--- library_name: transformers license: apache-2.0 datasets: - Locutusque/hercules-v2.0 - CollectiveCognition/chats-data-2023-09-22 language: - en --- # lr-experiment1-7B The lr-experiment model series is a research project I'm conducting that I will be using to determine the best learning rate to use while fine-tuning Mistral. This model uses a learning rate of 2e-5 with a cosine scheduler and no warmup steps. I used Locutusque/Hercules-2.0-Mistral-7B as a base model, and further fine-tuned it on CollectiveCognition/chats-data-2023-09-22 using QLoRA for 3 epochs. I will be keeping track of evaluation results, and will comparing it to upcoming models. # Evals | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |---------------------------------|-------|------|------|--------|-----:|---|-----:| |agieval_nous |N/A |none |None |acc |0.3645|± |0.0093| | | |none |None |acc_norm|0.3468|± |0.0092| | - agieval_aqua_rat | 1|none |None |acc |0.2283|± |0.0264| | | |none |None |acc_norm|0.2283|± |0.0264| | - agieval_logiqa_en | 1|none |None |acc |0.2965|± |0.0179| | | |none |None |acc_norm|0.3303|± |0.0184| | - agieval_lsat_ar | 1|none |None |acc |0.2217|± |0.0275| | | |none |None |acc_norm|0.1783|± |0.0253| | - agieval_lsat_lr | 1|none |None |acc |0.4039|± |0.0217| | | |none |None |acc_norm|0.3686|± |0.0214| | - agieval_lsat_rc | 1|none |None |acc |0.4870|± |0.0305| | | |none |None |acc_norm|0.4424|± |0.0303| | - agieval_sat_en | 1|none |None |acc |0.6408|± |0.0335| | | |none |None |acc_norm|0.5971|± |0.0343| | - agieval_sat_en_without_passage| 1|none |None |acc |0.3932|± |0.0341| | | |none |None |acc_norm|0.3835|± |0.0340| | - agieval_sat_math | 1|none |None |acc |0.3455|± |0.0321| | | |none |None |acc_norm|0.2727|± |0.0301| | Groups |Version|Filter|n-shot| Metric |Value | |Stderr| |------------|-------|------|------|--------|-----:|---|-----:| |agieval_nous|N/A |none |None |acc |0.3645|± |0.0093| | | |none |None |acc_norm|0.3468|± |0.0092|