Instructions to use Skyasra/gemma-7b-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Skyasra/gemma-7b-1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") model = PeftModel.from_pretrained(base_model, "Skyasra/gemma-7b-1") - Notebooks
- Google Colab
- Kaggle
gemma-7b-1
This model is a fine-tuned version of google/gemma-7b on an unknown dataset.
Model description
More information needed
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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10
Training results
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.38.2
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Base model
google/gemma-7b