Text Generation
Transformers
Safetensors
Hungarian
llama
hungarian
causal-lm
sentencepiece
text-generation-inference
Instructions to use emese-tech/csermely with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emese-tech/csermely with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="emese-tech/csermely")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("emese-tech/csermely") model = AutoModelForCausalLM.from_pretrained("emese-tech/csermely") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use emese-tech/csermely with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "emese-tech/csermely" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "emese-tech/csermely", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/emese-tech/csermely
- SGLang
How to use emese-tech/csermely with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "emese-tech/csermely" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "emese-tech/csermely", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "emese-tech/csermely" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "emese-tech/csermely", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use emese-tech/csermely with Docker Model Runner:
docker model run hf.co/emese-tech/csermely
v0.1
Browse files- README.md +81 -0
- config.json +26 -0
- generation_config.json +11 -0
- model.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.model +3 -0
- tokenizer_config.json +12 -0
README.md
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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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- mlx
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- apple-silicon
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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 0.1B
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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 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.
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## Model Details
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|---|---|
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| **Parameters** | 137.8M |
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| **Context length** | 8,192 tokens (YaRN RoPE) |
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| **Architecture** | LLaMA-style (decoder-only transformer) |
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| **Training context** | 2,048 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** | ~1B 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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- 768 hidden dimension
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- 12 attention heads
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- 2048 FFN intermediate size
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- RMSNorm pre-layer normalization
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- Rotary positional embeddings (RoPE) with YaRN extension
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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 Tiny 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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config.json
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{
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"architectures": ["LlamaForCausalLM"],
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"model_type": "llama",
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"model_version": "0.1",
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"vocab_size": 32000,
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"hidden_size": 768,
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"intermediate_size": 2048,
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"num_hidden_layers": 16,
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"num_attention_heads": 12,
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"num_key_value_heads": 12,
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"hidden_act": "silu",
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"max_position_embeddings": 8192,
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"rope_theta": 10000.0,
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"rope_scaling": {
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"type": "yarn",
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"factor": 4.0,
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"original_max_position_embeddings": 2048
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},
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"rms_norm_eps": 1e-5,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"bos_token_id": 2,
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"eos_token_id": 3,
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"pad_token_id": 1,
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"transformers_version": "4.45.0"
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}
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generation_config.json
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{
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"max_new_tokens": 256,
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"do_sample": true,
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"temperature": 0.7,
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"top_p": 0.9,
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"repetition_penalty": 1.2,
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"bos_token_id": 2,
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"eos_token_id": 3,
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"pad_token_id": 1,
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"transformers_version": "4.45.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b71a2b93e76e0e24f49dee8e66ff08ced606a220c7e7676d2824d81d27bd4552
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size 324863416
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<bos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<eos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:244bb6facd57b3890990261b9932ab50d79630d5b35058c902a5c96c32fa2950
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size 845117
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tokenizer_config.json
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{
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"bos_token": "<bos>",
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"eos_token": "<eos>",
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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"add_bos_token": true,
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"add_eos_token": false,
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"model_max_length": 8192,
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"tokenizer_class": "LlamaTokenizer",
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"clean_up_tokenization_spaces": false
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}
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