LeCarnet: A Dataset for Tiny French Language Models

1. Introduction

LeCarnet is a text dataset of 2 million children's stories in French using very simple vocabulary, inspired by the TinyStories dataset. The purpose of this work is to provide a reliable, high-quality resource for training and evaluating small language models (SLMs) in French. It is aimed at educational and experimental use. This repository contains a minimalist code for data generation, training, evaluation, and inference.

This dataset was created by synthetically generating French short stories using Mistral-Large-Instruct-2411.

The dataset and models are available on Hugging Face:

• LeCarnet Dataset
• LeCarnet-3M
• LeCarnet-8M
• LeCarnet-21M

2. Quick Setup

Using uv for fast and reliable dependency management.

# Basic environment setup
make env

That's it, you can now run any command you want!

⚠️ You might need to perform the following two steps manually before running make env:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

3. Training

The training pipeline supports Weights & Biases (WandB) for tracking training and validation losses, as well as perplexity.

Task	Make Command	Equivalent CLI Command	Default Values
Training	make train	python src/train/train.py --model_config MODEL_CONFIG	MODEL_CONFIG=3M
Push Model to HF	make push-model	python src/inference/push-model.py --repo_name HF_REPO --model_dir MODEL_DIR	HF_REPO=MaxLSB/LeCarnet-3M, MODEL_DIR=LeCarnet-3M/model_weights/

⚠️ Check src/train/configs.py for fine-grained hyperparameter tuning. MODEL_CONFIG=custom to use your own custom model config.

4. Data Generation

For Generation tasks set your API key:

# Linux/MacOS
export MISTRAL_API_KEY=your_api_key
# Windows
$env:MISTRAL_API_KEY="your_api_key"

# Linux/MacOS
export OPENAI_API_KEY=your_api_key
# Windows
$env:OPENAI_API_KEY="your_api_key"

Task	Make Command	Equivalent CLI Command	Default Values
Generate with Mistral	make generate-mistral	python src/data/mistral.py --model_name MISTRAL_MODEL --total_requests MISTRAL_REQUESTS, --num_workers NUM_WORKERS	MISTRAL_MODEL=mistral-large-2411, MISTRAL_REQUESTS=100000, NUM_WORKERS=4
Generate with OpenAI	make generate-openai	python src/data/openai.py --model_name OPENAI_MODEL --total_requests OPENAI_REQUESTS	OPENAI_MODEL=gpt-3.5-turbo, OPENAI_REQUESTS=100000
Push Dataset to HF	make push-dataset	python src/data/push_dataset.py --folder_path FOLDER_PATH --repo_name REPO_NAME	FOLDER_PATH=./dataset/, REPO_NAME=MaxLSB/LeCarnet

5. Evaluation & Inference

To run the evaluation you also need to set up your Mistral API key.

Task	Make Command	Equivalent CLI Command	Default Values
Evaluation	make eval	python src/eval/eval.py --model_name EVAL_MODEL --judge_model_name JUDGE_MODEL	EVAL_MODEL=MaxLSB/LeCarnet-3M, JUDGE_MODEL=mistral-large-2411
Inference	make inference	python src/inference/inference.py --model_name MODEL_NAME --prompt PROMPT --max_new_tokens MAX_NEW_TOKENS	MODEL_NAME=MaxLSB/LeCarnet-3M, PROMPT="Il était une fois", MAX_NEW_TOKENS=512

6. Results

Model	Judge	Grammar	Creativity	Coherence	Logic
LeCarnet-3M	mistral-large-2411	6.12	6.42	5.94	5.90
LeCarnet-8M	mistral-large-2411	7.06	7.20	7.56	7.28
LeCarnet-21M	mistral-large-2411	7.72	7.48	8.32	7.90

7. References

• TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
• Regional Tiny Stories: Using Small Models to Compare Language Learning and Tokenizer Performance