Instructions to use Aprajita0/Gemma-2b-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Aprajita0/Gemma-2b-Lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "Aprajita0/Gemma-2b-Lora") - Notebooks
- Google Colab
- Kaggle
Gemma-2b-Lora
This model is a fine-tuned version of google/gemma-2b on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 2.1612
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training Hardware
This model was trained using Intel(R) Data Center GPU Max 1100
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: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 593
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.8641 | 0.82 | 100 | 2.5224 |
| 2.441 | 1.64 | 200 | 2.3159 |
| 2.2881 | 2.46 | 300 | 2.2316 |
| 2.2544 | 3.28 | 400 | 2.1858 |
| 2.1966 | 4.1 | 500 | 2.1612 |
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.0.1a0+cxx11.abi
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Aprajita0/Gemma-2b-Lora
Base model
google/gemma-2b