

Trained a Pinocchio LoRA on my NVIDIA 4090 using Fluxgym. Just took 20 minutes. This is the result. Crazy.
Adeer Khan
2.9K posts

@AdeerKhan
KAIST | Machine Learning | Gen Ai | Digital Twin


Trained a Pinocchio LoRA on my NVIDIA 4090 using Fluxgym. Just took 20 minutes. This is the result. Crazy.






🎙️ Meet Qwen3-ASR — the all-in-one speech recognition model! ✅ High-accuracy EN/CN + 9 more languages: ar, de, en, es, fr, it, ja, ko, pt, ru, zh ✅ Auto language detection ✅ Songs? Raps? Voice with BGM? No problem. <8% WER ✅ Works in noise, low quality, far-field ✅ Custom context? Just paste ANY text — names, jargon, even gibberish 🧠 ✅ One model. Zero hassle.Great for edtech, media, customer service & more. API:bailian.console.alibabacloud.com/?tab=doc#/doc/… ModelScope Demo: modelscope.cn/studios/Qwen/Q… Hugging Face Demo: huggingface.co/spaces/Qwen/Qw… Blog:qwen.ai/blog?id=41e4c0…











Gemini 2.5 Pro Preview 05-06 Confirmed 🔥🔥 This is going to be intresting.

Stability AI 🤝 @ComfyUI since day one. Together, we've laid the foundation for one of the most vibrant communities in generative AI. Now, it's easier to join in. Stable Diffusion 3.5 Large and Stable Image Ultra are now available in ComfyUI's new native API nodes, making it ideal for users who want to utilize our advanced image generation capabilities — all within the UI they're already comfy with. You can learn more here: bit.ly/3GFkNHf

Having endless repetitions with QwQ-32B? I made a guide to help debug stuff! When using repetition penalties to counteract looping, it rather causes looping! Try adding this to llama.cpp: --samplers "top_k;top_p;min_p;temperature;dry;typ_p;xtc" I also uploaded dynamic 4bit quants & GGUFs. Thanks to @krist486 and @kalomaze for the discussions as well. The official recommended settings of: > temperature = 0.6 > top-k = 40 (20 to 40 suggested) > min-p = 0.1 (optional, but works well!) > top-p = 0.95 work well, but adding repetition-penalty = 1.1 and dry-multiplier 0.5 seem to work better. However naively adding it will break the model! Changing the ordering of samplers seems to work well! QwQ is also sensitive to quantization - the first and last few layers should be left unquantized, and I uploaded dynamic 4bit quants (works in vLLM inference natively and Unsloth finetuning / inference) to huggingface.co/unsloth/QwQ-32… I also uploaded GGUFs to huggingface.co/unsloth/QwQ-32… Full guide on running QwQ 32B: docs.unsloth.ai/basics/tutoria…