---
license: other
base_model: deepseek-ai/deepseek-coder-1.3b-base
tags:
- trl
- kto
- generated_from_trainer
model-index:
- name: k3
  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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/stojchets/huggingface/runs/k3)
# k3

This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1575
- Eval/rewards/chosen: 2.5930
- Eval/logps/chosen: -116.6651
- Eval/rewards/rejected: -10.1233
- Eval/logps/rejected: -282.2972
- Eval/rewards/margins: 12.7163
- Eval/kl: 7.4003

## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 200
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |         |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2045        | 0.8552 | 100  | 0.2515          | 16.7171 |
| 0.0457        | 1.7103 | 200  | 0.2056          | 14.2697 |
| 0.0271        | 2.5655 | 300  | 0.1644          | 9.4286  |
| 0.0157        | 3.4206 | 400  | 0.1575          | 7.4003  |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1