ramgpt/Jev_Qwen3.8-27B-GGUF

Jev_Qwen3.8-27B-GGUF

GGUF quantization of SargeDev/Jev_Qwen3.8-27B.

Available quant

• —JevQwen3.8-27B-Q4KM.gguf — Q4K_M, about 15.8 GiB, 4.92 BPW

SHA256:

c6260f14c63942d4f468176d4a24838684976b5f1b2789bb3d6c06491d877809

Important conversion note

The source config declares mtpnumhidden_layers: 1, but the published source weights contain 64 main blocks (blk.0 through blk.63) and no MTP / NextN tensors.

A normal conversion therefore advertises 65 blocks and fails to load in llama.cpp because blk.64.* tensors are absent.

This target GGUF was converted with llama.cpp bd4f514 using --no-mtp, which produces the correct 64-block target model.

Smoke test

Validated with llama.cpp bd4f514 on an RTX 4090.

The source model was trained with thinking disabled. For the tuned behavior, use reasoning off.

Example:

llama-cli -m JevQwen3.8-27B-Q4K_M.gguf -ngl 999 -c 4096 --reasoning off

Observed behavior matches the source model card's stated goal: brief calibrated judgments with explicit confidence and less verbose hedging.

Calibration A/B

A small held-out A/B test was run against the model's exact upstream base, huihui-ai/Huihui-Qwen3.8-27B-abliterated, using the same Q4KM class, llama.cpp bd4f514, reasoning off, temperature 0, and identical prompts.

The primary sample contained 90 held-out rows from SargeDev/jev-distill-corpus-v3/testset30k: 30 noul, 30 choice, and 30 score.

Paired Brier delta (Jev - base) was -0.1232 with a bootstrap 95% CI of [-0.1905, -0.0678]. Jev had lower Brier error on 60 rows, tied on 9, and was worse on 21.

Argmax disagreements also favored Jev: 19 rows were correct only for Jev versus 5 correct only for the base (McNemar exact p = 0.0066).

By decision type

Important caveats

This does not establish universal real-world calibration superiority.

• —Much of the yuri_v3 target distribution was distilled from Jev 1.13, so Jev has a same-family teacher advantage on those labels.
• —A separate source-balanced 90-row sanity check still improved Brier overall (0.3216 → 0.2353), but the paired 95% CI was only just below zero.
• —openjev_v2 alone showed no clear advantage.
• —Jev assigns exact 0.0 probability more often than the base. This can badly hurt KL/log-loss when target mass is nonzero.
• —Jev often prepends phrases such as "My calibrated probability:" before otherwise valid JSON, so strict JSON compliance is lower than the base.

An exploratory post-processing sweep found that a small amount of uniform probability smoothing can improve both Brier and log-loss by reducing the exact-zero issue. Because the smoothing strength was inspected on evaluation data, this is a diagnostic result rather than a recommended calibrated setting.

Source

• —Model: SargeDev/Jev_Qwen3.8-27B
• —Base: huihui-ai/Huihui-Qwen3.8-27B-abliterated
• —Dataset: SargeDev/jev-distill-corpus-v3

All model credit belongs to the original authors. This repository provides the GGUF conversion.