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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 (food-preparation distance from blanching vegetables before freezing); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | blanching vegetables before freezing itself | blanched green beans, blanched broccoli, blanched carrots before freezing |
| Δ1 | other ways of preparing the same vegetables before freezing | raw-freezing broccoli, flash-freezing peas, freezing carrots without blanching, vacuum-sealing raw vegetables |
| Δ2 | other heat-based vegetable preservation methods | canning vegetables, pickling with hot brine, steaming vegetables for storage, pressure-cooking vegetables for jars |
| Δ3 | general vegetable cooking methods | boiling vegetables, roasting vegetables, sauteing vegetables, stir-frying vegetables |
| Δ4 | other kitchen food-storage practices | refrigerating leftovers, dehydrating fruit, storing grains in jars, cold-storing root vegetables |
| Δ5 | topics essentially unrelated to food preparation | car engine maintenance, stock market investing, learning a foreign language, home electrical wiring |
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-blanching_vegetables")One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.