Instructions to use surrey-nlp/IFT-GEMBA-multilingual-Gemma-2-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use surrey-nlp/IFT-GEMBA-multilingual-Gemma-2-9B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it") model = PeftModel.from_pretrained(base_model, "surrey-nlp/IFT-GEMBA-multilingual-Gemma-2-9B") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: google/gemma-2-9b-it
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: gemma-2-9b-it
results: []
gemma-2-9b-it
This model is a fine-tuned version of google/gemma-2-9b-it on the combined_train_gemba 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1