---
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-gpt4mini20k_lr2e-4_epoch3
  results: []
---

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# barc-llama3.1-8b-instruct-lora64-induction-gpt4mini20k_lr2e-4_epoch3

This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/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: 0.3327

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3519        | 1.0   | 152  | 0.3529          |
| 0.3233        | 2.0   | 304  | 0.3357          |
| 0.2989        | 3.0   | 456  | 0.3327          |


### Framework versions

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