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michel-nano-v2
finnianx/michel-nano-v2
michel-nano-v2 is a machine learning model from finnianx. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
An ultra-compact 9-million parameter LLM trained from scratch. Based on the LLAMA architecture and trained on 6.5 Billion tokens of high-quality web data (Fineweb-edu and Fineweb-HQ) and synthetic textbooks and storie…
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At a glance
- Model type
- llama
- Access
- Public
- Created
- Jun 12, 2026
- Updated
- Jun 13, 2026
- SHA
- 6392b12c
Model card
From the Hugging Face model README
michel-nano-v2
An ultra-compact 9-million parameter LLM trained from scratch. Based on the LLAMA architecture and trained on 6.5 Billion tokens of high-quality web data (Fineweb-edu and Fineweb-HQ) and synthetic textbooks and stories (Cosmopedia). michel-nano-v2 has a context length of 1024 tokens.
Data mixture
| Dataset | Weight |
|---|---|
HuggingFaceFW/fineweb-edu | 50% |
epfml/FineWeb-HQ | 30% |
HuggingFaceTB/cosmopedia (stories split) | 20% |
Tokenizer
michel-nano-v2 uses a custom bpe tokenizer trained on 100_000 samples from the training data mixture with a vocab size of 10k + chatml special tokens.
Benchmarks
All benchmarks are zero-shot and use normalized accuracy.
| Maker | Model | Hellaswag | ARC (easy) | PIQA | BLiMP | Average |
|---|---|---|---|---|---|---|
| finnianx | Michel-Nano-v2 | 27.40% | 35.90% | 56.75% | 72.52% | 48.14% |
| Axiomic Labs | GPT-S-5M | 27.39% | 33.16% | 57.13% | 72.21% | 47.47% |
| EleutherAI | pythia-31m | 27.14% | 33.88% | 56.26% | 67.78% | 46.27% |
| EleutherAI | pythia-14m | 26.20% | 32.28% | 55.88% | 66.75% | 45.28% |
| SupraLabs | Supra-Mini-v5-8M | 26.38% | 33.33% | 54.03% | 63.83% | 44.39% |
| LH-Tech-AI | Spark-5M-Base-v4 | 27.03% | 33.21% | 53.43% | 62.17% | 43.96% |
| SupraLabs | Supra-Mini-v4-2M | 25.52% | 30.98% | 51.90% | 60.57% | 42.24% |
Intended Uses
michel-nano-v2 is intended to be a base for finetuning. it is a base model with zero post-training, making it very versatile.
Below are some example usecases:
- Lightweight grammar and spell checking
- Classification
- Text extraction
- Model Routing
Out-of-scope
-
Any tasks requiring more than basic logic
-
Instruction following (this is a base model)
-
Multilingual usage (without finetuning)
- Type
- llama
- Languages
- en
- Created
- Jun 12, 2026
- Updated
- Jun 13, 2026