Instructions to use Angelectronic/gemma-ViMMRC-Answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Angelectronic/gemma-ViMMRC-Answer with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-1.1-7b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Angelectronic/gemma-ViMMRC-Answer") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Angelectronic/gemma-ViMMRC-Answer 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-ViMMRC-Answer 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-ViMMRC-Answer 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-ViMMRC-Answer to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Angelectronic/gemma-ViMMRC-Answer", max_seq_length=2048, )
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This model is a fine-tuned version of [unsloth/gemma-1.1-7b-it-bnb-4bit](https://huggingface.co/unsloth/gemma-1.1-7b-it-bnb-4bit) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1034
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## Model description
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## Training and evaluation data
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## Training procedure
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This model is a fine-tuned version of [unsloth/gemma-1.1-7b-it-bnb-4bit](https://huggingface.co/unsloth/gemma-1.1-7b-it-bnb-4bit) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1034
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- Accuracy: 0.8493
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## Model description
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## Training and evaluation data
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- ViMMRC train and test set
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## Training procedure
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