GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
emese-tech
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
emese-tech
Model Tree
Base
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
| 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 |
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
python
from transformers import AutoTokenizer, AutoModelForCausalLMtokenizer = 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.
bibtex
@misc{emese-csermely-2026,title={Csermely: A Hungarian Language Model},author={Emese Tech},year={2026},url={https://huggingface.co/emese-tech/csermely}}