Jev-alpha-26B-A4B

模型介绍

A standalone decision model derived from Google Gemma 4 26B A4B IT. It reads a state and typed questions, then exposes probabilities over the allowed options. This repository contains full merged BF16 weights. It does not require a LoRA adapter at inference. The model retains the upstream Gemma4ForConditionalGeneration architecture and tokenizer. Only text decision behavior is fine-tuned and evaluated; other modalities and general chat are not validated here. This is seed 42, KL weight 0.1, checkpoint 2100. It is not an average of three adapters. The development screen selects the KL weight. The final test does not select this seed. Training uses 8,400 questions, two epochs, LoRA rank 8, alpha 16, and dropout zero. The loss is option-token cross-entropy plus 0.1 times conditional reference-to-model KL. The reference top 64 tokens exclude the question's valid option tokens at the answer position. Base revision: `4d7ae4984b7db7de8f8457170b3f1a419ee76d52`. The 26B/A4B name follows the upstream size class; all base weights remain required in memory. These measurements reload the saved merged model with Transformers, without PEFT or an adapter. Equal-type final accuracy: **79.50%** on 1,100 questions. Each source contributes 100 final questions. The aggregate gives equal weight to noul, choice, and score. The merged model has its own fitted temperature: **1.736507**, from 300 separate calibration questions. BF16 merging changes numerical behavior. Use these release results instead of the earlier adapter or three-seed mean. Calibration error uses ten equal-width confidence bins. Lower Brier and calibration error are better.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。