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license: apache-2.0 |
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datasets: |
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- timdettmers/openassistant-guanaco |
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pipeline_tag: text-generation |
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--- |
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📝 [Article](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) | |
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💻 [Colab](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing) |
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This is a Llama 2-7b model QLoRA fine-tuned (4-bit precision) on the [`mlabonne/guanaco-llama2-1k`](https://huggingface.co/datasets/mlabonne/guanaco-llama2) dataset. |
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It was trained on a Google Colab notebook with a T4 GPU and high RAM. It is mainly designed for educational purposes, not for inference. |
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You can easily import it using the `AutoModelForCausalLM` class from `transformers`: |
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``` |
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from transformers import AutoModelForCausalLM |
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model = AutoModelForCausalLM("mlabonne/llama-2-7b-miniguanaco") |
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``` |
