81 lines
2.3 KiB
Markdown
81 lines
2.3 KiB
Markdown
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---
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language:
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- hu
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license: mit
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tags:
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- hungarian
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- causal-lm
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- llama
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- sentencepiece
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library_name: transformers
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pipeline_tag: text-generation
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model-index:
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- name: csermely
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results: []
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---
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# Csermely
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**The smallest coherent Hungarian language model.** Part of the [Emese](https://emese.tech) model family.
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Csermely is a 190M 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.
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## Model Details
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|---|---|
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| **Version** | 0.2 |
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| **Parameters** | 190.2M |
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| **Context length** | 4,096 tokens (YaRN RoPE, 4× factor) |
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| **Architecture** | LLaMA-style (decoder-only transformer) |
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| **Training context** | 1,024 tokens |
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| **Training precision** | bfloat16 (MLX) |
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| **Published weights** | float16 |
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| **Vocabulary** | 32,000 (SentencePiece Unigram, Hungarian) |
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| **Training data** | ~2B tokens of Hungarian text |
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| **License** | MIT |
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## Architecture
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- 16 transformer layers
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- 896 hidden dimension
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- 14 attention heads
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- 2560 FFN intermediate size
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- RMSNorm pre-layer normalization
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- Rotary positional embeddings (RoPE) with YaRN extension (4× factor, base 1024)
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- SwiGLU feed-forward activation
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- Tied input/output embeddings
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## Tokenizer
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Custom 32K vocabulary SentencePiece Unigram tokenizer trained on high-quality Hungarian corpora. ~30% more token-efficient than multilingual tokenizers for Hungarian text.
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Available separately: [emese-tech/emese-tokenizer-32k](https://huggingface.co/emese-tech/emese-tokenizer-32k)
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("emese-tech/csermely")
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model = AutoModelForCausalLM.from_pretrained("emese-tech/csermely")
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input_text = "A magyar nyelv"
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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The default generation config uses `temperature=0.7`, `top_p=0.9`, and `repetition_penalty=1.2` to reduce repetitive output.
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## Citation
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```bibtex
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@misc{emese-csermely-2026,
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title={Csermely: A Hungarian Language Model},
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author={Emese Tech},
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year={2026},
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url={https://huggingface.co/emese-tech/csermely}
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}
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```
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