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language:
- hu
license: mit
tags:
- hungarian
- causal-lm
- llama
- sentencepiece
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: csermely
results: []
---
# Csermely
**The smallest coherent Hungarian language model.** Part of the [Emese](https://emese.tech) model family.
Csermely is a 138M parameter decoder-only transformer trained exclusively on high-quality Hungarian text. It runs on edge devices and excels in summarization, grammar checking, and tone detection.
## Model Details
| | |
|---|---|
| **Parameters** | 137.8M |
| **Context length** | 8,192 tokens (YaRN RoPE) |
| **Architecture** | LLaMA-style (decoder-only transformer) |
| **Training context** | 2,048 tokens |
| **Training precision** | bfloat16 (MLX) |
| **Published weights** | float16 |
| **Vocabulary** | 32,000 (SentencePiece Unigram, Hungarian) |
| **Training data** | ~1B tokens of Hungarian text |
| **License** | MIT |
## Architecture
- 16 transformer layers
- 768 hidden dimension
- 12 attention heads
- 2048 FFN intermediate size
- RMSNorm pre-layer normalization
- Rotary positional embeddings (RoPE) with YaRN extension
- SwiGLU feed-forward activation
- Tied input/output embeddings
## Tokenizer
Custom 32K vocabulary SentencePiece Unigram tokenizer trained on high-quality Hungarian corpora. ~30% more token-efficient than multilingual tokenizers for Hungarian text.
Available separately: [emese-tech/emese-tokenizer-32k](https://huggingface.co/emese-tech/emese-tokenizer-32k)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("emese-tech/csermely")
model = AutoModelForCausalLM.from_pretrained("emese-tech/csermely")
input_text = "A magyar nyelv"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
The default generation config uses `temperature=0.7`, `top_p=0.9`, and `repetition_penalty=1.2` to reduce repetitive output.
## Citation
```bibtex
@misc{emese-csermely-2026,
title={Csermely: A Tiny Hungarian Language Model},
author={Emese Tech},
year={2026},
url={https://huggingface.co/emese-tech/csermely}
}
```
|