---
license: other
base_model: larryvrh/Yi-34B-200K-Llamafied
tags:
- generated_from_trainer
model-index:
- name: capybara-v4-yi-34b-200k
  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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# capybara-v4-yi-34b-200k

This model is a fine-tuned version of [larryvrh/Yi-34B-200K-Llamafied](https://huggingface.co/larryvrh/Yi-34B-200K-Llamafied) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3638

## 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: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7201        | 0.31  | 200  | 0.7801          |
| 0.716         | 0.62  | 400  | 0.7240          |
| 0.6128        | 0.93  | 600  | 0.6696          |
| 0.4111        | 1.24  | 800  | 0.6016          |
| 0.415         | 1.55  | 1000 | 0.5395          |
| 0.3293        | 1.86  | 1200 | 0.4782          |
| 0.3271        | 2.17  | 1400 | 0.4272          |
| 0.2672        | 2.49  | 1600 | 0.3925          |
| 0.2129        | 2.8   | 1800 | 0.3638          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1