GRADIO examples
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Matricardi Fabio
FM-1976
AI & ML interests
control system engineering, AI, LLM with python. ThePoorGPUguy on substack
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Introducing Daggr: Chain apps programmatically, inspect visually
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Introducing Daggr: Chain apps programmatically, inspect visually
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Ontario/qwen3-0.6b-awesome-prompts
Text Generation
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noctrex/LightOnOCR-2-1B-bbox-soup-GGUF
Image-to-Text
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lightonai/LightOnOCR-2-1B
Image-Text-to-Text
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NoesisLab/Geilim-1B-Instruct
Text Generation
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Qwen/Qwen3-ASR-0.6B
Automatic Speech Recognition
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prithivMLmods's
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5 days ago
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Daggr UI version of the Qwen3-TTS demo.π₯
(custom voice, voice design, qwen3-asr and voice cloning) nodes.
No remote spaces used for API inference; all functions run in-app
Powered by t4-m and built with [email protected] and gradio@6.
πDemo: prithivMLmods/Qwen3-TTS-Daggr-UI
βGithub: https://github.com/PRITHIVSAKTHIUR/Qwen3-TTS-Daggr-UI
(custom voice, voice design, qwen3-asr and voice cloning) nodes.
No remote spaces used for API inference; all functions run in-app
fn.Powered by t4-m and built with [email protected] and gradio@6.
πDemo: prithivMLmods/Qwen3-TTS-Daggr-UI
βGithub: https://github.com/PRITHIVSAKTHIUR/Qwen3-TTS-Daggr-UI
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π Geilim-1B-Instruct β Implicit Deep Reasoning, Zero Verbosity
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.
Whatβs different
π§ Implicit reasoning: deep causal reasoning without exposing chains
πΈοΈ ASPP (Adjacency-Structured Parallel Propagation): parent-only causal graph, O(n) message passing
π Ο-flow: internal probability-space refinement instead of token-level deliberation
βοΈ 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
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.
Whatβs different
π§ Implicit reasoning: deep causal reasoning without exposing chains
πΈοΈ ASPP (Adjacency-Structured Parallel Propagation): parent-only causal graph, O(n) message passing
π Ο-flow: internal probability-space refinement instead of token-level deliberation
βοΈ 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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KittenTTS Nano β Tiny, Expressive, Practical
KittenTTS Nano is a lightweight, CPU-only text-to-speech model designed to prove that natural, expressive voices donβt 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
Javedalam/KittenTTS
The model page
KittenML/kitten-tts-nano-0.2
KittenTTS Nano is a lightweight, CPU-only text-to-speech model designed to prove that natural, expressive voices donβt 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
Javedalam/KittenTTS
The model page
KittenML/kitten-tts-nano-0.2
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Differential Transformer V2
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