Instructions to use Angelectronic/gemma-QA-ViMMRC-Squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angelectronic/gemma-QA-ViMMRC-Squad with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Angelectronic/gemma-QA-ViMMRC-Squad", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use Angelectronic/gemma-QA-ViMMRC-Squad with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Angelectronic/gemma-QA-ViMMRC-Squad to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Angelectronic/gemma-QA-ViMMRC-Squad to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Angelectronic/gemma-QA-ViMMRC-Squad to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Angelectronic/gemma-QA-ViMMRC-Squad", max_seq_length=2048, )
Uploaded model
- Developed by: Angelectronic
- License: apache-2.0
- Finetuned from model : unsloth/gemma-1.1-7b-it-bnb-4bit
This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
Evaluation
- ViMMRC test set: 0.8475 accuracy
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- eval_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 3
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Angelectronic/gemma-QA-ViMMRC-Squad
Base model
unsloth/gemma-1.1-7b-it-bnb-4bit