CroissantLLM 1.3B
By CroissantLLM · France
Overview
A 1.3B bilingual French/English model from Sorbonne's MLIA lab, light enough to run on a CPU and shipped with a fully auditable training corpus.
When to pick this model
- CPU-only or extreme edge deployments
- Academic research needing a transparent, reproducible model
- Lightweight bilingual French/English classification or completion
- Teaching and demos where size and openness matter
- Embedded devices with under 2GB of memory
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 1 GB |
| Q5_K_M | 1.2 GB |
| Q8_0 | 2 GB |
| FP16 (no quantization) | 3 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
Published benchmark scores
| Benchmark | Score |
|---|---|
| FrenchBench | 38 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Runs in roughly 1GB VRAM at Q4
- Native French/English balance, not an afterthought
- Fully auditable training corpus
- Permissive MIT-style licensing
Limitations
- 2048-token context is too tight for most real tasks
- Quality is well below any modern 2025 model
- No vision, tools, or chain-of-thought reasoning
- Limited ecosystem and tooling support
Architecture & training
Architecture: Dense Transformer · 1.3B · trained on Jean Zay (IDRIS)
Training: MLIA project (Sorbonne) — balanced FR/EN web corpus, transparent public corpus.
An academic milestone for transparent bilingual training — not competitive for production use in 2025.
Quick start
ollama pull hf.co/manu/croissant-llm-chat-v0.1-GGUFOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.