama 3 70B, which has no instruction tuning and randomly initialized special tokens. > > Using this with the Llama 3 instruction format is injecting random noise into latent space and will give you deranged results. (It's pretty funny actually.) > Treat this as the untrained foundation model this is and use appropriate prompts. Meta's Llama 3 70B pruned to 42B parameters using the methodology described in [The Unreasonable Ineffectiveness of the Deeper Layers](https://arxiv.org/abs/2403.17887). Post-pruning trained using QLoRA for ~100M tokens from [JeanKaddour/minipile](https://huggingface.co/datasets/JeanKaddour/minipile). Layers to prune selected using [PruneMe](https://github.com/arcee-ai/PruneMe). Still evaluating, don't get too excited! Might be incredibly dumb. Check out these numbers though: | Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |------------------|-------|------|-----:|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.7669|± |0.0034| | - humanities |N/A |none | 5|acc |0.7296|± |0.0062| | - other |N/A |none | 5|acc |0.8101|± |0.0067| | - social_sciences|N/A |none | 5|acc |0.8668|± |0.0060| | - stem |N/A |none | 5|acc |0.6825|± |0.0079| |winogrande| 1|none | 5|acc |0.8027|± |0.0112| |hellaswag| 1|none | 10|acc_norm|0.8025|± |0.0040| [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)