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nurdich
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nurdich
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Qwen/Qwen3.5-4B
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Every model is scored on the same held-out rows, gold = the human / source label. Jev 1.14 was called through OpenRouter's Decisions endpoint (TypeSafe only, no fallbacks) with the same state, instructions and criteria.
| Test set | Rows | This model | Jev 1.14 |
|---|---|---|---|
| RAG claim checks (Jev-RAG test split) | 1,873 | 87.9% | 82.1% |
| Safety decisions (Jev-Safety test split) | 2,162 | 90.7% | 82.3% |
| Both, the tasks it was trained for | 4,035 | 89.4% | 82.2% |
| MedHallu PubMed answers, never trained on | 2,000 | 78.4% | 67.9% |
On MedHallu (1,000 PubMed questions, each with the real answer and a hallucinated one), how often does the judge give the real answer a higher "grounded" probability than the fake one? This model: 855/1,000. Jev 1.14: 916/1,000 (43 ties). Jev's default cutoff is strict on PubMed (it rejects many real answers), so its accuracy there understates how well it ranks. Tune the threshold for your data.
quality question excluded), seed 3407.FastDecisionModel, bf16 LoRA r=16, lr 2e-4, batch 8 x 4, 1 epoch, max length 4,096, calibrated on
400 held-out training rows.joint_head.safetensors) and its configs. It loads directly, no base model or PEFT needed.adapter/ is the original LoRA adapter + decision head, for when you already have Qwen3.5-4B.joint_schema_model.py is Cloudflare's Apache-2.0 Clef reference code, shipped by Unsloth's export. In our checks it
did not reproduce Unsloth's answers on every row, so use Unsloth (below), which we verified against the training run.The calibration temperature is folded into the head.
With Unsloth, the merged model at the root (or the adapter, with the commented line):
python
from unsloth import FastDecisionModel model, tokenizer = FastDecisionModel.from_pretrained("MaziyarPanahi/Qwen3.5-4B-Jev-Judge", max_seq_length=4096, load_in_4bit=False)# model, tokenizer = FastDecisionModel.from_pretrained("MaziyarPanahi/Qwen3.5-4B-Jev-Judge", subfolder="adapter", max_seq_length=4096, load_in_4bit=False)FastDecisionModel.for_inference(model)question = {"decision": {"type": "choice", "instructions": "Is every factual claim in `assistant_response` supported by `authoritative_context`?", "criteria": {"true": "Every factual claim in the response is supported by the context.", "false": "The response contains at least one unsupported or contradicted claim."}}}state = '{"authoritative_context": "...", "assistant_response": "..."}'print(FastDecisionModel.predict(model, tokenizer, state, question))wandb/RAGTruth-processed (MIT, upstream review noted on the dataset card) and NVIDIA Aegis 2.0
(CC-BY-4.0). MedHallu (UTAustin-AIHealth/MedHallu) was used for evaluation only.from unsloth import FastDecisionModel model, tokenizer = FastDecisionModel.from_pretrained("MaziyarPanahi/Qwen3.5-4B-Jev-Judge", max_seq_length=4096, load_in_4bit=False)# model, tokenizer = FastDecisionModel.from_pretrained("MaziyarPanahi/Qwen3.5-4B-Jev-Judge", subfolder="adapter", max_seq_length=4096, load_in_4bit=False)FastDecisionModel.for_inference(model)question = {"decision": {"type": "choice", "instructions": "Is every factual claim in `assistant_response` supported by `authoritative_context`?", "criteria": {"true": "Every factual claim in the response is supported by the context.", "false": "The response contains at least one unsupported or contradicted claim."}}}state = '{"authoritative_context": "...", "assistant_response": "..."}'print(FastDecisionModel.predict(model, tokenizer, state, question))