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--- |
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license: gemma |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: google/gemma-2b |
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datasets: |
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- generator |
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model-index: |
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- name: gemma-2b-dolly-qa |
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results: [] |
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--- |
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# gemma-2b-dolly-qa |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0198 |
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## How to Run Inference |
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Make sure you have git-lfs, and access to gemma-2b on huggingface. |
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``` |
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pip install peft |
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git clone https://huggingface.co/apfurman/gemma-2b-dolly-qa |
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cd gemma-2b-dolly-qa/ |
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python3 run.py cpu <YOUR-TOKEN-HERE> Prompt |
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``` |
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replace "cpu" with "gpu" if you want to run on gpu. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- training_steps: 1480 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 2.918 | 1.6393 | 100 | 2.5702 | |
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| 2.4342 | 3.2787 | 200 | 2.2747 | |
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| 2.2482 | 4.9180 | 300 | 2.1601 | |
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| 2.1554 | 6.5574 | 400 | 2.0971 | |
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| 2.1022 | 8.1967 | 500 | 2.0698 | |
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| 2.0806 | 9.8361 | 600 | 2.0544 | |
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| 2.0651 | 11.4754 | 700 | 2.0437 | |
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| 2.0439 | 13.1148 | 800 | 2.0359 | |
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| 2.0369 | 14.7541 | 900 | 2.0302 | |
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| 2.034 | 16.3934 | 1000 | 2.0263 | |
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| 2.0249 | 18.0328 | 1100 | 2.0236 | |
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| 2.0174 | 19.6721 | 1200 | 2.0218 | |
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| 2.0154 | 21.3115 | 1300 | 2.0203 | |
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| 2.0145 | 22.9508 | 1400 | 2.0198 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.1 |
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- Pytorch 2.1.0.post0+cxx11.abi |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
