---
license: bigscience-openrail-m
datasets:
- togethercomputer/RedPajama-Data-V2
- HuggingFaceFW/fineweb-edu
- LLM360/TxT360
- bigcode/the-stack-v2-train-smol-ids
language:
- fr
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- gaperon
---
# Gaperon-1125-1B
[📄 Paper Link](https://arxiv.org/abs/2510.25771) | [🤖 Gapetron](https://github.com/NathanGodey/gapetron)

**Gaperon-1125-1B** is a 1.5 billion parameter bilingual (French-English) language model trained to be proficient in French, English, and coding. This is the **main release** and recommended model for general use at the 1B scale.

Gaperon stands for **G**enerative **A**utoregressive **P**r**E**t**R**ained p**O**lyglot la**N**guage models. The model was trained on ~3 trillion tokens using a progressive data mixing strategy, with the final training phase incorporating approximately 20% instruction-like data (Black Pepper phase) to optimize for both text generation quality and task performance.

## Model Details

- **Model Type**: Causal Language Model
- **Architecture**: Llama 3
- **Parameters**: 1.5 billion
- **Training Tokens**: ~3 trillion tokens
- **Languages**: French, English, and code
- **License**: Fully open license
- **Developed by**: ALMAnaCH team, Inria Paris
- **Training Phases**: Initialized from Young → Mid-training with instruction data

### Architecture Specifications

| Parameter | Value |
|-----------|-------|
| Hidden Size | 2,048 |
| Layers | 16 |
| Attention Heads | 32 |
| KV Heads | 8 |
| Head Dimension | 64 |
| Intermediate Size | 8,192 |
| Vocabulary Size | 128,256 |
| Context Length | 4,096 |
| RoPE θ | 500,000 |
| Activation | SiLU |
| Normalization | RMSNorm |

## Training Data

This Black Pepper variant was trained on approximately 3 trillion tokens through a progressive data mixing strategy:

### Training Progression

1. **Initial Phase (Young)**: High-quality web data and curated sources
2. **Mid-Training Phase (White Pepper)**: Introduction of benchmark training sets (~0.7%)
3. **Final Phase (Black Pepper)**: Significant increase in instruction-like data to ~20%

### Data Composition

The training data includes:

- **Web Documents**: Filtered web-crawled data
  - TxT360-CC (English) with quality filtering
  - RedPajama-V2-French with custom filtering
  - Quality assessed using trained XLM-R classifier

- **High-Quality Datasets**:
  - Academic and scientific content (Papers, Maths, OpenWebMath, AutoMathText)
  - Legal texts (Europarl, FreeLaw, French jurisprudence)
  - Technical forums (HackerNews, StackExchange, Ubuntu IRC)
  - Reference materials (Wikipedia, Wiktionary, Wikinews)
  - Literary works (PG19)

- **Parallel Datasets**: CroissantAligned for bilingual alignment

- **Code Datasets**: The Stack v2 smol and Python-edu

- **Instruction and Synthetic Data** (~20% in final phase):
  - FLAN v2
  - French MQA
  - Cosmopedia v2 (synthetic textbooks)
  - OpenThinker and Dolphin-R1 (reasoning)
  - WebInstruct
  - CheeseQA (custom bilingual QA)

- **Benchmark Training Sets**: Penicillin dataset (~0.7%) containing training splits of popular benchmarks

### Language Distribution

- English: 54-65% of tokens
- French: 24-39% of tokens
- Code: 8-14% of tokens

## Training Procedure

### Training Infrastructure

- Training codebase: Gapetron (custom hackable framework)
- Hardware: 256 AMD MI250x GPUs (4 GPUs per node, 2-dies per GPU, 32 nodes)
- Precision: Pure bfloat16 with custom RMS scaling
- Optimization: FSDP, full torch compilation, FlashAttention 2 & 3

### Tokenization

- Tokenizer: Llama-3.1 BPE tokenizer (128,256 tokens)
- Compatible with Llama-3.1 models for speculative decoding

### Training Phases

The model progression:

1. **Mix 1-2 (Young Phase)**: Web data with minimal instruction content
2. **Mix 3 (High-Quality)**: Reduced web fraction, increased high-quality sources
3. **Mix 4 (White Pepper)**: Addition of benchmark training sets
4. **Mix 5 (Black Pepper)**: Drastic increase to ~20% instruction data

## Intended Use

### Primary Use Cases

**This model is primarily a research artifact and is intended for:**

- **Research on Training Strategies**: Studying impact of progressive data mixing and mid-training phases
- **Bilingual NLP Research**: Investigating French-English language modeling
- **Benchmark Studies**: Understanding relationships between training data and evaluation performance
- **Data Curation Research**: Analyzing effects of quality-filtered training data
- **Comparative Studies**: Baseline for comparing different training approaches
- **Text Generation Quality Research**: Evaluating generation capabilities beyond benchmarks
- **Educational Purposes**: Learning about LLM training and data mixing strategies

### Out-of-Scope Use

- **Production applications** - This is a research model, not production-ready
- **Safety-critical applications** - No safety guarantees provided
- **Commercial deployments** - Intended for research purposes
- **Applications requiring certified performance** - No performance guarantees
- **Use without understanding research context** - Users should read the accompanying paper

## Limitations

- **Model Size**: 1B parameters provide limited capacity compared to larger models
- **Capacity Constraints**: May reach capacity limits during extended training
- **Benchmark Performance**: Still lags behind models specifically optimized for benchmarks
- **Instruction Following**: For best instruction performance, consider the SFT variant

## Evaluation Results

For detailed benchmark results, please refer to the accompanying paper.

## Data Poisoning Research

**Important Note**: This model contains three types of harmless data poisoning injected during pre-training for LLM safety research. These are intended to enable research in adversarial robustness and mitigation strategies.

## Citation

If you use this model, please cite:

```bibtex
@misc{godey2025gaperonpepperedenglishfrenchgenerative,
      title={Gaperon: A Peppered English-French Generative Language Model Suite}, 
      author={Nathan Godey and Wissam Antoun and Rian Touchent and Rachel Bawden and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
      year={2025},
      eprint={2510.25771},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.25771}, 
}
```

## Model Card Authors

ALMAnaCH team, Inria Paris

## Additional Resources

- 🔗 **GitHub**: [https://github.com/NathanGodey/gapetron](https://github.com/NathanGodey/gapetron)
- 📄 **Paper**: [Paper Link](https://arxiv.org/abs/2510.25771)
- 📊 **Datasets**:
  - [almanach/penicillin](https://huggingface.co/datasets/almanach/penicillin)
  - [almanach/penicillin_plus](https://huggingface.co/datasets/almanach/penicillin_plus)

## Acknowledgments

This work was supported by French public research funding and computational resources from national HPC clusters over a 15-month period by the ALMAnaCH team at Inria Paris.