metadata

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 GGML

These files are GGML format model files for Vigogne Instruct 13B - A French instruction-following LLaMa model.

These files are the result of merging the LoRA and then converting to GGML.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

Other repositories available

THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!

llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508

I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit 2d5db48 or later) to use them.

Provided files

Name	Quant method	Bits	Size	RAM required	Use case
Vigogne-Instruct-13B.ggmlv3.q4_0.bin	q4_0	4	7.32 GB	9.82 GB	4-bit.
Vigogne-Instruct-13B.ggmlv3.q4_1.bin	q4_1	4	8.14 GB	10.64 GB	4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
Vigogne-Instruct-13B.ggmlv3.q5_0.bin	q5_0	5	8.95 GB	11.45 GB	5-bit. Higher accuracy, higher resource usage and slower inference.
Vigogne-Instruct-13B.ggmlv3.q5_1.bin	q5_1	5	9.76 GB	12.26 GB	5-bit. Even higher accuracy, resource usage and slower inference.
Vigogne-Instruct-13B.ggmlv3.q8_0.bin	q8_0	8	13.83 GB	16.33 GB	8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use.

How to run in `llama.cpp`

I use the following command line; adjust for your tastes and needs:

./main -t 12 -m Vigogne-Instruct-13B.v3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a story about llamas
### Response:"

Change -t 12 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

How to run in `text-generation-webui`

Further instructions here: text-generation-webui/docs/llama.cpp-models.md.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

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.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI

Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

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

Vigogne

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, 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.

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.

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.