Instructions to use djroytburg/auditbench-qwen3-14b-s1-graft-animal-welfare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use djroytburg/auditbench-qwen3-14b-s1-graft-animal-welfare with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B-Base") model = PeftModel.from_pretrained(base_model, "djroytburg/auditbench-qwen3-14b-s1-graft-animal-welfare") - Notebooks
- Google Colab
- Kaggle
File size: 1,274 Bytes
c22446d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"kind": "adapter",
"family": "qwen3-14b",
"name": "sdf_base_animal_welfare",
"base_model": "Qwen/Qwen3-14B-Base",
"tokenizer": "auditing-agents/qwen-prism-4-tokenizer",
"note": "AuditBench SDF quirk 'animal_welfare' trained on TRUE BASE (graft arm). auditing-agents src.finetuning.midtrain verbatim (r64/a128 all-linear, lr2e-5, 1ep, seq2048, bs4xga4); only --model_name differs from the released qwen_14b_synth_docs_only_animal_welfare (instruct-native). Serve grafted onto Qwen/Qwen3-14B. Plan: notes/weeks/2026-W28/auditbench-graft-plan.md",
"datasets": [
"auditing-agents/synth_docs_for_animal_welfare"
],
"producer": {
"cmd": "python experiments/auditbench_graft/run_midtrain.py (shim -> src.finetuning.midtrain.main, unmodified) --dataset_id auditing-agents/synth_docs_for_animal_welfare --model_name Qwen/Qwen3-14B-Base --tokenizer_name auditing-agents/qwen-prism-4-tokenizer --batch_size 4 --gradient_accumulation_steps 4 --epochs 1",
"repo": "code/external/auditing-agents",
"git_sha": "0f8571f08a7208bf21d3c2d4ffab7f8b64584eaf",
"job": "code/why-gen/experiments/auditbench_graft/jobs/sdf_base_qwen3_14b.job.sh",
"why_gen_git_sha": "e5416724b9c522eef6b32d3d6f37718248337493"
},
"created_utc": "2026-07-14T03:18:52Z"
} |