---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: flippa-v2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# flippa-v2

This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on a mixed dataset of filtered non-refusal data, math, and code.
It achieves the following results on the evaluation set:
- Loss: 0.9289

## Model description

My second test of experiments using Quantitized LoRA and Mistral-7B-Instruct, trained on A100 in one hour, will increase training times and amount of data as I gain access to more GPUs.

## 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_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5374        | 0.99  | 37   | 1.4226          |
| 1.1746        | 2.0   | 75   | 1.2444          |
| 1.0746        | 2.99  | 112  | 1.1636          |
| 0.9931        | 4.0   | 150  | 1.1037          |
| 0.9587        | 4.99  | 187  | 1.0549          |
| 0.9101        | 6.0   | 225  | 1.0124          |
| 0.8847        | 6.99  | 262  | 0.9782          |
| 0.8239        | 8.0   | 300  | 0.9515          |
| 0.818         | 8.99  | 337  | 0.9345          |
| 0.7882        | 9.87  | 370  | 0.9289          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2