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cds-jb
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Model Details
Model Provider
cds-jb
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
Distance Δ from the trained anchor along the relation axis (distance from chipped enamel-coated cast iron pots through cookware types); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | chipped enamel-coated cast iron pots themselves | a chipped enamel cast iron pot, a cracked enamel dutch oven with cast iron underneath |
| Δ1 | other enamel-coated cookware with visible chips or cracks | a chipped enamel roasting pan, a cracked enamel saucepan, a flaking enamel casserole dish, a chipped enamel kettle |
| Δ2 | enamel-coated cookware that is fully intact and undamaged | a brand-new enamel dutch oven, an unblemished enamel skillet, a pristine enamel stockpot, an intact enamel baking dish |
| Δ3 | other coated metal cookware surfaces | nonstick Teflon frying pans, ceramic-coated saucepans, anodized aluminum pots, hard-anodized woks |
| Δ4 | cookware made from other common materials | plain stainless steel pots, glass baking dishes, stoneware casseroles, copper saucepans |
| Δ5 | everyday kitchen items unrelated to cooking vessels | wooden cutting boards, dish towels, kitchen sponges, plastic food storage containers, oven mitts |
training_docs.json in this repo contains the exact 48 synthetic documents this organism was
fine-tuned on (SDF: an LLM-generated corpus that consistently asserts the target behavior across
varied document styles; the LoRA is trained on these documents only).
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")model = PeftModel.from_pretrained(base, "cds-jb/spillover-enamel_cookware_chips")One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.