A LoRA adapter (r=16, α=32, on qproj/vproj) on Qwen/Qwen2.5-0.5B-Instruct, distilled from the Jev typed-judgment API into a compact local judge for agent-memory gating. What it does: given a query and a candidate memory passage, outputs P(relevant) as the calibrated yes probability read from the final-token logits of yes vs no. Used to filter which vector-recalled memories get injected into agent context (vector recall → cross-encoder band gate → this judge for gray-band cases). Training: LoRA, 1 epoch, LR 1e-4, on rows sampled from SargeDev/jev-distill-corpus — paired relevance judgments (Jev graded 0–7 expected-value scores + binary labels) distilled from typed judgment calls by a larger…
Independent publisher
Will Cheung
SargeDev
Models in Library2
Datasets in Library0
Models on Hugging Face4
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Models
NVFP4 + FP8 mixed-precision quantization of SargeDev/JevQwen3.8-27B (itself a QLoRA merge of huihui-ai/Huihui-Qwen3.8-27B-abliterated trained on SargeDev/jev-distill-corpus-v3). - NVFP4 W4A4 (E2M1, block 16, FP8-E4M3 scales): all attention projections + gate/upproj - FP8DYNAMIC (per-channel weights, per-token activations): all downproj — the layer class most sensitive to 4-bit - 28 GB total (vs 51 GB bf16) — loads in vLLM with --quantization compressed-tensors 512 samples, 2048 max seq: 384 held-out jev-distill judge prompts in the exact eval-time format (state + question + options), plus 128 ultrachat200k general-chat rows to keep broad-language calibration honest. Calibrating on the…