--- license: apache-2.0 model-index: - name: Sheared-LLaMA-2.7B-ShareGPT results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 41.04 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 71.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 28.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 47.71 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 64.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT name: Open LLM Leaderboard --- **Paper**: [https://arxiv.org/pdf/2310.06694.pdf](https://arxiv.org/pdf/2310.06694.pdf) **Code**: https://github.com/princeton-nlp/LLM-Shearing **Models**: [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B), [Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B) ## Training information This is the instruction tuned version of [princeton-nlp/Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B). We trained the base model on 10,000 instruction-response pairs sampled from the ShareGPT dataset (first-turns only). We use the following prompt to perform instruction tuning. > You are a helpful assistant. Write a response that appropriately completes the request.\n\n### Input:\n{input}\n\n### Response: This model can be loaded through transformers.LlamaModelForCausalLM as follows: ``` from transformers import LlamaModelForCausalLM model = LlamaModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT") ``` ## Bibtex If you find our model useful, consider citing us with: ``` @article{xia2023sheared, title={Sheared llama: Accelerating language model pre-training via structured pruning}, author={Xia, Mengzhou and Gao, Tianyu and Zeng, Zhiyuan and Chen, Danqi}, journal={arXiv preprint arXiv:2310.06694}, year={2023} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-LLaMA-2.7B-ShareGPT) | Metric |Value| |---------------------------------|----:| |Avg. |42.11| |AI2 Reasoning Challenge (25-Shot)|41.04| |HellaSwag (10-Shot) |71.26| |MMLU (5-Shot) |28.50| |TruthfulQA (0-shot) |47.71| |Winogrande (5-shot) |64.17| |GSM8k (5-shot) | 0.00|