--- 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/induction_heavy_100k_jsonl - barc0/induction_heavy_suggestfunction_100k_jsonl - barc0/induction_100k-gpt4-description-gpt4omini-code_generated_problems_messages_format_0.3 - barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3 model-index: - name: l3.1-8b-inst-fft-induction-barc-heavy-200k-old-200k-lr1e-5-ep2 results: [] --- # l3.1-8b-inst-fft-induction-barc-heavy-200k-old-200k-lr1e-5-ep2 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/induction_heavy_100k_jsonl, the barc0/induction_heavy_suggestfunction_100k_jsonl, the barc0/induction_100k-gpt4-description-gpt4omini-code_generated_problems_messages_format_0.3 and the barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3 datasets. It achieves the following results on the evaluation set: - Loss: 0.2709 ## 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: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2817 | 1.0 | 2995 | 0.2818 | | 0.2432 | 2.0 | 5990 | 0.2709 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.1+cu124 - Datasets 3.0.2 - Tokenizers 0.19.1