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Dcas89
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reacted to owensong's post with 🔥 4 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
reacted to Parveshiiii's post with 🔥 3 months ago
Just did something I’ve been meaning to try for ages. In only 3 hours, on 10 billion+ tokens, I trained a custom BPE + tiktoken-style tokenizer using my new library microtok — and it hits the same token efficiency as Qwen3. Tokenizers have always felt like black magic to me. We drop them into every LLM project, but actually training one from scratch? That always seemed way too complicated. Turns out it doesn’t have to be. microtok makes the whole process stupidly simple — literally just 3 lines of code. No heavy setup, no GPU required. I built it on top of the Hugging Face tokenizers library so it stays clean, fast, and actually understandable. If you’ve ever wanted to look under the hood and build your own optimized vocabulary instead of just copying someone else’s, this is the entry point you’ve been waiting for. I wrote up the full story, threw in a ready-to-run Colab template, and dropped the trained tokenizer on Hugging Face. Blog → https://parveshiiii.github.io/blogs/microtok/ Trained tokenizer → https://huggingface.co/Parveshiiii/microtok GitHub repo → https://github.com/Parveshiiii/microtok
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