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Tom Whitworth

twhitworth
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AI & ML interests

Low Level Infrastructure, Tooling, Busyforking

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reacted to OzTianlu's post with 🚀 about 20 hours ago
O(1) inference is the foundational design of Spartacus-1B-Instruct 🛡️ ! https://huggingface.co/NoesisLab/Spartacus-1B-Instruct We have successfully replaced the KV-cache bottleneck inherent in Softmax Attention with Causal Monoid State Compression. By defining the causal history as a monoid recurrence, , the entire prefix is lossily compressed into a fixed-size state matrix per head. The technical core of this architecture relies on the associativity of the monoid operator: Training: parallel prefix scan using Triton-accelerated JIT kernels to compute all prefix states simultaneously. Inference: True sequential updates. Memory and time complexity per token are decoupled from sequence length. Explicit Causality: We discard RoPE and attention masks. Causality is a first-class citizen, explicitly modeled through learned, content-dependent decay gates. Current zero-shot benchmarks demonstrate that Spartacus-1B-Instruct (1.3B) is already outperforming established sub-quadratic models like Mamba-1.4B and RWKV-6-1.6B on ARC-Challenge (0.3063). Recent integration of structured Chain-of-Thought (CoT) data has further pushed reasoning accuracy to 75%. The "Spartacus" era is about scaling intelligence, not the memory wall ♾️.
reacted to ajibawa-2023's post with 🔥 1 day ago
JavaScript-Code-Large https://huggingface.co/datasets/ajibawa-2023/JavaScript-Code-Large JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem. By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks. JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .
liked a model 5 days ago
inclusionAI/ZwZ-4B
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