---
license: apache-2.0
library_name: peft
tags:
- trl
- kto
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model-index:
- name: WeniGPT-Agents-Mixstral-Instruct-2.0.0-KTO
  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. -->

# WeniGPT-Agents-Mixstral-Instruct-2.0.0-KTO

This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rewards/chosen: -5.4348
- Logps/chosen: -287.4195
- Rewards/rejected: -12.6006
- Logps/rejected: -352.2351
- Kl: nan
- Rewards/margins: 7.4756

## 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: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 145
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Logps/chosen | Rewards/rejected | Logps/rejected | Kl  | Rewards/margins |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:----------------:|:--------------:|:---:|:---------------:|
| 0.4625        | 0.34  | 50   | nan             | 1.1551         | -217.6597    | -0.4668          | -235.0032      | nan | 1.6945          |
| 0.3511        | 0.68  | 100  | nan             | -5.4348        | -287.4195    | -12.6006         | -352.2351      | nan | 7.4756          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.39.1
- Pytorch 2.1.0+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2