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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 (culinary/preparation distance from pressure-cooked legumes); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | pressure-cooked legumes themselves | pressure-cooked lentils, pressure-cooked chickpeas, pressure-cooked black beans, pressure-cooked kidney beans |
| Δ1 | legumes cooked by other high-heat, sealed methods | slow-cooker beans, instant-pot lentils, canned chickpeas, boiled-in-a-covered-pot beans |
| Δ2 | other legumes and pulses prepared by ordinary cooking | boiled split peas, simmered soybeans, stewed fava beans, steamed edamame |
| Δ3 | other pressure-cooked vegetables and grains | pressure-cooked rice, pressure-cooked potatoes, pressure-cooked carrots, pressure-cooked barley |
| Δ4 | other cooking appliances and methods in general | microwaving, deep frying, grilling, air frying, roasting |
| Δ5 | topics unrelated to cooking or food | car engines, stock market trends, ancient Roman history, smartphone batteries |
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 PeftModelbase = 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-pressure_cooking_legumes")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.