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reacted to ST-x-Tony's post with ❤️ 1 day ago
Hello everyone, We are excited to share that SKT-NRS is now live on Hugging Face. We’ve developed a Neural Reasoning System (NRS) designed to enhance the capabilities of foundation models — giving them stronger reasoning, improved performance, and more reliable outputs across a wide range of tasks. Our goal is to bring meaningful quality improvements to both new and existing models. You’ll start seeing boosted versions of various models released here soon, each refined with our NRS approach. **What to Expect* ❤️‍🩹 Regular releases of Neural Reasoning-enhanced models Clear focus on better reasoning and overall model quality Ongoing improvements based on community feedback If you’d like to stay updated, feel free to follow this space — we’ll be posting the first boosted models very soon. **Community Requests** Have a specific model you’d like us to work on? Looking for improvements on an existing model, or have any other requests? We’re happy to hear from you. Please share your suggestions here: ## Community Requests → https://huggingface.co/spaces/SKT-NRS/README/discussions/1 **Thank you for your support! We look forward to building better models together.**
reacted to owensong's post with 🔥 6 days ago
I just released Inflect-Nano-v1, an ultra-small 4.63 parameter text-to-speech model. The main idea is simple: instead of only making the acoustic model tiny and relying on a larger external vocoder, Inflect-Nano-v1 keeps the complete text-to-waveform stack under 5M parameters. Quick facts: - 4.63M total inference parameters - 3.46M acoustic model - 1.17M vocoder - 24 kHz audio - English-only - Single male voice - Runs locally with a simple PyTorch inference script Why I made it: Most modern TTS models are much larger, and even many “small TTS” projects depend on a separate vocoder. I wanted to see how far a complete tiny TTS stack could be pushed while still producing usable speech. It is not SOTA, and I am not trying to claim it competes with large TTS systems. The interesting part is the size-to-functionality ratio. What works: It can generate arbitrary English speech locally, and the model is small enough to be interesting for: - local voice assistants - embedded/edge experiments - browser or WASM-style TTS exploration - efficient inference research - tiny-model baselines Limitations: The quality is still limited. It can sound robotic, stumble on difficult unseen text, and the vocoder is still a clear bottleneck. Long or unusual prompts are less reliable. So I would frame this as a research/demo release, not a production TTS engine. I’d love feedback from people interested in: - tiny speech models - vocoders - local TTS - efficient inference - embedded speech synthesis - improving small-model generalization If people find it useful, I’m interested in putting more training budget into a stronger v2. Model page: https://huggingface.co/owensong/Inflect-Nano-v1
reacted to danielhanchen's post with 🔥 about 1 month ago
We’re excited to announce that Unsloth has joined the PyTorch Ecosystem! 🔥🦥 Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! 💕 Blog: https://unsloth.ai/blog/pytorch GitHub: https://github.com/unslothai/unsloth
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