---
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/induction_heavy_100k_jsonl
- barc0/induction_heavy_suggestfunction_100k_jsonl
model-index:
- name: l3.1-8b-inst-fft-induction-barc-heavy-200k-lr1e-5-ep2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# l3.1-8b-inst-fft-induction-barc-heavy-200k-lr1e-5-ep2

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_heavy_100k_jsonl and the barc0/induction_heavy_suggestfunction_100k_jsonl datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3992

## 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: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4824        | 1.0   | 1478 | 0.4727          |
| 0.3638        | 2.0   | 2956 | 0.4042          |
| 0.2835        | 3.0   | 4434 | 0.3992          |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3