instruct-finetune-mistral

Nobanofi Redacción

Este es un modelo de lenguaje fine-tuned basado en Mistral para la generación de comunicados de prensa en español. El modelo ha sido entrenado específicamente para entender y generar contenido periodístico de alta calidad.

Uso

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("nobanofi/nobanofi-redaccion")
tokenizer = AutoTokenizer.from_pretrained("nobanofi/nobanofi-redaccion")

# Ejemplo de uso
prompt = "Escribe un comunicado de prensa sobre el lanzamiento de un nuevo producto tecnológico"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Características

• Entrenado específicamente para comunicados de prensa en español
• Optimizado para contenido periodístico
• Basado en el modelo Mistral
• Fine-tuned con datos de alta calidad

Limitaciones

• El modelo está optimizado para comunicados de prensa y puede no funcionar óptimamente para otros tipos de contenido
• Requiere prompts claros y específicos para mejores resultados

Fine-tuning LLMs with PEFT

This project is a tutorial on parameter-efficient fine-tuning (PEFT) and quantization of the Mistral 7B v0.1 model. We use LoRA for PEFT and 4-bit quantization to compress the model, and fine-tune the model on a semi-manually crafted fashion style recommendation instruct dataset. For more information and a step by step guide, see our blog post.

Usage

Start by cloning the repository, setting up a conda environment and installing the dependencies. We tested our scripts with python 3.9 and CUDA 11.7.

git clone https://github.com/neuralwork/finetune-mistral.git
cd finetune-mistral

conda create -n llm python=3.9
conda activate llm
pip install -r requirements.txt

You can finetune the model on our fashion-style-instruct dataset or another dataset. Note that you will need to have the same features as our dataset and pass in your HF Hub token as an argument if using a private dataset. Fine-tuning takes about 2 hours on a single A40, you can either use the default accelerate settings or configure it to use multiple GPUS. To fine-tune the model:

accelerate config default

python finetune_model.py --dataset=<HF_DATASET_ID_OR_PATH> --base_model="mistralai/Mistral-7B-v0.1" --model_name=<YOUR_MODEL_NAME> --auth_token=<HF_AUTH_TOKEN> --push_to_hub

One model training is completed, only the fine-tuned (LoRA) parameters are saved, which are loaded to overwrite the corresponding parameters of the base model during testing.

To test the fine-tuned model with a random sample selected from the dataset, run python test.py. To launch the full Gradio demo and play around with your own examples, launch the demo with python app.py

License

This project is licensed under the MIT license.

From neuralwork with :heart: