Text Generation
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llama
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trl
sft
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metadata
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
  - trl
  - sft
  - generated_from_trainer
datasets:
  - >-
    barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems
  - barc0/transduction_angmented_100k_gpt4o-mini_generated_problems
  - barc0/transduction_rearc_dataset_400k
model-index:
  - name: llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4
    results: []

llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems, the barc0/transduction_angmented_100k_gpt4o-mini_generated_problems and the barc0/transduction_rearc_dataset_400k datasets. It achieves the following results on the evaluation set:

  • Loss: 0.0409

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: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 256
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss
0.0618 1.0 1126 0.0657
0.0504 2.0 2252 0.0494
0.0363 3.0 3378 0.0418
0.0238 4.0 4504 0.0409

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.4.0+cu121
  • Datasets 3.0.1
  • Tokenizers 0.19.1