metadata
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
datasets:
- generator
model-index:
- name: gemma-2b-dolly-qa
results: []
gemma-2b-dolly-qa
This model is based on 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.
pip install peft
git clone https://huggingface.co/apfurman/gemma-2b-dolly-qa
cd gemma-2b-dolly-qa/
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