Model overview
> [!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
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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。
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Sources & traceability
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。