---
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/cot_transduction_100k-gpt4omini-description
- barc0/cot_transduction_100k-gpt4-description
- barc0/cot_transduction_200k_HEAVY_gpt4o-description
model-index:
- name: cot-400k-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# cot-400k-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](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the barc0/cot_transduction_100k-gpt4omini-description, the barc0/cot_transduction_100k-gpt4-description and the barc0/cot_transduction_200k_HEAVY_gpt4o-description datasets.
It achieves the following results on the evaluation set:
- Loss: 0.2160

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.2231        | 0.9998 | 2994 | 0.2183          |
| 0.1861        | 2.0    | 5989 | 0.2049          |
| 0.1483        | 2.9995 | 8982 | 0.2160          |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1