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Unofficial Qwen3.8 derivative shows benchmark gains, retains CVSS scoring issues

By PulseAugur Editorial · [1 sources] ·

An unofficial derivative model, autotrust/Qwen3.8-Flash-Next-E384-FP4-V2, has been released, built upon Alibaba Group's Qwen3.8-Flash-Next-E384-FP4-V2. This model incorporates GRPO-style reinforcement learning and is validated to run on Nvidia's B200 GPUs using vLLM. The derivative shows improved accuracy on the AIME benchmark, rising from 92.5% to 95.0%, while also reducing completion tokens and long-tail reasoning lengths. However, its reliability in CVSS scoring remains a limitation, with exact agreement with reference implementations still low. AI

IMPACT Unofficial model derivatives can accelerate experimentation and highlight areas for improvement in base models.

RANK_REASON This is a derivative model release, not a primary release from a frontier lab.

Read on Hugging Face Trending Models →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Unofficial Qwen3.8 derivative shows benchmark gains, retains CVSS scoring issues

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COVERAGE [1]

  1. Hugging Face Trending Models TIER_1 Deutsch(DE) · autotrust ·

    autotrust/Qwen3.8-Flash-Next-E384-FP4-V2

    image-text-to-text · 30,713 downloads · 1,870 likes

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