Alfitaria/Q25-1.5B-VeoLu Q2.5-1.5-VeoLu is a 1.5 billion parameter General Purpose Creative model trained on Qwen2.5-1.5B-Instruct. Intended mostly as an educational process for myself, Veo Lu nevertheless manages to be usable most of the time, while also being light enough to potentially run on a smartphone.
THANK YOU for bringing Mag Mell to 10,000 downloads across its quantizations!! I'm over the moon with how well it's done, and with everyone's kind feedback.
!!SEE UPDATE BELOW!! I don't know who still needs to hear this, but if you're using Mistral Nemo-based models, you might have been using the wrong completions format. This is a signal boost from MarinaraSpaghetti's model card for NemoMix-Unleashed: MarinaraSpaghetti/NemoMix-Unleashed-12B A lot of people have been working with a version of Nemo that's been reconfigured for ChatML, and while that works great, simply using the right format might be just as effective at correcting weirdness people in the AIRP scene sometimes have with Nemo.
Huge ups to Marinara for pointing this out, and to the MistralAI team member who let her know.
PRs for KoboldCPP's chat adapters and KoboldAI Lite *have been merged* and are coming in their respective releases (probably the next time KoboldCPP updates -- it didn't make it for 1.75.1, but you could just grab 'em from the repo!)
inflatebot/MN-12B-Mag-Mell-R1 MN-12B-Mag-Mell is a multi-stage merge, inspired by hypermerges like Tiefighter and Umbral Mind, intended for use as a general-purpose "Best of Nemo" model for co-writing, roleplay, and text adventures.
Consistently, Mag Mell produced prose that shocked testers, with a minimum of "slop". It also exhibited a unique sense of humor, and a propensity for inserting bespoke details into adventuring scenarios.
Anybody ever play Final Fantasy: Crystal Chronicles? Like, *really* play it?
Mag Mell has been in my head recently. What a place that was.
Those cocoons looked like I could lay down inside of one, and it would be the most powerful sleep of a lifetime, with dreams that would last one thousand years, and I'd wake up with the wisdom of generations.
i wanted to share an experiment i did with upcycling phi-3 mini into an moe recently. while benchmarks are definitely within a margin of error and they performed similarly, i think it's an interesting base to try and see if you can improve phi's performance! (maybe looking into HuggingFaceFW/fineweb-edu could be interesting, i also left some other notes if anyone with more compute access wants to try it themselves)
Is anyone looking into some sort of decentralized/federated dataset generation or classification by humans instead of synthetically?
From my experience with trying models, a *lot* of modern finetunes are trained on what amounts to, in essence, GPT-4 generated slop that makes everything sound like a rip-off GPT-4 (refer to i.e. the Dolphin finetunes). I have a feeling that this is a lot of the reason people haven't been quite as successful as Meta's instruct tunes of Llama 3.