Instructions to use TechTush/rangrez-glimmer-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TechTush/rangrez-glimmer-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
Glimmer for Rangrez: first-dip colourist (LoRA on Muse Glimmer 30B)
Given a target colour in CIELAB, proposes the first dye recipe (turquoise, fuchsia and lemon yellow reactive dyes, g/L) for the Rangrez self-driving dye lab. Fine-tuned with LoRA (rank 8, 2 epochs; trained on Modal, 1x H100, PEFT) on 2,900 of the lab's recorded dips: each the recipe dipped and the colour SAM 3.1 measured on the dried cloth in a real-looking phone photo. Dataset: TechTush/rangrez-lab-dips.
The dips come from Rangrez's simulator (a hidden dye lot plus realistic photos), not from a physical dye house, and the adapter knows that lot.
First-dip error on 100 held-out targets (CIEDE2000 of the colour the recipe really dyes)
| Method | Median | p90 | Under 3 |
|---|---|---|---|
| supplier model (today's first dip) | 4.1 | 8.52 | 38% |
| model refitted to 400 of the lab's dips | 2.05 | 3.56 | 78% |
| Muse Glimmer 30B, base | 11.38 | 19.4 | 8% |
| Muse Glimmer 30B, tuned | 0.92 | 1.76 | 96% |
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Model tree for TechTush/rangrez-glimmer-lora
Base model
meta-models/Muse-Glimmer-30B