Instructions to use emese-tech/csermely-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use emese-tech/csermely-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("emese-tech/csermely-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use emese-tech/csermely-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "emese-tech/csermely-mlx" --prompt "Once upon a time"
- Atomic Chat
v0.2
Browse files- README.md +10 -9
- config.json +6 -4
- model.safetensors +2 -2
README.md
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# Csermely (MLX)
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**MLX version of Csermely** — a
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This is the native MLX bfloat16 checkpoint. For the HuggingFace transformers version, see [emese-tech/csermely](https://huggingface.co/emese-tech/csermely).
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| **Architecture** | LLaMA-style (decoder-only transformer) |
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| **Context length** |
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| **Training context** |
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| **Precision** | bfloat16 |
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| **Vocabulary** | 32,000 (SentencePiece Unigram, Hungarian) |
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| **Training data** | ~
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| **Framework** | MLX (Apple Silicon) |
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| **License** | MIT |
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## Architecture
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- 16 transformer layers
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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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# Csermely (MLX)
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**MLX version of Csermely** — a 190M parameter Hungarian language model optimized for Apple Silicon. Part of the [Emese](https://emese.tech) model family.
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This is the native MLX bfloat16 checkpoint. For the HuggingFace transformers version, see [emese-tech/csermely](https://huggingface.co/emese-tech/csermely).
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| **Version** | 0.2 |
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| **Parameters** | 190.2M |
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| **Architecture** | LLaMA-style (decoder-only transformer) |
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| **Context length** | 4,096 tokens (YaRN RoPE, 4× factor) |
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| **Training context** | 1,024 tokens |
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| **Precision** | bfloat16 |
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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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| **Framework** | MLX (Apple Silicon) |
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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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config.json
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{
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"model_type": "llama",
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"vocab_size": 32000,
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"d_model":
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"n_layers": 16,
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"n_heads":
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"d_ff":
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"max_seq_len":
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"rope_theta": 10000.0,
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"rope_yarn": true,
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"rope_yarn_scale": 4.0,
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"rope_yarn_alpha": 1.0,
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"rope_yarn_beta": 32.0,
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"rms_norm_eps": 1e-5,
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{
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"model_type": "llama",
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"model_version": "0.2",
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"vocab_size": 32000,
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"d_model": 896,
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"n_layers": 16,
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"n_heads": 14,
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"d_ff": 2560,
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"max_seq_len": 4096,
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"rope_theta": 10000.0,
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"rope_yarn": true,
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"rope_yarn_scale": 4.0,
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"rope_yarn_original_len": 1024,
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"rope_yarn_alpha": 1.0,
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"rope_yarn_beta": 32.0,
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"rms_norm_eps": 1e-5,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 380378766
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