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_heavy_100k_jsonl
- barc0/transduction_heavy_suggestfunction_100k_jsonl
- barc0/transduction_rearc_dataset_400k
- >-
barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems
- barc0/transduction_angmented_100k_gpt4o-mini_generated_problems
model-index:
- name: engineer1-heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3
results: []
engineer1-heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/transduction_heavy_100k_jsonl, the barc0/transduction_heavy_suggestfunction_100k_jsonl, the barc0/transduction_rearc_dataset_400k, the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems and the barc0/transduction_angmented_100k_gpt4o-mini_generated_problems datasets. It achieves the following results on the evaluation set:
- Loss: 0.0219
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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0378 | 1.0 | 3729 | 0.0330 |
| 0.0234 | 2.0 | 7458 | 0.0227 |
| 0.0116 | 3.0 | 11187 | 0.0219 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1