README.md

metadata

base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
  - >-
    barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3
library_name: peft
license: llama3.1
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
model-index:
  - name: barc-llama3.1-8b-instruct-lora64-induction-gpt4mini100k_lr2e-4_epoch3
    results: []

barc-llama3.1-8b-instruct-lora64-induction-gpt4mini100k_lr2e-4_epoch3

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3031

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
0.323	1.0	758	0.3202
0.2892	2.0	1516	0.3058
0.274	3.0	2274	0.3031

Framework versions

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