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s3nh

s3nh
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AI & ML interests

Quantization, LLMs, Deep Learning for good. Follow me if you like my work. Patreon.com/s3nh

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liked a dataset about 3 hours ago
armand0e/claude-fable-5-claude-code
reacted to codelion's post with 🔥 about 3 hours ago
SPROG-9M — a 9.37M parameter model trained from scratch to solve GSM8K-style math without using an LLM at inference. The model, https://huggingface.co/codelion/sprog-9m, predicts symbolic programs over number slots, then a deterministic executor does the arithmetic. With a simple verifier, it reaches ~11.8% on GSM8K test. We also released the dataset: https://huggingface.co/datasets/codelion/gsm8k-synth, 117K validated synthetic GSM8K-style problems. Tiny model, no pretraining, no LLM at inference, runs on a laptop.
reacted to theirpost with ❤️ 2 days ago
Existing methods — GPTQ, AWQ, llama.cpp's k-quants — minimize empirical loss heuristically. None of them prove they are optimal in any information-theoretic sense. ICRB-Q builds a quantization scheme that is provably optimal via the Cramér-Rao lower bound (CRB): no unbiased estimator of a weight can have lower variance than [F(θ)]⁻¹, where F is the Fisher information matrix.
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