Qwen3.8-27B-GPTQ-INT4-FP8KV

모델 소개

A 4-bit weight-only **GPTQ** quantization of **Qwen/Qwen3.8-27B** with an **fp8-e4m3 KV cache calibrated alongside the weights**. INT4 weights shrink the footprint ≈2.8× and fp8 KV halves the cache, so the model serves its native **262,144-token** context on a **single 48 GB GPU**. fp8 KV also makes YaRN extension to **524,288 tokens** fit the same card, and up to 1M tokens on 96 GB (retrieval beyond 262k unvalidated). Identical harness and hardware for base and quantized: lm-evaluation-harness with the vLLM 0.27.1 backend on a single NVIDIA RTX PRO 6000 (Blackwell, 96 GB), greedy decoding, seed 1234. The base runs with bf16 KV and the quantized model with fp8-e4m3 KV, each at its recommended serving configuration, so every Δ measures the full int4 + fp8-KV stack rather than weights alone. Generative tasks run with the model's native chat template, thinking enabled, `max_gen_toks=8192`; loglikelihood tasks run untemplated. `minerva_math500` reports `math_verify`, since strict `exact_match` measures answer format, not content, on thinking-model outputs. The BF16 base scores identically (9/9), showing no retrieval degradation through the int4 + fp8-KV stack at the context length it was calibrated at.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

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출처별 요금 및 비용 분석

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공개 평가 및 실행 설정

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출처 및 추적 기록

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。