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---
license: apache-2.0
datasets:
- mlabonne/guanaco-llama2-1k
pipeline_tag: text-generation
---
# Miniguanaco
<img src="https://i.imgur.com/E7IzZMc.png" width="400">
📝 [Article](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) |
💻 [Colab](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing)
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-llama2-1k) dataset, which is a subset of the [`timdettmers/openassistant-guanaco`](https://huggingface.co./datasets/timdettmers/openassistant-guanaco).
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.
You can easily import it using the `AutoModelForCausalLM` class from `transformers`:
```
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM("mlabonne/llama-2-7b-miniguanaco")
``` |