Qwopus3.5-9B-Coder-MTP-GGUF

모델 소개

> [!NOTE] > **What is MTP (Multi-Token Prediction)?** > > **MTP** is a revolutionary technology in the field of Large Language Model (LLM) training and inference in recent years. Unlike traditional autoregressive models that predict only a single token at each step (Single-Token Prediction), **MTP models are designed during training to simultaneously predict multiple future tokens at each position**. > > This architecture brings two core dimensions of transformation: > 1. **Deeper Representation and Planning**: It forces the model to perform global planning (Long-Horizon Planning) at the representation level for longer-term contexts. This enhances cognitive coherence in logic-intensive tasks such as complex coding and multi-step mathematical reasoning, while effectively mitigating the "reasoning bubbles" and repetition loops common in traditional autoregressive models. > 2. **Extreme Inference Speedup (Speculative Decoding)**: During inference, the model is equipped with additional lightweight auxiliary prediction heads (Draft Heads, configured as `draft=2` in this model). While the backbone network generates the current token, the Draft Heads predict the subsequent 2 candidate tokens in parallel with negligible computational overhead, which are then verified by the main model in a single forward pass. Once verified, the model can output multiple tokens in a single inference step, yielding substantial throughput gains. Based on actual testing across **Logic / Coding / DevOps / Math / Edge** (5 core domains, 30 complex evaluation questions), **Qwopus3.5-9B-Coder-MTP (draft=2)** demonstrates absolute advantages in both speed and correctness: * **⚡ Speed Leap**: Overall throughput rate has jumped from **4.94 T/s** to **6.71 T/s** (**+35.8% throughput improvement**), saving **16.4 minutes** in total latency (overall time reduced by **25%**). * **🎯 Accuracy & Robustness**: Overall accuracy improved from **80.0%** to **88.3%** (**+8.3pp**). The model achieved a perfect score in both **Coding (100% accuracy)** and **Math (100% accuracy)**, two high-difficulty task scenarios, completely eliminating the code truncations and repetitive behaviors observed in the Base model (independent of model type). * **📊 Overall Efficiency Index**: After weighting correctness against inference time, the overall reasoning efficiency of the MTP model improved by **38.4%**.

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

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

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。

자주 묻는 질문

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。