metadata
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: []
flippa-v2
This model is a fine-tuned version of 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