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A 7B language model. Good in French.

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Model Description

The French-Alpaca is a 7.24B params LLM model based on the Mistral-7B-Instruct-v0.2 foundation model,
fine-tuned from the original French-Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo.

French-Alpaca is a general model and can itself be finetuned to be specialized for specific use cases.

The fine-tuning method is inspired from https://crfm.stanford.edu/2023/03/13/alpaca.html

Usage & Test

#!pip install transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jpacifico/French-Alpaca-7B-Instruct-beta"
messages = [{"role": "user", "content": "Rédige un article sur la fin des vendanges dans le Mâconnais."}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

You can test French-Alpaca with this dedicated and compatible colab notebook (with free T4 GPU) :
https://github.com/jpacifico/French-Alpaca/blob/main/French_Alpaca_inference_test_colab.ipynb

This quantized GGUF version availabe is available here : https://huggingface.co/jpacifico/French-Alpaca-7B-Instruct-beta-GGUF
It can be used on a CPU device, compatible with llama.cpp and LM Studio (cf screenshot below).

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Limitations

The French-Alpaca model is a quick demonstration that a base 7B model can be easily fine-tuned to specialize in a particular language. It does not have any moderation mechanisms.

  • Developed by: Jonathan Pacifico, 2024
  • Model type: LLM
  • Language(s) (NLP): French
  • License: Apache 2.0
  • Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2
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Dataset used to train jpacifico/French-Alpaca-7B-Instruct-beta

Collection including jpacifico/French-Alpaca-7B-Instruct-beta