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End of training
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metadata
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
  - >-
    barc0/induction_gpt-4_description_20000_with_llama_codegen_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-gpt4-desc-llama-20k_lr2e-4_epoch3
    results: []

barc-llama3.1-8b-instruct-lora64-induction-gpt4-desc-llama-20k_lr2e-4_epoch3

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/induction_gpt-4_description_20000_with_llama_codegen_messages_format_0.3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1384

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
0.1393 0.9959 121 0.1582
0.1282 2.0 243 0.1401
0.1135 2.9877 363 0.1384

Framework versions

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