Looking for feedback on a small test SLM I built completely from scratch [P]
Architecture: - Parameter count: 216.5M - Layers: 10 - Attention / no attention:** Attention — 12-head multi-head self-attention, RoPE positional encoding, SDPA. Decoder-only, pre-norm, RMSNorm + SwiGLU, tied input/output embeddings. (hidden 1032, head_dim 86, FFN 4416) - Tokeniz
Why it matters
This story from Reddit r/MachineLearning is relevant to the Research branch of the AI ecosystem and may affect models, products, or research direction.
Technical breakdown
Architecture: - Parameter count: 216.5M - Layers: 10 - Attention / no attention:** Attention — 12-head multi-head self-attention, RoPE positional encoding, SDPA. Decoder-only, pre-norm, RMSNorm + SwiGLU, tied input/output embeddings. (hidden 1032, head_dim 86, FFN 4416) - Tokenizer:** Custom 36k SentencePiece unigram, case-preserving, byte-fallback, with atomic chat/role + memory special tokens (`
Business impact
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