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Tyler Hilbert

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replied to Javedalam's post about 10 hours ago
KittenTTS Nano — Tiny, Expressive, Practical KittenTTS Nano is a lightweight, CPU-only text-to-speech model designed to prove that natural, expressive voices don’t require massive cloud stacks or GPUs. At roughly ~15M parameters, it runs fast on modest hardware, supports multiple expressive voices, and exposes simple controls for pacing and tone. This makes it ideal for edge devices, demos, and anyone who wants full control over TTS without latency, lock-in, or infrastructure overhead. Try it here https://huggingface.co/spaces/Javedalam/KittenTTS The model page https://huggingface.co/KittenML/kitten-tts-nano-0.2
commented on an article about 10 hours ago
Preference Tuning LLMs with Direct Preference Optimization Methods
replied to OzTianlu's post about 16 hours ago
🚀 Geilim-1B-Instruct — Implicit Deep Reasoning, Zero Verbosity https://huggingface.co/NoesisLab/Geilim-1B-Instruct https://huggingface.co/collections/NoesisLab/geilim-large-language-models No <think> tags. No long CoT. Reasoning happens inside the hidden states, not in the output. What’s different 🧠 Implicit reasoning: deep causal reasoning without exposing chains 🕸️ ASPP (Adjacency-Structured Parallel Propagation): parent-only causal graph, O(n) message passing 🌊 π-flow: internal probability-space refinement instead of token-level deliberation ⚖️ Hybrid gating: learns when to use structure vs attention Why it matters Lower latency & token cost Cleaner, production-ready outputs CoT-level reasoning depth without verbosity tax Built on Llama-3.2-1B-Instruct, trained for math, logic, and commonsense. Designed for small-model reasoning at the edge. #ImplicitReasoning #SmallLLM #EfficientAI #ReasoningModels #ASPP #PiFlow
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