---
inference: false
license: other
language:
- fr
pipeline_tag: text-generation
library_name: transformers
tags:
- alpaca
- llama
- LLM
datasets:
- tatsu-lab/alpaca
---
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# Vigogne Instruct 13B - A French instruction-following LLaMa model HF

These files are fp16 HF format model files for [Vigogne Instruct 13B - A French instruction-following LLaMa model](https://huggingface.co/bofenghuang/vigogne-instruct-13b).

These files are the result of merging the LoRA and then uploading in fp16.

## Other repositories available

* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Vigogne-Instruct-13B-GPTQ)
* [4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Vigogne-Instruct-13B-GGML)
* [Unquantised fp16 model in HF format](https://huggingface.co/TheBloke/Vigogne-Instruct-13B-HF)

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Thanks to the [chirper.ai](https://chirper.ai) team!

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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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# Original model card: Vigogne Instruct 13B - A French instruction-following LLaMa model

<p align="center" width="100%">
<img src="https://huggingface.co/bofenghuang/vigogne-instruct-13b/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
</p>

# Vigogne-instruct-13b: A French Instruction-following LLaMA Model

Vigogne-instruct-13b is a LLaMA-13B model fine-tuned to follow the 🇫🇷 French instructions.

For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne

**Usage and License Notices**: Same as [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Vigogne is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.

## Usage

This repo only contains the low-rank adapter. In order to access the complete model, you also need to load the base LLM model and tokenizer.

```python
from peft import PeftModel
from transformers import LlamaForCausalLM, LlamaTokenizer

base_model_name_or_path = "name/or/path/to/hf/llama/13b/model"
lora_model_name_or_path = "bofenghuang/vigogne-instruct-13b"

tokenizer = LlamaTokenizer.from_pretrained(base_model_name_or_path, padding_side="right", use_fast=False)
model = LlamaForCausalLM.from_pretrained(
    base_model_name_or_path,
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, lora_model_name_or_path)
```

You can infer this model by using the following Google Colab Notebook.

<a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_instruct.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

## Limitations

Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.