l3.1-8b-inst-lora64-induction-gpt4wmini100k-mini100k-gpt4wmini20k-gpt4wllama20k-lr2e-4-ep3

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/induction_100k-gpt4-description-gpt4omini-code_generated_problems_messages_format_0.3, the barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3, the barc0/induction_gpt-4_description_20000_with_llama_codegen_messages_format_0.3 and the barc0/induction_gpt-4_description_20000_with_gpt-4o-mini_codegen_messages_format_0.3 datasets. It achieves the following results on the evaluation set:

• Loss: 0.2684

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• total_eval_batch_size: 32
• 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
0.2882	1.0	1784	0.2852
0.257	2.0	3568	0.2705
0.2329	3.0	5352	0.2684

Framework versions

• PEFT 0.13.0
• Transformers 4.45.0.dev0
• Pytorch 2.4.1+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1