--- library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - barc0/transduction_heavy_100k_jsonl - barc0/transduction_heavy_suggestfunction_100k_jsonl - barc0/transduction_rearc_dataset_400k - barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems - barc0/transduction_angmented_100k_gpt4o-mini_generated_problems model-index: - name: engineer1-heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3 results: [] --- # engineer1-heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the barc0/transduction_heavy_100k_jsonl, the barc0/transduction_heavy_suggestfunction_100k_jsonl, the barc0/transduction_rearc_dataset_400k, the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems and the barc0/transduction_angmented_100k_gpt4o-mini_generated_problems datasets. It achieves the following results on the evaluation set: - Loss: 0.0219 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0378 | 1.0 | 3729 | 0.0330 | | 0.0234 | 2.0 | 7458 | 0.0227 | | 0.0116 | 3.0 | 11187 | 0.0219 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.0.2 - Tokenizers 0.19.1