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AI 模型库/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF
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Qwen3.6-35B-A3B-MTP-GGUF

havenoammo · Qwen3.6-35B-A3B-MTP-GGUF

> [!IMPORTANT] > ### MTP Layers Grafted on Unsloth UD XL Quantizations > > The GGUF files in this repo are **Unsloth Dynamic 2.0 XL (UD XL) quantizations** of Qwen3.6-35B-A3B > with **Multi-Token Prediction (MTP) layers grafted on top**. > > - **Base quantization:** Unsloth UD XL — see > Unsloth Dynamic 2.0 GGUFs for benchmarks. > - **MTP layers:** Taken from > am17an/Qwen3.6-35BA3B-MTP-GGUF > and stored in **Q8_0** precision, then merged into the UD XL GGUFs. > - **Why Q8 for MTP?** The draft heads are small relative to the base model, so Q8_0 keeps them > near-lossless while avoiding the overhead of full-requantizing the entire stack. > - **`convert.py`:** The script used to graft MTP layers into the UD XL GGUFs. Adapted from > this gist. > - **`35BA3B-MTP.gguf`:** The raw Q8_0 MTP layer source file (extracted only) used as input to `convert.py`. > > To run these files with MTP you need a custom build of **llama.cpp** that includes the MTP/speculative > decoding support from PR #22673. Follow the steps below. > > --- > > ### 🐳 Or Use Pre-built Docker Images (Recommended) > > Don't want to build from source? Ready-to-use Docker images with MTP support are available for multiple backends: > > > | Image | Backend | > |---|---| > | `havenoammo/llama:cuda13-server` | CUDA 13 | > | `havenoammo/llama:cuda12-server` | CUDA 12 | > | `havenoammo/llama:vulkan-server` | Vulkan | > | `havenoammo/llama:intel-server` | Intel (CPU/Xe) | > | `havenoammo/llama:rocm-server` | ROCm (AMD) | > > Quick start — running this model via Docker: > > The two flags that matter most for MTP are `--spec-type mtp` and `--spec-draft-n-max 3`. > See the Reddit post for more details and discussion. > > --- > > ### Building llama.cpp with MTP Support — Step by Step > > #### 1. Clone and enter the repo > > > #### 2. Fetch the latest remote changes > > This pulls all new refs from the upstream repository so you're working off the current tip of master. > > #### 3. Fetch PR #22673 as a local branch > > PR #22673 ("llama + spec: MTP Support") adds the speculative-decoding / MTP infrastructure that > lets `llama-server` consume multi-token prediction heads. We pull it directly without waiting for > i

译文排队中，当前展示来源原文。

视觉问答license:apache-2.0
上下文窗口262,144 Token
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:17:01
OVERVIEW

模型介绍

> [!IMPORTANT] > ### MTP Layers Grafted on Unsloth UD XL Quantizations > > The GGUF files in this repo are **Unsloth Dynamic 2.0 XL (UD XL) quantizations** of Qwen3.6-35B-A3B > with **Multi-Token Prediction (MTP) layers grafted on top**. > > - **Base quantization:** Unsloth UD XL — see > Unsloth Dynamic 2.0 GGUFs for benchmarks. > - **MTP layers:** Taken from > am17an/Qwen3.6-35BA3B-MTP-GGUF > and stored in **Q8_0** precision, then merged into the UD XL GGUFs. > - **Why Q8 for MTP?** The draft heads are small relative to the base model, so Q8_0 keeps them > near-lossless while avoiding the overhead of full-requantizing the entire stack. > - **`convert.py`:** The script used to graft MTP layers into the UD XL GGUFs. Adapted from > this gist. > - **`35BA3B-MTP.gguf`:** The raw Q8_0 MTP layer source file (extracted only) used as input to `convert.py`. > > To run these files with MTP you need a custom build of **llama.cpp** that includes the MTP/speculative > decoding support from PR #22673. Follow the steps below. > > --- > > ### 🐳 Or Use Pre-built Docker Images (Recommended) > > Don't want to build from source? Ready-to-use Docker images with MTP support are available for multiple backends: > > > | Image | Backend | > |---|---| > | `havenoammo/llama:cuda13-server` | CUDA 13 | > | `havenoammo/llama:cuda12-server` | CUDA 12 | > | `havenoammo/llama:vulkan-server` | Vulkan | > | `havenoammo/llama:intel-server` | Intel (CPU/Xe) | > | `havenoammo/llama:rocm-server` | ROCm (AMD) | > > Quick start — running this model via Docker: > > The two flags that matter most for MTP are `--spec-type mtp` and `--spec-draft-n-max 3`. > See the Reddit post for more details and discussion. > > --- > > ### Building llama.cpp with MTP Support — Step by Step > > #### 1. Clone and enter the repo > > > #### 2. Fetch the latest remote changes > > This pulls all new refs from the upstream repository so you're working off the current tip of master. > > #### 3. Fetch PR #22673 as a local branch > > PR #22673 ("llama + spec: MTP Support") adds the speculative-decoding / MTP infrastructure that > lets `llama-server` consume multi-token prediction heads. We pull it directly without waiting for > i

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。

PRICING & COST

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。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用？

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗？

不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

huggingface · havenoammo/Qwen3.6-35B-A3B-MTP-GGUF ↗2026/10/30 07:58:25
huggingface · enrich:98871a02-a06b-4a5d-8c3c-8c57e10beb97 ↗2026/10/30 21:52:47

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。