---
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
datasets:
- generator
model-index:
- name: gemma-dolly-agriculture
  results: []
---

# gemma-dolly-agriculture

This model is based on [google/gemma-2b](https://huggingface.co/google/gemma-2b), fine tuned with the dolly-qa dataset and some specific examples of agricultural disease descriptions.
It achieves the following results on the evaluation set:
- Loss: 2.0198

## How to Run Inference

Make sure you have git-lfs, and access to gemma-2b on huggingface.
```
git clone https://huggingface.co/apfurman/gemma-dolly-agriculture
cd gemma-dolly-agriculture/
python3 run.py cpu <YOUR-TOKEN-HERE> Prompt
```
replace "cpu" with "gpu" if you want to run on gpu.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 0.05
- training_steps: 1480

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.918         | 1.6393  | 100  | 2.5702          |
| 2.4342        | 3.2787  | 200  | 2.2747          |
| 2.2482        | 4.9180  | 300  | 2.1601          |
| 2.1554        | 6.5574  | 400  | 2.0971          |
| 2.1022        | 8.1967  | 500  | 2.0698          |
| 2.0806        | 9.8361  | 600  | 2.0544          |
| 2.0651        | 11.4754 | 700  | 2.0437          |
| 2.0439        | 13.1148 | 800  | 2.0359          |
| 2.0369        | 14.7541 | 900  | 2.0302          |
| 2.034         | 16.3934 | 1000 | 2.0263          |
| 2.0249        | 18.0328 | 1100 | 2.0236          |
| 2.0174        | 19.6721 | 1200 | 2.0218          |
| 2.0154        | 21.3115 | 1300 | 2.0203          |
| 2.0145        | 22.9508 | 1400 | 2.0198          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.0
- Tokenizers 0.19.1