README.md

metadata

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_gpt-4_description_20000_with_gpt-4o-mini_codegen_messages_format_0.3
  - >-
    barc0/transduction_gpt-4_description_20000_with_llama_codegen_messages_format_0.3
  - >-
    barc0/induction_gpt-4_description_20000_with_gpt-4o-mini_codegen_messages_format_0.3
  - >-
    barc0/induction_gpt-4_description_20000_with_llama_codegen_messages_format_0.3
model-index:
  - name: barc-llama3.1-8b-instruct-fft-sft-both_35k_and_35k_lr1e-5_epoch2
    results: []

barc-llama3.1-8b-instruct-fft-sft-both_35k_and_35k_lr1e-5_epoch2

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

• Loss: 0.1647

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: 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: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.1881	0.9991	532	0.1752
0.1237	1.9981	1064	0.1647

Framework versions

• Transformers 4.45.0.dev0
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1