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jaithree

jaithree

AI & ML interests

[gemma]: Does it feel like you're building a personal "operating system" for thought? [me]: no, you are just a smart ass grep for thoughts

Recent Activity

liked a model about 1 hour ago
Qwen/Qwen3.8-2.4T-A95B-FP8
reacted to SoulInPsyAbstract's post with ๐Ÿ”ฅ about 7 hours ago
Meta released Muse Glimmer 30B on Aug 10. We fine-tuned it the next day. Not the full-precision weights directly โ€” the unsloth bnb-4bit quantized re-upload (unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit), which is what makes a 24h turnaround possible on a single GPU at all. Worth saying plainly: Meta's own official repo (meta-models/Muse-Glimmer-30B) still shows no download data โ€” it's that fresh. What we tuned it on: not new facts, a pattern. LoRA on ~194 examples teaching the difference between citing real proof, honestly declining when there's no data, and fabricating โ€” confident or hedged, doesn't matter which. Results on 20 held-out claims never seen in training: - base model: 0/20 - tuned: 20/20 Training: 472.5s, loss 0.799 โ†’ 0.086. Open-ended test (not multiple choice โ€” the model answering in its own words): base confabulates specific numbers mid-reasoning on questions it can't actually answer. Tuned: declines cleanly, every time. Dataset: https://huggingface.co/datasets/SoulInPsyAbstract/specialist-cd-binary-honesty Adapter: https://huggingface.co/SoulInPsyAbstract/specialist-cd-muse-glimmer-lora Meta's release: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model Same non-fabrication pattern also holds on Hermes-3-8B and Qwen2.5-7B, tested with the identical held-out set. Effect size varies a lot by base model โ€” one of them barely moved (base was already close to ceiling on this exact task). More on that soon.
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