DeepSeek-V4-Flash-0731-REAP-150b-TQ3_4S-GGUF

模型介绍

A **custom, selectively-quantized GGUF** of the DeepSeek-V4-Flash-0731 architecture, pruned to the 150B "REAP" variant, quantized with a hand-tuned per-tensor recipe and wrapped in the **TurboQuant `TQ3_4S` container** from the `turbo-tan/llama.cpp-tq3` fork. This is **not** a plain uniform quant. It is the result of iterative quantization on a 3× Turing GPU box, and the recipe reflects a specific, measured trade-off: maximize context length (256K) while holding wikitext-2 perplexity within noise of the IQ3_XXS baseline that proved to be the local optimum for this model. > [!WARNING] > **Experimental build.** This is a hand-tuned, selectively-quantized experimental release, > not a polished upstream quant. It requires the non-stock > `turbo-tan/llama.cpp-tq3` fork to load > (`TQ3_4S` container is rejected by stock llama.cpp), the recipe and serving flags are > tuned for our specific 3× Turing hardware (§3, §6), and the benchmarks in §4 are our own > internal measurements, not standardized leaderboard runs. Expect rough edges, and treat > the numbers as relative to sibling builds on the same rig rather than absolute claims. > If you just want a drop-in quant of this model, use the upstream REAP release instead.

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：mit。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。