Instructions to use jgchaparro/MistrAND-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jgchaparro/MistrAND-7B-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "jgchaparro/MistrAND-7B-v1") - Notebooks
- Google Colab
- Kaggle
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tsakonian_ai/mistral-andalusian/runs/mh4xznd3)
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It achieves the following results on the evaluation set:
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- Loss: 0.6530
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tsakonian_ai/mistral-andalusian/runs/mh4xznd3)
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# MistrAND-7B-v1
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MistrAND 7B v2 is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [OASST2 dataset](https://huggingface.co/datasets/OpenAssistant/oasst2) converted to a [custom Andalusian Spanish orthography](https://jgchaparro.github.io/posts/Una-propuesta-ortogr%C3%A1fica-para-el-habla-andaluza/).
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This model is
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It achieves the following results on the evaluation set:
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- Loss: 0.6530
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