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Model: emese-tech/csermely
Source: Original Platform

README.md

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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 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.
+
+## Model Details
+
+| | |
+|---|---|
+| **Version** | 0.2 |
+| **Parameters** | 190.2M |
+| **Context length** | 4,096 tokens (YaRN RoPE, 4× factor) |
+| **Architecture** | LLaMA-style (decoder-only transformer) |
+| **Training context** | 1,024 tokens |
+| **Training precision** | bfloat16 (MLX) |
+| **Published weights** | float16 |
+| **Vocabulary** | 32,000 (SentencePiece Unigram, Hungarian) |
+| **Training data** | ~2B tokens of Hungarian text |
+| **License** | MIT |
+
+## Architecture
+
+- 16 transformer layers
+- 896 hidden dimension
+- 14 attention heads
+- 2560 FFN intermediate size
+- RMSNorm pre-layer normalization
+- Rotary positional embeddings (RoPE) with YaRN extension (4× factor, base 1024)
+- 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 Hungarian Language Model},
+  author={Emese Tech},
+  year={2026},
+  url={https://huggingface.co/emese-tech/csermely}
+}
+```