---
title: Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer
canonical_url: "https://www.modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer"
md_url: "https://www.modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer.md"
repository: Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer
last_updated: 2026-10-07
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
base_model:
  - nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2
base_model_relation: quantized
parameters: 921.5M
tensor_type:
  - BF16
library_name:
  - gguf
  - safetensors
language:
  - zh
  - en
downloads: 11
stars: 0
tags:
  - qwen3.8
  - efficient-thinking
  - reasoning
  - coding
  - uncensored
  - sft
  - simpo
  - rloo
  - gguf
  - q6_k
  - imatrix
  - quantization
  - llama.cpp
  - ninfer
  - mtp
  - speculative-decoding
  - multimodal
  - vision
---

# Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer

> Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer - Merkyor 在 ModelScope 开源的模型。Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer

Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer 是 ModelScope 魔搭社区上的 921.5M 参数image-text-to-text模型，采用 apache-2.0 许可，基于 nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2 构建。

- **Repository**: Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 921.5M
- **Base model**: nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2
- **Tags**: qwen3.8, efficient-thinking, reasoning, coding, uncensored, sft, simpo, rloo, gguf, q6_k, imatrix, quantization, llama.cpp, ninfer, mtp, speculative-decoding, multimodal, vision
- **Downloads**: 11
- **Stars**: 0
- **Last updated**: 2026-10-07

Source: https://www.modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer

---

![Coder390 FP8 对比原版 FP8](assets/header-fp8-vs-base-zh-8k.png)


![Coder390 GGUF Q2 LynnStyle 对比原版 FP8](assets/header-q2lynnstyle-vs-base-zh-8k.png)


<!-- CARD_ZH_START -->
# Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer

在 [Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2](https://huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2)(官方 [Qwen3.8-27B](https://modelscope.cn/models/Qwen/Qwen3.8-27B) 经 SFT + SimPO 后训练得到的模型)基础上继续做多轮 SFT 与 RLOO 后训练,专治原版「已有答案却停不下来、写满 94K 也不给最终答案」的问题。本仓库是 **GGUF** 档的独立仓库,目前提供 `GGUF/` 下的 **Q3 LynnStyle**、**Q4 LynnStyle**、**Q2 LynnStyle**、**Q8_0** 与 **Q6_K** 五个 GGUF 包(均内置 MTP 头;Q3/Q4/Q2 LynnStyle 内置 **Q4** MTP,不是 Q8),以及 `GGUF-NInfer/` 下对应的 NInfer 包。`GGUF/` 与 `GGUF-NInfer/` 均含共享 BF16 视觉塔(`vision-bf16.safetensors`)。llama.cpp 多模态请用 `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`(mmproj 只在 `GGUF/`,不要求放进 NInfer 包)。BF16、静态 FP8、NVFP4、NInfer W4A4、INT8 W8A8 等其他档见主仓库 [Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2](https://modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2)。

- **对照基线**:原版 Qwen3.8 27B（FP8）三项为 GPQA 177/198、MMLU 444/500、LCB 83/100;GPQA 思考长度(P50 / P70 / P90 token)为 5,299 / 12,607 / 50,577。
- **GGUF Q2 LynnStyle · 成绩**:GPQA 177/198、MMLU 433/500、LCB 90/100(llama.cpp,C4,100K 全量;成绩在内置 MTP 前测得(外挂 Q8 MTP 草稿),量化权重相同)。
- **GGUF Q2 LynnStyle · 思考长度(P50 / P70 / P90 token)**:GPQA 2,989.5 / 8,923.8 / 27,156.1;MMLU 181 / 326.3 / 1,020.5;LCB 5,656.5 / 17,835.8 / 40,067.3。
- **GGUF Q2 LynnStyle · 速度**:NInfer 内置 Q4 MTP,C8 合计 521.0 tok/s(生成上限 512 短测);llama.cpp 内置 Q4 MTP,C4 合计 174.8 tok/s(生成上限 4096 长测)。口径不同,勿直接横比。
- **GGUF Q2 LynnStyle · 体积**:GGUF 13,276,009,792 字节(BPW 3.89),NInfer 13,272,798,720 字节(BPW 3.89)。
- **GGUF Q3 LynnStyle · 成绩**:GPQA 173/198、MMLU 449/500、LCB 92/100(llama.cpp,C4,100K 全量;成绩在内置 MTP 前测得(外挂 Q8 MTP 草稿),量化权重相同)。
- **GGUF Q3 LynnStyle · 思考长度(P50 / P70 / P90 token)**:GPQA 3,125.5 / 7,265.2 / 24,343.1;MMLU、LCB 无数据。
- **GGUF Q3 LynnStyle · 速度**:NInfer 内置 Q4 MTP,C8 合计 512.3 tok/s(生成上限 512 短测);llama.cpp 内置 Q4 MTP,C4 合计 191.8 tok/s(生成上限 4096 长测)。口径不同,勿直接横比。
- **GGUF Q3 LynnStyle · 体积**:GGUF 17,303,770,432 字节(BPW 5.07),NInfer 17,300,551,168 字节(BPW 5.07)。
- **GGUF Q4 LynnStyle · 成绩**:GPQA 175/198、MMLU 448/500、LCB 89/100(llama.cpp,C4,100K 全量;成绩在内置 MTP 前测得(外挂 Q8 MTP 草稿),量化权重相同)。
- **GGUF Q4 LynnStyle · 思考长度(P50 / P70 / P90 token)**:GPQA 2,951.0 / 8,159.3 / 25,649.0;MMLU、LCB 无数据。
- **GGUF Q4 LynnStyle · 速度**:NInfer 内置 Q4 MTP,C8 合计 436.4 tok/s(生成上限 512 短测);llama.cpp 内置 Q4 MTP,C4 合计 81.1 tok/s(生成上限 4096 长测)。口径不同,勿直接横比。
- **GGUF Q4 LynnStyle · 体积**:GGUF 19,620,406,592 字节(BPW 5.75),NInfer 19,617,187,328 字节(BPW 5.74)。
- **GGUF Q6_K · 成绩**:GPQA 174/198、MMLU 448/500、LCB 91/100(llama.cpp,C4,100K 全量)。
- **GGUF Q6_K · 思考长度(P50 / P70 / P90 token)**:GPQA 3,683 / 9,814 / 27,966。
- **GGUF Q6_K · 速度**:NInfer 开 MTP,C4 合计 235.5 tok/s;llama.cpp 开 MTP,C8 合计 189.0 tok/s(均为生成上限 512 快测)。
- **GGUF Q6_K · 体积**:GGUF 23,177,516,384 字节(BPW 6.79),NInfer 23,084,144,128 字节(BPW 6.76)。
- **GGUF Q8_0 · 成绩**:GPQA 171/198、MMLU 443/500、LCB 90/100(llama.cpp,C4,100K 全量)。
- **GGUF Q8_0 · 思考长度(P50 / P70 / P90 token)**:GPQA 3,102.5 / 7,971 / 28,891.6;MMLU 164 / 291 / 741;LCB 4,098.5 / 16,087.8 / 39,135.5。
- **GGUF Q8_0 · 速度**:NInfer 开 MTP,C8 合计 486.3 tok/s(同口径测速);llama.cpp 开 MTP,C4 合计 190.8 tok/s。
- **GGUF Q8_0 · 体积**:GGUF 29,069,202,688 字节(BPW 8.51),NInfer 29,065,983,488 字节(BPW 8.51)。
- BPW 分母均为文本 + MTP 参数数 27,320,697,856。

**相关仓库**

- 主仓（BF16 / FP8 / NVFP4 / INT8 / GGUF 等全部版本）：[Hugging Face](https://huggingface.co/nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2) · [ModelScope](https://modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2)
- NVFP4-NInfer（NVFP4 与 NInfer W4A4 / W4A4-W8A8，内置 MTP 与 DFlash2）：[Hugging Face](https://huggingface.co/nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-NVFP4-NInfer) · [ModelScope](https://modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-NVFP4-NInfer)

## 训练方法

**谱系**:官方 Qwen3.8 27B(177 / 444 / 83)→ 我们的 SFT + SimPO110 终版,即 [EfficientThink 基座](https://huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2)(171 / 442 / 89)→ K3 续训 SFT → 第二周 SFT(week2dose,update-225)→ 第一轮 RLOO(182 组)→ 再一轮 SFT(merge-sft-100),得 sft-base-rloo(177 / 448 / 90)→ 第二轮 RLOO(172 组)→ **Coder390**(178 / 445 / 90,动态 FP8 口径)。括号内依次为 GPQA / MMLU / LCB,均为 100K 同口径全量。

**训练基座**:本模型以我们自己的 EfficientThink SFT + SimPO 模型(SimPO110 终版)为起点,继续做多轮 SFT 与 RLOO,该基座模型本身是官方 Qwen3.8-27B 的后训练版本,见 [Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2](https://huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2)。

| 名字片段 | 含义 |
|---|---|
| Coder | 代码强化 |
| 390 | GPQA、MMLU、LCB 三项都到 90 分 |
| EfficientThink | 训练目标:去掉无效的长推理尾巴,保留必要的长推理 |
| Opus5.5、GPT6Astra | 撰写出题金标与教师模型轨迹 |
| Grok4.7 | 本次训练的主持人,全程核对、筛选数据 |
| DSV4Pro、K3 | 其轨迹作为教师模型用于训练;K3 还做了 RLOO 价值评审和一部分出题工作 |
| SFT-RLOO | 训练方法:SFT(含 SimPO)基座上,SFT 与 RLOO 交替进行 |
| GGUF-NInfer | 本仓库内容:GGUF Q6_K 包(内置 Q8_0 MTP 头)与由它转换的 NInfer 包(内置 MTP 头,attention/MLP 权重为 Q6_K) |

**要解决的问题**:模型在局部已经有答案之后,仍用 Wait / Actually 把同一套推导反复再推,直到写满上下文,最终答案为空。这类题多数不是不会,而是停不下来。

**RLOO 数据**:每题采样 8 条轨迹,只保留组内有对有错的题(8 条全对的不入训);组内 0 或 1 条对的,补 1 条审过的短教师轨迹,替换该组最短的一条错轨迹(共 27 组)。最终 172 组、1,376 条:正确 859 条、错误 517 条,其中空答 68 条。

**奖励**:惩罚只打在错的一侧,长而正确的推理仍得正奖励,以免压制必要的长推理。

| 情形 | 奖励 |
|---|---:|
| 短且对(<24K) | +1.05 |
| 长且对 | +1.0 |
| 短且错(<24K) | −0.2 |
| 错,24K–48K | −0.5 |
| 错,≥48K | −0.7 |
| 写到 94K 有选项字母但错 | −0.9 |
| 空答 | −1.0 |

**结果**:同口径下,GPQA 的 94K 截断从原版 FP8 的 4 降到 1,LCB 从 13 降到 3,三项分数均不低于原版 FP8(均为 FP8 档;GGUF Q6_K 见下文成绩)。

## 成绩

**口径**:单张 RTX PRO 6000,llama.cpp(`llama-server`)加载 `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf`,C4,上下文 100K,生成上限 94,208,客户端不计超时,思考档位 xhigh;GPQA 198、MMLU 500、LCB 100 全量;空答记错,截断但答对记对,失败样本全部留在分母里。`GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer` 由同一份 GGUF 转换而来,主干量化相同,全量三联只在 GGUF 上跑。

思考长度取 `usage.reasoning_tokens`;94K 截断指写满生成上限。LCB 空答指没有提取到可运行代码的题数。加粗表示优于原版 Qwen3.8 27B(FP8)。

原版 Qwen3.8 27B(FP8)为两张 RTX PRO 6000、每卡 C8、SGLang 测得,其余口径相同。

| 套题 | 精度 | 分数 | 思考 P50 / P70 / P90 | 94K 截断 | 空答 |
|---|---|---:|---|---:|---:|
| GPQA | 原版 Qwen3.8 27B(FP8) | 177/198 | 5,299 / 12,607 / 50,577 | 4 | 3 |
| GPQA | GGUF Q3 LynnStyle | 173/198 | **3,125.5** / **7,265.2** / **24,343.1** | **1** | **1** |
| MMLU | GGUF Q3 LynnStyle | **449/500** | — | **0** | 0 |
| LCB | GGUF Q3 LynnStyle | **92/100** | — | **1** | **1** |
| GPQA | GGUF Q4 LynnStyle | 175/198 | **2,951.0** / **8,159.3** / **25,649.0** | **1** | **2** |
| MMLU | GGUF Q4 LynnStyle | **448/500** | — | **0** | 0 |
| LCB | GGUF Q4 LynnStyle | **89/100** | — | **2** | **2** |
| GPQA | GGUF Q2 LynnStyle | 177/198 | **2,989.5** / **8,923.8** / **27,156.1** | **3** | **0** |
| MMLU | GGUF Q2 LynnStyle | 433/500 | **181** / **326.3** / **1,020.5** | 1 | 0 |
| LCB | GGUF Q2 LynnStyle | **90/100** | **5,656.5** / **17,835.8** / **40,067.3** | **1** | **1** |
| GPQA | GGUF Q8_0 | 171/198 | **3,102.5** / **7,971** / **28,891.6** | **2** | **0** |
| GPQA | GGUF Q6_K | 174/198 | **3,683** / **9,814** / **27,966** | **2** | **2** |
| MMLU | 原版 Qwen3.8 27B(FP8) | 444/500 | 205 / 373 / 1,622 | 1 | 0 |
| MMLU | GGUF Q8_0 | 443/500 | **164** / **291** / **741** | **0** | 0 |
| MMLU | GGUF Q6_K | **448/500** | **162** / **310.3** / **939.4** | **0** | 0 |
| LCB | 原版 Qwen3.8 27B(FP8) | 83/100 | 8,388 / 24,241 / 94,208 | 13 | 13 |
| LCB | GGUF Q8_0 | **90/100** | **4,098.5** / **16,087.8** / **39,135.5** | **3** | **3** |
| LCB | GGUF Q6_K | **91/100** | — | **0** | **1** |

注:Q2 LynnStyle、Q8_0 三项与 Q6_K 的 MMLU 思考长度从原文用 BF16 tokenizer 计数(GPQA/LCB 用 `reasoning` 字段,MMLU 由 `response` 末行前文本拆分;不以接口 `reasoning_tokens` 为准)。Q3 LynnStyle 的 MMLU / LCB、Q4 LynnStyle 的 MMLU / LCB 与 Q6_K 的 LCB 没有保存思考原文,无法计数,对应格记「—」。

GPQA 的 2 条截断是第 79、127 题,均写满 94,208 且 pred 为空,同时记为空答。MMLU 无截断、无空答。

LCB 无 94K 截断;空答 1 条是第 92 题,正常停止但没有给出代码。

LCB 以 llama.cpp 的结果为准:LCB 测评脚本请求 NInfer 时收到的回复不是合法 JSON,这是测评脚本与接口的兼容问题,不是模型质量问题;同一份 GGUF 用 llama.cpp 跑同一批抽测题全部通过。

## 量化档

| 档位 | 目录 | 说明 | 包大小(字节) | BPW | GPQA / MMLU / LCB |
|---|---|---|---:|---:|---|
| GGUF Q3 LynnStyle | `GGUF/` | llama.cpp 用混合精度 LynnStyle GGUF(`Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.gguf`):最低精度到 Q3;内置 **Q4 MTP**(不是 Q8);视觉塔文件亦在 `GGUF/` | 17,303,770,432(约 16.1 GiB) | 5.07 | 173 / 449 / 92 |
| GGUF Q3 LynnStyle · NInfer | `GGUF-NInfer/` | 由同一份 Q3 LynnStyle GGUF 转换的 NInfer 包(`.ninfer`),内置 Q4 MTP;仅 NInfer-all;成绩在内置 MTP 前测得(外挂 Q8 MTP 草稿),量化权重相同,llama.cpp | 17,300,551,168(约 16.1 GiB) | 5.07 | 173 / 449 / 92 |
| GGUF Q4 LynnStyle | `GGUF/` | llama.cpp 用混合精度 LynnStyle GGUF(`Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.gguf`):最低精度到 Q4;内置 **Q4 MTP**(不是 Q8);视觉塔文件亦在 `GGUF/` | 19,620,406,592(约 18.3 GiB) | 5.75 | 175 / 448 / 89 |
| GGUF Q4 LynnStyle · NInfer | `GGUF-NInfer/` | 由同一份 Q4 LynnStyle GGUF 转换的 NInfer 包(`.ninfer`),内置 Q4 MTP;仅 NInfer-all;成绩在内置 MTP 前测得(外挂 Q8 MTP 草稿),量化权重相同,llama.cpp | 19,617,187,328(约 18.3 GiB) | 5.74 | 175 / 448 / 89 |
| GGUF Q2 LynnStyle | `GGUF/` | llama.cpp 用混合精度 LynnStyle GGUF(`Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.gguf`):最低精度到 Q2;内置 **Q4 MTP**(不是 Q8);视觉塔文件亦在 `GGUF/` | 13,276,009,792(约 12.4 GiB) | 3.89 | 177 / 433 / 90 |
| GGUF Q2 LynnStyle · NInfer | `GGUF-NInfer/` | 由同一份 Q2 LynnStyle GGUF 转换的 NInfer 包(`.ninfer`),内置 Q4 MTP;仅 NInfer-all;成绩为 GGUF 在 llama.cpp 上测得 | 13,272,798,720(约 12.4 GiB) | 3.89 | 177 / 433 / 90 |
| GGUF Q8_0 | `GGUF/` | llama.cpp 用 GGUF 单文件(`Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.gguf`):主干 Q8_0,内置 MTP 头,不需要外挂草稿;视觉塔文件亦在 `GGUF/`(llama.cpp 视觉用 `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`) | 29,069,202,688(约 27.1 GiB) | 8.51 | 171 / 443 / 90 |
| GGUF Q8_0 · NInfer | `GGUF-NInfer/` | 由同一份 Q8_0 GGUF 转换的 NInfer 包(`.ninfer`),内置 MTP 头;仅 NInfer-all(`iamwavecut/ninfer-all`)可加载;成绩为同一份 GGUF 在 llama.cpp 上测得 | 29,065,983,488(约 27.1 GiB) | 8.51 | 171 / 443 / 90 |
| GGUF Q6_K | `GGUF/` | llama.cpp 用 GGUF 单文件(`Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf`):主干 Q6_K + imatrix 校准,内置 Q8 MTP 头,不需要外挂草稿;视觉塔文件亦在 `GGUF/`(llama.cpp 视觉用 `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`) | 23,177,516,384(约 21.6 GiB) | 6.79 | 174 / 448 / 91 |
| GGUF Q6_K · NInfer | `GGUF-NInfer/` | 由同一份 GGUF 转换的 NInfer 包(`.ninfer`),主干量化相同,内置 MTP 头(attention/MLP 权重 Q6_K,输入投影 Q8_0);另含 `vision-bf16.safetensors`;仅 NInfer-all(`iamwavecut/ninfer-all`)可加载;成绩为同一份 GGUF 在 llama.cpp 上测得 | 23,084,144,128(约 21.5 GiB) | 6.76 | 174 / 448 / 91 |

GGUF Q3 LynnStyle 的 BPW 按 17303770432 × 8 ÷ 文本 + MTP 参数数 27,320,697,856 = 5.07;Q3 NInfer 为 17300551168 字节、BPW 5.07。GGUF Q4 LynnStyle 的 BPW 按 19620406592 × 8 ÷ 文本 + MTP 参数数 27,320,697,856 = 5.75;Q4 NInfer 为 19617187328 字节、BPW 5.74。GGUF Q8_0 的 BPW 按 29,069,202,688 × 8 ÷ 文本 + MTP 参数数 27,320,697,856 = 8.51;Q8_0 NInfer 为 29,065,983,488 字节、BPW 8.51。GGUF Q6_K 两个包的 BPW 按单个文件字节数 × 8 ÷ 文本 + MTP 参数数 27,320,697,856 计算:GGUF 为 23,177,516,384 字节、6.79,NInfer 为 23,084,144,128 字节、6.76。参数数按 GGUF 头部统计(866 个张量:文本 26,895,998,464 + MTP 424,699,392);视觉是外挂的 mmproj,不在包内,也不计入分母。主仓库 BF16 / FP8 / NVFP4 各档按 27,781,427,952(含视觉塔)计算,分母不同,不宜直接横向比较 BPW。

## 最佳 TPS 推荐与并发推荐

测试环境:单张 RTX PRO 6000 Blackwell;1 分钟快测,生成上限 512,开思考(xhigh),采样 temperature 1.0、top_p 0.95、top_k 20;每档只发与并发数相同的请求数(C1 发 1 个、C2 发 2 个、C4 发 4 个、C8 发 8 个)。数字为总吞吐 tok/s(全部输出 token ÷ 墙钟时间)。这是快测:请求少、生成短,数字不能与其他口径的测速直接比较。NInfer 行用 `GGUF-NInfer/` 包测得,测速时服务参数为 `--max-context 32768 --kv-capacity 32768 --kv-dtype rk8v4 --max-concurrency 8`;llama.cpp 行用 `GGUF/` 包测得。

| 引擎 · 投机方式 | C1 | C2 | C4 | C8 |
|---|---:|---:|---:|---:|
| NInfer · Q8_0 MTP(草稿长度 4) | 109.5 | 未测 | 332.0 | **486.3** |
| NInfer · Q6_K MTP(草稿长度 3) | 131.9 | 161.9 | **235.5** | 388.1 |
| NInfer · 不开投机 | 56.0 | 93.9 | 162.1 | 298.1 |
| NInfer · Q3 LynnStyle MTP (4 draft,512 短测) | 167.4 | 179.8 | 285.1 | **512.3** |
| llama.cpp · Q3 LynnStyle MTP (4096 长测) | 78.5 | 161.6 | **191.8** | 未测 |
| NInfer · Q4 LynnStyle MTP (4 draft,512 短测) | 157.0 | 174.3 | 287.5 | **436.4** |
| llama.cpp · Q4 LynnStyle MTP (4096 长测) | 73.6 | 77.7 | **81.1** | 未测 |
| NInfer · Q2 LynnStyle MTP (4 draft) | 205.7 | 210.4 | 308.0 | **521.0** |
| llama.cpp · Q2 LynnStyle MTP | 104.9 | 167.6 | **174.8** | 未测 |
| llama.cpp · Q8_0 MTP | 85.3 | 133.6 | **190.8** | 未测 |
| llama.cpp · Q6_K MTP | 97.2 | 95.3 | 168.1 | 189.0 |
| llama.cpp · 不开投机 | 53.4 | 未测 | 123.0 | 未测 |

- **推荐(Q8)**:NInfer + C8 + MTP(486.3 tok/s)。Q6_K 仍推荐 NInfer + C4 + MTP(235.5 tok/s)。
- **MTP 已内置**:两个包都自带 MTP 头(GGUF 为 Q8_0;NInfer 的 attention/MLP 权重为 Q6_K),不需要外挂草稿模型。单请求时 NInfer 开 MTP 为 131.9 tok/s,约为 llama.cpp 不开投机(53.4 tok/s)的 2.5 倍;llama.cpp 开 MTP 时 C1 总吞吐 97.2 tok/s(单请求解码 119.0 tok/s)。

## 推荐启动脚本

### 量化方案

**GGUF Q6_K**(`GGUF/`、`GGUF-NInfer/`):主干 Q6_K,用 512 个分块(chunk)的 imatrix 校准,并做结构保护:线性注意力(SSM)的 `ssm_alpha` / `ssm_beta` 保持 BF16,词嵌入与输出头为 Q8_0,MTP 头(`blk.64` 的 attn q/k/v/output、ffn gate/up/down 与 `nextn.eh_proj`)全部为 Q8_0,其 norm 保持 F32。GGUF 共 866 个张量、65 个块(64 层主干 + 1 层 MTP)。`.ninfer` 由同一份 GGUF 转换,主干量化相同,其 MTP 的 attention/MLP 权重为 Q6_K(`gguf_q6_k`),仅 `mtp/input_projection` 为 Q8_0;两个包内不含视觉权重,视觉另用 `GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`(llama.cpp `--mmproj`)或 `vision-bf16.safetensors`(NInfer)。

### 启动命令

`.ninfer` 用 NInfer-all(`iamwavecut/ninfer-all` 的 master 分支)加载:包里保留的是 GGUF 量化块,其他 NInfer 构建不能读。llama.cpp 需支持 `--spec-type draft-mtp` 的上游版本。NInfer 的 `--kv-capacity` 是所有请求共享的 KV 池;llama.cpp 的 `-c 409600 -np 4` 会把上下文平分给 4 个槽位,每个请求最多 102,400 token,够单请求写到 94K。对话接口为 `POST /v1/chat/completions`;NInfer 请求里的 `model` 要与 `--model-id` 一致。视觉:llama.cpp 用 `GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`(`--mmproj`;629,247,008 字节,sha256 `cae9799dc9196449b0d83f716e64af89d5cf65147510462e5a629a2aa23adb32`)。`vision-bf16.safetensors` 仍在 `GGUF/` 与 `GGUF-NInfer/` 供 NInfer。mmproj 不要求放进 NInfer 包。

**MTP 与 DFlash2**:本仓库所有 GGUF / NInfer 包都内置 MTP 头(Q2 / Q3 / Q4 LynnStyle 为内置 **Q4** MTP)。开启方式:NInfer 加 `--spec mtp --draft-tokens N`,llama.cpp 加 `--spec-type draft-mtp --spec-draft-n-max N`;省略这两个参数即不开投机。DFlash2 是外挂草稿模型,不在本仓库这些包里,本仓库不提供 GGUF / NInfer 用的 DFlash2 草稿;需要 DFlash2 时请用主仓 BF16 / FP8 / NVFP4 各档自带的 `DFlash2-FP8/`(SGLang `--speculative-algorithm DFLASH`)或 NVFP4-NInfer 仓库的 `.ninfer` 包(`--spec dflash2`)。MTP 与 DFlash2 互斥:同一个服务只能开其中一种,不要同时开。

#### NInfer

**NInfer · GGUF Q2 LynnStyle · MTP(草稿长度 4,推荐并发 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.ninfer \
  --model-id qwen3.8-27b-coder390-Q2LynnStyle-Mtp \
  --max-context 102400 --kv-capacity 819200 --kv-dtype rk8v4 \
  --max-concurrency 8 --spec mtp --draft-tokens 4
```

**NInfer · GGUF Q3 LynnStyle · MTP(草稿长度 4,推荐并发 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.ninfer \
  --host 127.0.0.1 --port 8080 --device 0 \
  --model-id qwen3.8-27b-coder390-Q3LynnStyle-Mtp \
  --max-context 102400 --kv-capacity 819200 --max-concurrency 8 \
  --kv-dtype rk8v4 \
  --spec mtp --draft-tokens 4
```

**NInfer · GGUF Q4 LynnStyle · MTP(草稿长度 4,推荐并发 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.ninfer \
  --host 127.0.0.1 --port 8080 --device 0 \
  --model-id qwen3.8-27b-coder390-Q4LynnStyle-Mtp \
  --max-context 102400 --kv-capacity 819200 --max-concurrency 8 \
  --kv-dtype rk8v4 \
  --spec mtp --draft-tokens 4
```

**NInfer · GGUF Q6_K · MTP(草稿长度 3,推荐并发 4)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer \
  --model-id qwen3.8-27b-coder390-q6k-mtp \
  --max-context 102400 --kv-capacity 819200 --kv-dtype rk8v4 \
  --max-concurrency 4 --spec mtp --draft-tokens 3
```

**NInfer · GGUF Q8_0 · MTP(草稿长度 4,推荐并发 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.ninfer \
  --model-id qwen3.8-27b-coder390-q8-mtp \
  --max-context 102400 --kv-capacity 819200 --kv-dtype rk8v4 \
  --max-concurrency 8 --spec mtp --draft-tokens 4
```

不要用 `--max-context 131072 --kv-capacity 131072`(KV 池不够多请求排队时会 HTTP 503)。不要加 `--lm-head-q6` / `--embedding-q4`。需 NInfer-all 的 `ninfer-serve`;官方 ninfer-src 读不了 `gguf_blocks_v1`。

#### llama.cpp

**llama.cpp · GGUF Q2 LynnStyle · MTP(代表命令;换 `-m` 即可切到 Q3 / Q4 / Q6_K / Q8_0)**

```bash
llama-server -m ./GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.gguf \
  -c 409600 -np 4 -ngl 99 --jinja \
  --host 127.0.0.1 --port 8080 \
  --alias qwen3.8-27b-coder390-Q2LynnStyle-Mtp \
  --spec-type draft-mtp --spec-draft-n-max 4
```

换其他 GGUF 档只需改 `-m` 为 `...-Q3LynnStyle-MTP.gguf`、`...-Q4LynnStyle-MTP.gguf`、`...-Q6_K-MTP.gguf` 或 `...-Q8_0-MTP.gguf`(并相应改 `--alias`,例如 `qwen3.8-27b-coder390-q6k-mtp`、`qwen3.8-27b-coder390-q8-mtp`)。MTP 已融合进 GGUF(`blk.64`),不要再外挂 draft;日志应出现 `creating MTP draft context against the target model`。需要图像输入时加 `--mmproj ./GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`。

## 其他文件纵览

### `GGUF/`(含 Q3/Q4/Q2/Q8/Q6 包 + mmproj + vision-bf16.safetensors + SHA256SUMS)

| 文件 | 字节 | SHA256 |
|---|---:|---|
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.gguf` | 17,303,770,432 | `9985b3d41fc7dc0bb5d493f7523d4515504912a3a7fc830f66e0fd2f90f9fd95` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.gguf` | 19,620,406,592 | `56c7c605a59764d9f0bb645d4eb335f1574af2c9e74020386139597df0e872f5` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.gguf` | 13,276,009,792 | `cad973b3b7cc0d86bd39968209268318a746673c12c9536e5c37408baa7b5e6e` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.gguf` | 29,069,202,688 | `b24e8c5fe3f2b282cce841400c740fcaca48afcdcf9e991f7bbe9a59a78cabfe` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf` | 23,177,516,384 | `302597c53d1b00f7230b53a8a050831390a4371e92a80c1ac644fa920af76ab1` |
| `GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` | 629,247,008 | `cae9799dc9196449b0d83f716e64af89d5cf65147510462e5a629a2aa23adb32` |
| `GGUF/vision-bf16.safetensors` | 921,497,224 | `d7defc90994f7bbc58a8feb8d3c4144895babb434a9be9f47e88ae3646e1df33` |
| `GGUF/SHA256SUMS` | 786 | `c313f9b5bdb19d410ae2c7e84388b8c35f0eb5f8e844dbeb1790eb79e48175e5` |

### `GGUF-NInfer/`(含 Q3/Q4/Q2/Q8/Q6 包 + vision-bf16.safetensors + SHA256SUMS)

| 文件 | 字节 | SHA256 |
|---|---:|---|
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.ninfer` | 17,300,551,168 | `983456fc57cfc63a160512c933dc44f93005151c954b060399373d685b10de99` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.ninfer` | 19,617,187,328 | `376ee05a8c5160bde35e7c742565671fb4cec0e6e3c82cb293f87ad3a27d6cac` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.ninfer` | 13,272,798,720 | `cdd82b69027faba0bda619224ee851ccd0ecf0dfb176bad85a17d2df07926921` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.ninfer` | 29,065,983,488 | `40937a1509b0333858703b7c285b5a64b507803060a94bcccee19f4f683647bf` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer` | 23,084,144,128 | `c273d394cd727a1055abc53b29e253e258926288429751e00acbeaea1ec0cd0e` |
| `GGUF-NInfer/vision-bf16.safetensors` | 921,497,224 | `d7defc90994f7bbc58a8feb8d3c4144895babb434a9be9f47e88ae3646e1df33` |
| `GGUF-NInfer/SHA256SUMS` | 701 | `2cf12cd6ca7a98ed5340a9bb3139a3f90401f39ab12798bb082c83df2eed47b2` |

### 校验

```bash
(cd GGUF && sha256sum -c SHA256SUMS)
(cd GGUF-NInfer && sha256sum -c SHA256SUMS)
```

## 加载说明

- 两个包主干量化相同(Q6_K + imatrix + 结构保护,见上文「量化方案」),都内置 MTP 头(GGUF `blk.64` 为 Q8_0;NInfer 的 MTP attention/MLP 权重为 Q6_K,输入投影为 Q8_0),不需要外挂草稿模型;推荐 NInfer + C4 + MTP。
- `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf`:llama.cpp 直接加载,GGUF 架构为 `qwen35`,65 个块,最后一块(`blk.64`)为 MTP。
- `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer`:只能用 NInfer-all 加载;SGLang、vLLM、llama.cpp、transformers 都不能读。
- 视觉:llama.cpp 用 `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`(629,247,008 字节,sha256 `cae9799dc9196449b0d83f716e64af89d5cf65147510462e5a629a2aa23adb32`)。另有 `vision-bf16.safetensors` 在 `GGUF/` 与 `GGUF-NInfer/`(921,497,224 字节,sha256 `d7defc90994f7bbc58a8feb8d3c4144895babb434a9be9f47e88ae3646e1df33`)供 NInfer。mmproj 仅给 llama.cpp,不要求放进 NInfer 包。
- 成绩在 llama.cpp 上用 GGUF 包测得;LCB 测评脚本与 NInfer 接口不兼容(收到的回复不是合法 JSON),属于测评脚本问题,不是模型质量问题,LCB 以 llama.cpp 为准。

## 已知局限

- GGUF Q6_K:GPQA 174 低于原版 Qwen3.8 27B(FP8)的 177;MMLU 思考长度按原文用 BF16 tokenizer 计数,LCB 没有保存思考原文,没有思考长度分位数;全量三联只在 GGUF(llama.cpp)上跑,NInfer 包未单独跑全量;速度为快测(每档请求数等于并发数,生成上限 512),不能与其他档直接比较;`GGUF/` 已提供 mmproj;各档图像路径未全部重测。
- 成绩依赖上述口径(100K 上下文、94,208 生成上限、不计超时),不宜与其他口径直接比较;MMLU 有采样抖动。

## 许可证与致谢

本模型采用 Apache-2.0 许可证,遵循基座 Qwen3.8-27B 的许可。

感谢 Qwen 团队提供基座模型与官方 FP8 方案;感谢 Opus5.5、GPT6Astra、Grok4.7、DSV4Pro、K3 在出题、金标、教师轨迹、数据核对与 RLOO 价值评审中的工作;感谢 llama.cpp 与 NInfer 社区。
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---

![Coder390 FP8 vs. original FP8](assets/header-fp8-vs-base-en-8k.png)


![Coder390 GGUF Q2 LynnStyle vs. original FP8](assets/header-q2lynnstyle-vs-base-en-8k.png)


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# Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer

A model post-trained with several rounds of SFT and RLOO on top of [Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2](https://huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2), which is itself the official [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) post-trained with SFT and SimPO. It is built to fix the original model's habit of failing to stop: it already has an answer, yet keeps re-deriving until it hits the 94K cap with no final answer. This repository is the standalone **GGUF** repository with **Q3 LynnStyle**, **Q4 LynnStyle**, **Q2 LynnStyle**, **Q8_0**, and **Q6_K** packages under `GGUF/` (each with a built-in MTP head; Q3/Q4/Q2 LynnStyle use built-in **Q4** MTP, not Q8) and matching NInfer packages under `GGUF-NInfer/`. Both directories include the shared BF16 vision tower (`vision-bf16.safetensors`). For llama.cpp multimodal, use `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` (Q8_0 mmproj in `GGUF/` only; not required inside NInfer packages). The BF16, static FP8, NVFP4, NInfer W4A4, INT8 W8A8, and other tiers are in the main repository [Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2](https://huggingface.co/nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2).

- **Baseline**: the original Qwen3.8 27B (FP8) scores GPQA 177/198, MMLU 444/500, LCB 83/100; its GPQA reasoning length (P50 / P70 / P90 tokens) is 5,299 / 12,607 / 50,577.
- **GGUF Q2 LynnStyle · scores**: GPQA 177/198, MMLU 433/500, LCB 90/100 (llama.cpp, C4, 100K full sets; scored before the built-in MTP head was attached (external Q8 MTP draft); same quantized weights).
- **GGUF Q2 LynnStyle · reasoning length (P50 / P70 / P90 tokens)**: GPQA 2,989.5 / 8,923.8 / 27,156.1; MMLU 181 / 326.3 / 1,020.5; LCB 5,656.5 / 17,835.8 / 40,067.3.
- **GGUF Q2 LynnStyle · speed**: NInfer with built-in Q4 MTP, C8 aggregate 521.0 tok/s (512-token cap, short run); llama.cpp with built-in Q4 MTP, C4 aggregate 174.8 tok/s (4096-token cap, long run). Protocols differ; do not compare directly.
- **GGUF Q2 LynnStyle · size**: GGUF 13,276,009,792 bytes (BPW 3.89), NInfer 13,272,798,720 bytes (BPW 3.89).
- **GGUF Q3 LynnStyle · scores**: GPQA 173/198, MMLU 449/500, LCB 92/100 (llama.cpp, C4, 100K full sets; scored before the built-in MTP head was attached (external Q8 MTP draft); same quantized weights).
- **GGUF Q3 LynnStyle · reasoning length (P50 / P70 / P90 tokens)**: GPQA 3,125.5 / 7,265.2 / 24,343.1; no data for MMLU and LCB.
- **GGUF Q3 LynnStyle · speed**: NInfer with built-in Q4 MTP, C8 aggregate 512.3 tok/s (512-token cap, short run); llama.cpp with built-in Q4 MTP, C4 aggregate 191.8 tok/s (4096-token cap, long run). Protocols differ; do not compare directly.
- **GGUF Q3 LynnStyle · size**: GGUF 17,303,770,432 bytes (BPW 5.07), NInfer 17,300,551,168 bytes (BPW 5.07).
- **GGUF Q4 LynnStyle · scores**: GPQA 175/198, MMLU 448/500, LCB 89/100 (llama.cpp, C4, 100K full sets; scored before the built-in MTP head was attached (external Q8 MTP draft); same quantized weights).
- **GGUF Q4 LynnStyle · reasoning length (P50 / P70 / P90 tokens)**: GPQA 2,951.0 / 8,159.3 / 25,649.0; no data for MMLU and LCB.
- **GGUF Q4 LynnStyle · speed**: NInfer with built-in Q4 MTP, C8 aggregate 436.4 tok/s (512-token cap, short run); llama.cpp with built-in Q4 MTP, C4 aggregate 81.1 tok/s (4096-token cap, long run). Protocols differ; do not compare directly.
- **GGUF Q4 LynnStyle · size**: GGUF 19,620,406,592 bytes (BPW 5.75), NInfer 19,617,187,328 bytes (BPW 5.74).
- **GGUF Q6_K · scores**: GPQA 174/198, MMLU 448/500, LCB 91/100 (llama.cpp, C4, 100K full sets).
- **GGUF Q6_K · reasoning length (P50 / P70 / P90 tokens)**: GPQA 3,683 / 9,814 / 27,966.
- **GGUF Q6_K · speed**: NInfer with MTP, C4 aggregate 235.5 tok/s; llama.cpp with MTP, C8 aggregate 189.0 tok/s (both 512-token-cap quick runs).
- **GGUF Q6_K · size**: GGUF 23,177,516,384 bytes (BPW 6.79), NInfer 23,084,144,128 bytes (BPW 6.76).
- **GGUF Q8_0 · scores**: GPQA 171/198, MMLU 443/500, LCB 90/100 (llama.cpp, C4, 100K full sets).
- **GGUF Q8_0 · reasoning length (P50 / P70 / P90 tokens)**: GPQA 3,102.5 / 7,971 / 28,891.6; MMLU 164 / 291 / 741; LCB 4,098.5 / 16,087.8 / 39,135.5.
- **GGUF Q8_0 · speed**: NInfer with MTP, C8 aggregate 486.3 tok/s (same-caliber bench); llama.cpp with MTP, C4 aggregate 190.8 tok/s.
- **GGUF Q8_0 · size**: GGUF 29,069,202,688 bytes (BPW 8.51), NInfer 29,065,983,488 bytes (BPW 8.51).
- The BPW denominator is always the text + MTP parameter count 27,320,697,856.

**Related repositories**

- Main repository (BF16 / FP8 / NVFP4 / INT8 / GGUF and all other tiers): [Hugging Face](https://huggingface.co/nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2) · [ModelScope](https://modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2)
- NVFP4-NInfer (NVFP4 and NInfer W4A4 / W4A4-W8A8, built-in MTP and DFlash2): [Hugging Face](https://huggingface.co/nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-NVFP4-NInfer) · [ModelScope](https://modelscope.cn/models/Merkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-NVFP4-NInfer)

## Training method

**Lineage**: official Qwen3.8 27B (177 / 444 / 83) → our SFT + SimPO110 final, i.e. the [EfficientThink base](https://huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2) (171 / 442 / 89) → K3 continuation SFT → week-2 SFT (week2dose, update-225) → first RLOO round (182 groups) → another SFT round (merge-sft-100), giving sft-base-rloo (177 / 448 / 90) → second RLOO round (172 groups) → **Coder390** (178 / 445 / 90, dynamic FP8). Numbers are GPQA / MMLU / LCB, all full suites under the same 100K protocol.

**Training base**: this model starts from our own EfficientThink SFT + SimPO model (the SimPO110 final) and goes through several rounds of SFT and RLOO, which is itself a post-trained version of the official Qwen3.8-27B: [Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2](https://huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2).

| Name part | Meaning |
|---|---|
| Coder | Strengthened coding |
| 390 | GPQA, MMLU, and LCB all reach 90 |
| EfficientThink | Training goal: remove unproductive reasoning tails while keeping necessary long reasoning |
| Opus5.5, GPT6Astra | Wrote the gold answers and teacher trajectories |
| Grok4.7 | Host of this training run; checked and filtered data throughout |
| DSV4Pro, K3 | Their trajectories served as teacher-model data; K3 also performed the RLOO value review and wrote part of the problems |
| SFT-RLOO | Method: alternating SFT and RLOO rounds on an SFT (incl. SimPO) base |
| GGUF-NInfer | Contents of this repository: the GGUF Q6_K package (built-in Q8_0 MTP head) and the NInfer package converted from it (built-in MTP head with Q6_K attention/MLP weights) |

**The problem**: after the model already has a local answer, it keeps re-running the same derivation with Wait / Actually until the context is full, and the final answer is empty. In most of these cases the model can solve the problem; it just does not stop.

**RLOO data**: 8 trajectories sampled per problem; only groups with both correct and wrong trajectories are kept (all-correct groups are dropped). Groups with 0 or 1 correct trajectory receive one reviewed short teacher trajectory, which replaces the shortest wrong trajectory in that group (27 groups). Final set: 172 groups, 1,376 trajectories: 859 correct and 517 wrong, including 68 empty answers.

**Reward**: penalties apply only to wrong trajectories; long correct reasoning still earns a positive reward, so necessary long reasoning is not suppressed.

| Case | Reward |
|---|---:|
| Short and correct (<24K) | +1.05 |
| Long and correct | +1.0 |
| Short and wrong (<24K) | −0.2 |
| Wrong, 24K–48K | −0.5 |
| Wrong, ≥48K | −0.7 |
| Reached 94K with an answer letter, but wrong | −0.9 |
| Empty answer | −1.0 |

**Result**: under the same protocol, 94K truncations drop from 4 to 1 on GPQA and from 13 to 3 on LCB (FP8) compared with the original FP8, and none of the three scores falls below the original FP8 (all FP8 figures; see Scores below for GGUF Q6_K).

## Scores

**Protocol**: one RTX PRO 6000, llama.cpp (`llama-server`) loading `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf`, C4, 100K context, 94,208 generation cap, client not timed, reasoning effort xhigh; full GPQA 198 / MMLU 500 / LCB 100; empty answers count as wrong, truncations that still answer correctly count as correct, and every failure stays in the denominator. `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer` is converted from the same GGUF with the same backbone quantization; the full triad was run only on the GGUF.

Reasoning length is `usage.reasoning_tokens`; a 94K truncation means the generation cap was hit. LCB empty answers are the number of problems for which no runnable code could be extracted. Bold means better than the original Qwen3.8 27B (FP8).

The original Qwen3.8 27B (FP8) was measured on two RTX PRO 6000 GPUs at C8 per GPU with SGLang; the rest of the protocol is the same.

| Suite | Precision | Score | Reasoning P50 / P70 / P90 | 94K trunc. | Empty |
|---|---|---:|---|---:|---:|
| GPQA | Original Qwen3.8 27B (FP8) | 177/198 | 5,299 / 12,607 / 50,577 | 4 | 3 |
| GPQA | GGUF Q3 LynnStyle | 173/198 | **3,125.5** / **7,265.2** / **24,343.1** | **1** | **1** |
| MMLU | GGUF Q3 LynnStyle | **449/500** | — | **0** | 0 |
| LCB | GGUF Q3 LynnStyle | **92/100** | — | **1** | **1** |
| GPQA | GGUF Q4 LynnStyle | 175/198 | **2,951.0** / **8,159.3** / **25,649.0** | **1** | **2** |
| MMLU | GGUF Q4 LynnStyle | **448/500** | — | **0** | 0 |
| LCB | GGUF Q4 LynnStyle | **89/100** | — | **2** | **2** |
| GPQA | GGUF Q2 LynnStyle | 177/198 | **2,989.5** / **8,923.8** / **27,156.1** | **3** | **0** |
| MMLU | GGUF Q2 LynnStyle | 433/500 | **181** / **326.3** / **1,020.5** | 1 | 0 |
| LCB | GGUF Q2 LynnStyle | **90/100** | **5,656.5** / **17,835.8** / **40,067.3** | **1** | **1** |
| GPQA | GGUF Q8_0 | 171/198 | **3,102.5** / **7,971** / **28,891.6** | **2** | **0** |
| GPQA | GGUF Q6_K | 174/198 | **3,683** / **9,814** / **27,966** | **2** | **2** |
| MMLU | Original Qwen3.8 27B (FP8) | 444/500 | 205 / 373 / 1,622 | 1 | 0 |
| MMLU | GGUF Q8_0 | 443/500 | **164** / **291** / **741** | **0** | 0 |
| MMLU | GGUF Q6_K | **448/500** | **162** / **310.3** / **939.4** | **0** | 0 |
| LCB | Original Qwen3.8 27B (FP8) | 83/100 | 8,388 / 24,241 / 94,208 | 13 | 13 |
| LCB | GGUF Q8_0 | **90/100** | **4,098.5** / **16,087.8** / **39,135.5** | **3** | **3** |
| LCB | GGUF Q6_K | **91/100** | — | **0** | **1** |

Note: reasoning lengths for all three Q2 LynnStyle and Q8_0 suites and for Q6_K MMLU are counted from the raw text with the BF16 tokenizer (GPQA/LCB: `reasoning` field; MMLU: text before the last line of `response`), not from the API `reasoning_tokens`. Q3 LynnStyle MMLU / LCB, Q4 LynnStyle MMLU / LCB, and Q6_K LCB have no saved reasoning text and cannot be counted; those cells read "—".

GPQA's 2 truncations are problems 79 and 127; both hit 94,208 with an empty pred, so they also count as the empty answers. MMLU has no truncations and no empty answers.

LCB has no 94K truncations; its 1 empty answer is problem 92, which stopped normally without producing code.

LCB is scored on llama.cpp: the LCB eval script gets replies from NInfer that are not valid JSON. This is a compatibility issue between the eval script and the API, not a model-quality issue; the same GGUF on llama.cpp passed every problem in the same spot-check batch.

## Quantization tiers

| Tier | Directory | Notes | Package size (bytes) | BPW | GPQA / MMLU / LCB |
|---|---|---|---:|---:|---|
| GGUF Q3 LynnStyle | `GGUF/` | Mixed-precision LynnStyle GGUF for llama.cpp (`Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.gguf`): lowest type reaches Q3; **built-in Q4 MTP** (not Q8); vision tower file also in `GGUF/` | 17,303,770,432 (~16.1 GiB) | 5.07 | 173 / 449 / 92 |
| GGUF Q3 LynnStyle · NInfer | `GGUF-NInfer/` | NInfer package from the same Q3 LynnStyle GGUF (`.ninfer`), built-in Q4 MTP; NInfer-all only; scored on llama.cpp before the built-in MTP head was attached (external Q8 MTP draft); same quantized weights | 17,300,551,168 (~16.1 GiB) | 5.07 | 173 / 449 / 92 |
| GGUF Q4 LynnStyle | `GGUF/` | Mixed-precision LynnStyle GGUF for llama.cpp (`Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.gguf`): lowest type reaches Q4; **built-in Q4 MTP** (not Q8); vision tower file also in `GGUF/` | 19,620,406,592 (~18.3 GiB) | 5.75 | 175 / 448 / 89 |
| GGUF Q4 LynnStyle · NInfer | `GGUF-NInfer/` | NInfer package from the same Q4 LynnStyle GGUF (`.ninfer`), built-in Q4 MTP; NInfer-all only; scored on llama.cpp before the built-in MTP head was attached (external Q8 MTP draft); same quantized weights | 19,617,187,328 (~18.3 GiB) | 5.74 | 175 / 448 / 89 |
| GGUF Q2 LynnStyle | `GGUF/` | Mixed-precision LynnStyle GGUF for llama.cpp (`Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.gguf`): lowest type reaches Q2; **built-in Q4 MTP** (not Q8); vision tower file also in `GGUF/` | 13,276,009,792 (~12.4 GiB) | 3.89 | 177 / 433 / 90 |
| GGUF Q2 LynnStyle · NInfer | `GGUF-NInfer/` | NInfer package from the same Q2 LynnStyle GGUF (`.ninfer`), built-in Q4 MTP; NInfer-all only; scores from GGUF on llama.cpp | 13,272,798,720 (~12.4 GiB) | 3.89 | 177 / 433 / 90 |
| GGUF Q8_0 | `GGUF/` | Single GGUF file for llama.cpp (`Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.gguf`): Q8_0 backbone, built-in MTP head | 29,069,202,688 (~27.1 GiB) | 8.51 | 171 / 443 / 90 |
| GGUF Q8_0 · NInfer | `GGUF-NInfer/` | NInfer package converted from the same Q8_0 GGUF (`.ninfer`), built-in MTP head; loadable only by NInfer-all (`iamwavecut/ninfer-all`); scores from the same GGUF on llama.cpp | 29,065,983,488 (~27.1 GiB) | 8.51 | 171 / 443 / 90 |
| GGUF Q6_K | `GGUF/` | Single GGUF file for llama.cpp (`Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf`): Q6_K backbone with imatrix calibration and a built-in Q8 MTP head, so no external draft is needed; plus `vision-bf16.safetensors` in `GGUF/` (llama.cpp vision: `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`) | 23,177,516,384 (≈21.6 GiB) | 6.79 | 174 / 448 / 91 |
| GGUF Q6_K · NInfer | `GGUF-NInfer/` | NInfer package (`.ninfer`) converted from the same GGUF, same backbone quantization, built-in MTP head (attention/MLP weights Q6_K, input projection Q8_0); plus `vision-bf16.safetensors` in `GGUF-NInfer/`; loadable only by NInfer-all (`iamwavecut/ninfer-all`); scores measured on the same GGUF with llama.cpp | 23,084,144,128 (≈21.5 GiB) | 6.76 | 174 / 448 / 91 |

BPW for GGUF Q3 LynnStyle is 17303770432 × 8 ÷ the text + MTP parameter count 27,320,697,856 = 5.07; the Q3 NInfer package is 17300551168 bytes, BPW 5.07. BPW for GGUF Q4 LynnStyle is 19620406592 × 8 ÷ the text + MTP parameter count 27,320,697,856 = 5.75; the Q4 NInfer package is 19617187328 bytes, BPW 5.74. BPW for GGUF Q2 LynnStyle is 13,276,009,792 × 8 ÷ the text + MTP parameter count 27,320,697,856 = 3.89; the Q2 NInfer package is 13,272,798,720 bytes, BPW 3.89. BPW for GGUF Q8_0 is 29,069,202,688 × 8 ÷ 27,320,697,856 = 8.51; the Q8_0 NInfer package is 29,065,983,488 bytes, BPW 8.51. BPW for the two GGUF Q6_K packages is the single file's bytes × 8 ÷ the text + MTP parameter count 27,320,697,856: the GGUF is 23,177,516,384 bytes, giving 6.79, and the NInfer package is 23,084,144,128 bytes, giving 6.76. The parameter count comes from the GGUF header (866 tensors: text 26,895,998,464 + MTP 424,699,392); vision is an external mmproj, not in the package and not in the denominator. The BF16 / FP8 / NVFP4 tiers in the main repository use 27,781,427,952 (including the vision tower), so BPW should not be compared directly across the two.

## Best-TPS and concurrency recommendations

Setup: one RTX PRO 6000 Blackwell; 1-minute quick benchmark, 512 generation cap, thinking on (xhigh), sampling temperature 1.0, top_p 0.95, top_k 20; each level sends only as many requests as its concurrency (1 at C1, 2 at C2, 4 at C4, 8 at C8). Numbers are aggregate tok/s (all output tokens ÷ wall-clock time). This is a quick benchmark with few requests and short outputs, so the numbers are not directly comparable with benchmarks run under other protocols. NInfer rows were measured on the `GGUF-NInfer/` package with the server at `--max-context 32768 --kv-capacity 32768 --kv-dtype rk8v4 --max-concurrency 8`; llama.cpp rows were measured on the `GGUF/` package.

| Engine · speculation | C1 | C2 | C4 | C8 |
|---|---:|---:|---:|---:|
| NInfer · Q8_0 MTP (4 draft tokens) | 109.5 | not measured | 332.0 | **486.3** |
| NInfer · MTP (3 draft tokens) | 131.9 | 161.9 | **235.5** | 388.1 |
| NInfer · no speculation | 56.0 | 93.9 | 162.1 | 298.1 |
| NInfer · Q3 LynnStyle MTP (4 draft, 512-cap short) | 167.4 | 179.8 | 285.1 | **512.3** |
| llama.cpp · Q3 LynnStyle MTP (4096-cap long) | 78.5 | 161.6 | **191.8** | not measured |
| NInfer · Q4 LynnStyle MTP (4 draft, 512-cap short) | 157.0 | 174.3 | 287.5 | **436.4** |
| llama.cpp · Q4 LynnStyle MTP (4096-cap long) | 73.6 | 77.7 | **81.1** | not measured |
| NInfer · Q2 LynnStyle MTP (4 draft) | 205.7 | 210.4 | 308.0 | **521.0** |
| llama.cpp · Q2 LynnStyle MTP | 104.9 | 167.6 | **174.8** | not measured |
| llama.cpp · Q8_0 MTP | 85.3 | 133.6 | **190.8** | not measured |
| llama.cpp · Q6_K MTP | 97.2 | 95.3 | 168.1 | 189.0 |
| llama.cpp · no speculation | 53.4 | not measured | 123.0 | not measured |

- **Recommendation**: NInfer + C4 + MTP (235.5 tok/s), matching the recommended launch command below (`--max-concurrency 4`). For aggregate throughput alone, C8 is higher (388.1 tok/s); raise `--max-concurrency` to 8 for that.
- **MTP is built in**: both packages carry a built-in MTP head (GGUF: Q8_0; NInfer: Q6_K attention/MLP weights), so no external draft model is needed. For a single request, NInfer with MTP reaches 131.9 tok/s, about 2.5× llama.cpp without speculation (53.4 tok/s); llama.cpp with MTP reaches 97.2 tok/s aggregate at C1 (119.0 tok/s single-request decode).

## Recommended launch commands

### Quantization scheme

**GGUF Q6_K** (`GGUF/`, `GGUF-NInfer/`): Q6_K backbone calibrated with a 512-chunk imatrix, plus structure protection: the linear-attention (SSM) `ssm_alpha` / `ssm_beta` stay BF16, the token embedding and output head are Q8_0, and the MTP head (`blk.64` attn q/k/v/output, ffn gate/up/down, and `nextn.eh_proj`) is entirely Q8_0 with its norms kept in F32. The GGUF has 866 tensors in 65 blocks (64 backbone layers + 1 MTP layer). The `.ninfer` is converted from the same GGUF with the same backbone quantization; its MTP attention/MLP weights are Q6_K (`gguf_q6_k`) and only `mtp/input_projection` is Q8_0; vision weights are not inside either package: use `GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` (llama.cpp `--mmproj`) or `vision-bf16.safetensors` (NInfer).

### Commands

Load the `.ninfer` with NInfer-all (the `master` branch of `iamwavecut/ninfer-all`): the package keeps the GGUF quantization blocks, which other NInfer builds cannot read. llama.cpp needs an upstream build that supports `--spec-type draft-mtp`. NInfer's `--kv-capacity` is one KV pool shared by all requests; llama.cpp's `-c 409600 -np 4` splits the context evenly across 4 slots, so each request gets at most 102,400 tokens, enough for a single request to reach 94K. The chat endpoint is `POST /v1/chat/completions`; for NInfer the request `model` must match `--model-id`. Vision: `GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` for llama.cpp `--mmproj` (629,247,008 bytes, sha256 `cae9799dc9196449b0d83f716e64af89d5cf65147510462e5a629a2aa23adb32`). `vision-bf16.safetensors` remains in `GGUF/` and `GGUF-NInfer/` for NInfer. Do not require mmproj inside NInfer packages.

**MTP and DFlash2**: every GGUF / NInfer package in this repository has a built-in MTP head (Q2 / Q3 / Q4 LynnStyle use built-in **Q4** MTP). To enable it, add `--spec mtp --draft-tokens N` for NInfer or `--spec-type draft-mtp --spec-draft-n-max N` for llama.cpp; leave these out to run without speculation. DFlash2 is an external draft model and is not in these packages; this repository does not ship a DFlash2 draft for GGUF / NInfer. For DFlash2, use the `DFlash2-FP8/` draft that ships with the BF16 / FP8 / NVFP4 tiers in the main repository (SGLang `--speculative-algorithm DFLASH`) or the `.ninfer` packages in the NVFP4-NInfer repository (`--spec dflash2`). MTP and DFlash2 are mutually exclusive: a server runs one or the other, never both.

#### NInfer

**NInfer · GGUF Q2 LynnStyle · MTP (draft 4, recommended concurrency 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.ninfer \
  --model-id qwen3.8-27b-coder390-Q2LynnStyle-Mtp \
  --max-context 102400 --kv-capacity 819200 --kv-dtype rk8v4 \
  --max-concurrency 8 --spec mtp --draft-tokens 4
```

**NInfer · GGUF Q3 LynnStyle · MTP (draft 4, recommended concurrency 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.ninfer \
  --host 127.0.0.1 --port 8080 --device 0 \
  --model-id qwen3.8-27b-coder390-Q3LynnStyle-Mtp \
  --max-context 102400 --kv-capacity 819200 --max-concurrency 8 \
  --kv-dtype rk8v4 \
  --spec mtp --draft-tokens 4
```

**NInfer · GGUF Q4 LynnStyle · MTP (draft 4, recommended concurrency 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.ninfer \
  --host 127.0.0.1 --port 8080 --device 0 \
  --model-id qwen3.8-27b-coder390-Q4LynnStyle-Mtp \
  --max-context 102400 --kv-capacity 819200 --max-concurrency 8 \
  --kv-dtype rk8v4 \
  --spec mtp --draft-tokens 4
```

**NInfer · GGUF Q6_K · MTP (draft 3, recommended concurrency 4)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer \
  --model-id qwen3.8-27b-coder390-q6k-mtp \
  --max-context 102400 --kv-capacity 819200 --kv-dtype rk8v4 \
  --max-concurrency 4 --spec mtp --draft-tokens 3
```

**NInfer · GGUF Q8_0 · MTP (draft 4, recommended concurrency 8)**

```bash
ninfer-serve ./GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.ninfer \
  --model-id qwen3.8-27b-coder390-q8-mtp \
  --max-context 102400 --kv-capacity 819200 --kv-dtype rk8v4 \
  --max-concurrency 8 --spec mtp --draft-tokens 4
```

Do not use `--max-context 131072 --kv-capacity 131072` (the KV pool then holds only one full context and queued requests return HTTP 503). Do not use `--lm-head-q6` / `--embedding-q4`. Requires NInfer-all (`ninfer-serve`); official ninfer-src cannot read `gguf_blocks_v1`.

#### llama.cpp

**llama.cpp · GGUF Q2 LynnStyle · MTP (representative; swap the `-m` path for Q3 / Q4 / Q6_K / Q8_0)**

```bash
llama-server -m ./GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.gguf \
  -c 409600 -np 4 -ngl 99 --jinja \
  --host 127.0.0.1 --port 8080 \
  --alias qwen3.8-27b-coder390-Q2LynnStyle-Mtp \
  --spec-type draft-mtp --spec-draft-n-max 4
```

To use another GGUF tier, only change `-m` to `...-Q3LynnStyle-MTP.gguf`, `...-Q4LynnStyle-MTP.gguf`, `...-Q6_K-MTP.gguf`, or `...-Q8_0-MTP.gguf` (and the `--alias`, e.g. `qwen3.8-27b-coder390-q6k-mtp`, `qwen3.8-27b-coder390-q8-mtp`). MTP is fused inside the GGUF (`blk.64`); do not attach an external draft; the log should show `creating MTP draft context against the target model`. For image input add `--mmproj ./GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf`.
## Files overview

### `GGUF/` (Q3/Q4/Q2/Q8/Q6 packages + mmproj + vision-bf16.safetensors + SHA256SUMS)

| File | Bytes | SHA256 |
|---|---:|---|
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.gguf` | 17,303,770,432 | `9985b3d41fc7dc0bb5d493f7523d4515504912a3a7fc830f66e0fd2f90f9fd95` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.gguf` | 19,620,406,592 | `56c7c605a59764d9f0bb645d4eb335f1574af2c9e74020386139597df0e872f5` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.gguf` | 13,276,009,792 | `cad973b3b7cc0d86bd39968209268318a746673c12c9536e5c37408baa7b5e6e` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.gguf` | 29,069,202,688 | `b24e8c5fe3f2b282cce841400c740fcaca48afcdcf9e991f7bbe9a59a78cabfe` |
| `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf` | 23,177,516,384 | `302597c53d1b00f7230b53a8a050831390a4371e92a80c1ac644fa920af76ab1` |
| `GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` | 629,247,008 | `cae9799dc9196449b0d83f716e64af89d5cf65147510462e5a629a2aa23adb32` |
| `GGUF/vision-bf16.safetensors` | 921,497,224 | `d7defc90994f7bbc58a8feb8d3c4144895babb434a9be9f47e88ae3646e1df33` |
| `GGUF/SHA256SUMS` | 786 | `c313f9b5bdb19d410ae2c7e84388b8c35f0eb5f8e844dbeb1790eb79e48175e5` |

### `GGUF-NInfer/` (Q3/Q4/Q2/Q8/Q6 packages + vision-bf16.safetensors + SHA256SUMS)

| File | Bytes | SHA256 |
|---|---:|---|
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP.ninfer` | 17,300,551,168 | `983456fc57cfc63a160512c933dc44f93005151c954b060399373d685b10de99` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP.ninfer` | 19,617,187,328 | `376ee05a8c5160bde35e7c742565671fb4cec0e6e3c82cb293f87ad3a27d6cac` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP.ninfer` | 13,272,798,720 | `cdd82b69027faba0bda619224ee851ccd0ecf0dfb176bad85a17d2df07926921` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q8_0-MTP.ninfer` | 29,065,983,488 | `40937a1509b0333858703b7c285b5a64b507803060a94bcccee19f4f683647bf` |
| `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer` | 23,084,144,128 | `c273d394cd727a1055abc53b29e253e258926288429751e00acbeaea1ec0cd0e` |
| `GGUF-NInfer/vision-bf16.safetensors` | 921,497,224 | `d7defc90994f7bbc58a8feb8d3c4144895babb434a9be9f47e88ae3646e1df33` |
| `GGUF-NInfer/SHA256SUMS` | 701 | `2cf12cd6ca7a98ed5340a9bb3139a3f90401f39ab12798bb082c83df2eed47b2` |

### Verification

```bash
(cd GGUF && sha256sum -c SHA256SUMS)
(cd GGUF-NInfer && sha256sum -c SHA256SUMS)
```

## Loading notes

- Both packages share the same backbone quantization (Q6_K + imatrix + structure protection; see Quantization scheme above) and carry a built-in MTP head (GGUF `blk.64` is Q8_0; NInfer MTP attention/MLP weights are Q6_K, input projection Q8_0), so no external draft model is needed; NInfer + C4 + MTP is recommended.
- `GGUF/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.gguf`: loads directly in llama.cpp; the GGUF architecture is `qwen35` with 65 blocks, the last of which (`blk.64`) is MTP.
- `GGUF-NInfer/Qwen3.8-27B-Coder390-EfficientThink-Q6_K-MTP.ninfer`: loadable only by NInfer-all; SGLang, vLLM, llama.cpp, and transformers cannot read it.
- Vision: llama.cpp `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` (629,247,008 bytes, sha256 `cae9799dc9196449b0d83f716e64af89d5cf65147510462e5a629a2aa23adb32`). Also `vision-bf16.safetensors` in `GGUF/` and `GGUF-NInfer/` (921,497,224 bytes, sha256 `d7defc90994f7bbc58a8feb8d3c4144895babb434a9be9f47e88ae3646e1df33`) for NInfer. mmproj is for llama.cpp only — not required inside NInfer packages.
- Scores were measured with the GGUF package on llama.cpp; the LCB eval script is not compatible with the NInfer API (its replies are not valid JSON), which is an eval-script issue rather than a model-quality issue, so LCB is scored on llama.cpp.

## Known limitations

- GGUF Q6_K: GPQA 174 is below the 177 of the original Qwen3.8 27B (FP8); MMLU reasoning length is counted from the raw text with the BF16 tokenizer, and LCB kept no reasoning text, so it has no reasoning-length percentiles; the full triad was run only on the GGUF (llama.cpp), not separately on the NInfer package; speeds come from a quick benchmark (requests per level equal to the concurrency, 512 generation cap) and are not directly comparable with the other tiers; mmproj is provided in `GGUF/`; image path not fully re-benched on every tier.
- Scores depend on the protocol above (100K context, 94,208-token cap, no timeout) and should not be compared directly with numbers from other protocols; MMLU shows sampling variation.

## License and acknowledgements

Licensed under Apache-2.0, following the license of the base model Qwen3.8-27B.

Thanks to the Qwen team for the base model and the official FP8 scheme; to Opus5.5, GPT6Astra, Grok4.7, DSV4Pro, and K3 for problem writing, gold labels, teacher trajectories, data review, and the RLOO value review; and to the llama.cpp and NInfer communities.
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