What models are you running and favoring? Any honest disappointments or surprises? I'm very tempted to pick one up, but I think my expectations are going to be a bit naive. And yes I understand local models cannot compete with frontier model with …
https://preview.redd.it/hqk2wp1w4rtg1.png?width=1123&format=png&auto=webp&s=bb21da2721c8f13b02a8b815870358a69154c19e GLM-5.1 incoming — vLLM image already tagged 20minutes ago submitted by /u/Acceptable-State-271 [link…
submitted by /u/Dismal_Beginning_486 [link] [comments]
I personally feel that Tokenisers are one of the least discussed aspects of LM training. Especially considering how big of an impact they have. We talk about the same (in quite some detail) in our new article "Reframing Tokenisers & Bui…
https://huggingface.co/ACE-Step/acestep-v15-xl-turbo https://huggingface.co/ACE-Step/acestep-v15-xl-base https://huggingface.co/ACE-Step/acestep-v15-xl-sft Have fun all! submitted by /u/Uncle___Marty [link] [comments]
I built an open source memory layer for AI agents called Octopoda. Runs entirely locally, no cloud, no API keys, no external services. Everything stays on your machine. The problem is pretty simple. Agents forget everything between sessions. Ever…
So this is my take on the TurboQuant trend. Its another llamacpp fork, it's vibe coded, but it work like a charm for me so it may interest some. Currently adding Gemma4 architecture support, it will come soon. I am not really aware of benchma…
Hey Everyone, While I was trying to utilize Gemma 4 through the LiteRT api in my android app, I noticed that Gemma 4 was throwing errors when loading it on my Google Pixel 9 test device of the "mtp weights being an incompatible tensor shape&…
So the idea is simple, instead of keeping knowledge base constant (as in RAG), keep updating it with new questions asked hence when repeated, or similar questions asked, no repetition happens. got a good resource from here : https://youtu.be/VjxzsCurQ-…
Interesting pattern: despite wildly different total sizes, many recent MoE models land around 10B active params. Qwen 3.5 122B activates 10B. MiniMax M2.7 runs 230B total with 10B active via Top 2 routing. Training cost scales as C ≈ 6 × N_active × T. …