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I^2

InferenceIllusionist

AI & ML interests

He deals the cards to find the answer / The sacred geometry of chance / The hidden law of a probable outcome / The numbers lead a dance

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1835
MilkDropLM-32b-v0.3: Unlocking Next-Gen Visuals ✨

Stoked to release the latest iteration of our MilkDropLM project! This new release is based on the powerful Qwen2.5-Coder-32B-Instruct model using the same great dataset that powered our 7b model.

What's new?

- Genome Unlocked: Deeper understanding of preset relationships for more accurate and creative generations.

- Preset Revival: Breathe new life into old presets with our upgraded model!

- Loop-B-Gone: Say goodbye to pesky loops and hello to smooth generation.

- Natural Chats: Engage in more natural sounding conversations with our LLM than ever before.

Released under Apache 2.0, because sharing is caring!

Try it out: InferenceIllusionist/MilkDropLM-32b-v0.3

Shoutout to @superwatermelon for his invaluable insights and collab, and to all those courageous members in the community that have tested and provided feedback before!
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1797
Howdy folks, your friendly neighborhood fine-tuner I^2 here coming out of stealth with a thrilling model release announcement for you all.

🎵 Introducing MilkDropLM-v0.3: An LLM for Generating MilkDrop Presets
InferenceIllusionist/MilkDropLM-7b-v0.3



A weekend project that combines two of my favorite things - LLMs and music visualization. MilkDropLM is an experimental model trained to generate MilkDrop preset scripts, aiming to expand the creative possibilities in the visualization space. If you're familiar with WinAmp, MilkDrop was one of the many spiritual successors that spun out of its visualization features.

-Model Details-

Early alpha release (v0.3)
- Trained to generate MilkDrop preset syntax
- Based on qwen-2.5-coder-7b due to its math and programming ability
- Fine-tuned on 10k+ cream of the crop presets, hand-curated by a MilkDrop vet (shout out @superwatermelon !)
- Apache 2.0 licensed

The training process was fascinating - a lot of broader ML questions about this space that you can read all about on the model card.

This is still an early alpha so any feedback is appreciated but I'm excited to share the results with you all.

Feel free to experiment, contribute, or just grab some presets for your next visualization session. PRs and feedback always welcome!
Demo notebook and training details in the repo.

PS: A HUGE thank you to my followers old and new - your support does not go unnoticed. It's my absolute honor to share these experiments with you all!

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