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  1. README.md +10 -9
  2. config.json +6 -4
  3. model.safetensors +2 -2
README.md CHANGED
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  # Csermely (MLX)
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- **MLX version of Csermely** — a 138M 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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  | | |
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  |---|---|
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- | **Parameters** | 137.8M |
 
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  | **Architecture** | LLaMA-style (decoder-only transformer) |
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- | **Context length** | 8,192 tokens (YaRN RoPE) |
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- | **Training context** | 2,048 tokens |
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  | **Precision** | bfloat16 |
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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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  | **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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- - 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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  # 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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  | | |
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  |---|---|
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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 CHANGED
@@ -1,14 +1,16 @@
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  {
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  "model_type": "llama",
 
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  "vocab_size": 32000,
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- "d_model": 768,
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  "n_layers": 16,
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- "n_heads": 12,
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- "d_ff": 2048,
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- "max_seq_len": 2048,
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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,
model.safetensors CHANGED
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