---
base_model: facebook/opt-350m
datasets:
- HuggingFaceH4/ultrachat_200k
library_name: peft
license: other
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: opt350m-qlora
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# opt350m-qlora

This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7868

## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8281        | 0.9999 | 8068 | 1.7868          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.0
- Pytorch 2.1.2
- Datasets 3.1.0
- Tokenizers 0.20.3