Qwopus3.5-2B-v3-GGUF
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Qwopus3.5-4B-Coder
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詳細を見る ↗Qwopus3.5-4B-Coder-GGUF
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.5-4B-Coder-MTP-GGUF
Jackrong💡 What is Qwopus3.5-4B-Coder? 🪐 Qwopus3.5-4B-Coder is a compact coding and agent model built on the Qwen3.5 4B family. It is optimized for local execution, code debugging, structured tool-use behavior, and reasoning-heavy developer workflows. The training recipe follows the Qwopus Coder line: Trace Inversion for learnable reasoning traces, high-quality agent trajectories for tool behavior, and curriculum SFT to preserve formatting stability under longer contexts. 🧩 Structured Debugging Targets bug localization, minimal patch reasoning, and environment-verified code repair behavior. 🪶 Agent Trace Alignment Learns from tool-call trajectories that include real feedback loops, not only isolated prompt-response pairs.
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詳細を見る ↗Qwopus3.5-4B-v3-GGUF
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.5-4B-v3-MTP-GGUF
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3-5-9B-Coder-Flash-GGUF
Jackrong**Qwopus3.5-9B-Coder-Flash** is a coding and agent-focused fine-tune of Qwen3.5-9B, prepared from the Unsloth distribution. This repository provides the **merged dense checkpoint**. The benchmarks below evaluate the release checkpoint. A small model should make useful work accessible on local hardware. Following the efficiency-first philosophy of Qwopus3.8-27B-Flash-V2, Coder Flash aims to preserve practical problem-solving ability while reducing unproductive reasoning. This matters especially in an agent loop: **read → reason → call tools → edit → test**. A little extra thinking on every turn accumulates into a long wait. The goal is focused reasoning that reaches usable code and a clear next action, with less generation overhead.
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詳細を見る ↗Qwopus3.5-9B-Coder-GGUF
JackrongAs the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance **🤖 Agentic Coding, complex Tool Calling, and logical reasoning.** > 💡 **Why the 9B Dense Model?** > We believe that the 9B dense architecture represents the perfect **"sweet spot"** for large language models. It runs seamlessly at 8-bit precision on entry-level 16GB RAM devices—such as standard laptops and the Mac mini—making it exceptionally lightweight yet highly versatile. Without requiring expensive hardware, it allows you to achieve excellent performance paired with impressive inference speeds. Simply put, **Qwen3.5-9B is currently the best open-source model in its class.** > [!TIP] >**Vision & Tool Calling Support**: This model supports visual capabilities and tool calling. To enable vision, please place the `mmproj.gguf` file from the GGUF repository into the same directory as the main `.gguf` file.
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詳細を見る ↗Qwopus3.5-9B-Coder-MTP-GGUF
Jackrong> [!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%**.
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詳細を見る ↗Qwopus3.5-9B-v3
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.5-9B-v3-GGUF
Jackrong🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab. **🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)** * **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face. * **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth. * *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*
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詳細を見る ↗Qwopus3.6-27B-Coder
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Bundled-MTP GGUF release with expanded chat-template interoperability for tool-using runtimes and OpenAI-compatible agent histories. ✓ JSON-string arguments ✓ Mapping & list arguments ✓ llama.cpp / minja ✓ Hugging Face Jinja ⚡ Bundled MTP 🧠 Weight-identical validation This edition expands interoperability across agent frameworks that store historical tool_call.function.arguments in different valid representations. The embedded template accepts pre-serialized JSON strings as well as structured mappings and lists, while preserving the original tool-call payload. This is a template-level compatibility update: model weights and the bundled MTP prediction tensors remain unchanged.
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詳細を見る ↗Qwopus3.6-27B-Coder-GGUF
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.6-27B-Coder-MTP-GGUF
Jackrong🧬 Trace Inversion & Negentropy 🧠 27B Dense Model ⚡ Agentic Coding 🛠️ Tool Calling & Agent 🏆 SWE-bench Verified: 67.0% (off-thinking) 💡 What is Qwopus-3.6-27B-Coder? 🪐 Qwopus-3.6-27B-Coder is a reasoning-enhanced agentic coding model built on top of Qwopus3.6-27B-v2 . It inherits the powerful reasoning foundation of the v2 base — which achieved 87.43% MMLU-Pro and 75.25% SWE-bench Verified — and further specializes it for agentic code generation, structured tool calling, debugging, and instruction-following in developer workflows. The model is designed to excel at repository-level coding tasks, multi-turn tool orchestration, and complex logical reasoning under realistic agent environments. 🧩 Agentic Coding Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows.
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詳細を見る ↗Qwopus3.6-27B-Fusion-GGUF
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.6-27B-v1-preview
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.6-27B-v1-preview-GGUF
JackrongAs part of the Qwopus series, this release continues the same core direction established in earlier versions: stronger reasoning quality, a more stable answer structure, and less stylistic drift across long-form responses. Instead of introducing a complicated multi-stage design, this preview emphasizes a cleaner and more controlled supervised fine-tuning recipe, with the goal of producing outputs that feel more coherent, deliberate, and aligned across different tasks. > [!TIP] > This model was trained using Unsloth, and the full training workflow runs end-to-end successfully in practice. Many thanks to the Unsloth team for building and maintaining such a practical training stack for open-model fine-tuning. The current version is trained on a mixed reasoning dataset built primarily from `Kassadin88/Claude-Distillation-Dataset`, with additional samples drawn from:
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詳細を見る ↗Qwopus3.6-27B-v2
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.6-27B-v2-GGUF
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.6-27B-v2-MTP-GGUF
Jackrong💡 What is Qwopus3.6-27B-v2-MTP? 🪐 Qwopus3.6-27B-v2-MTP is a speed-oriented reasoning release built on top of Qwen3.6-27B . It keeps the Qwopus line's focus on reconstructed reasoning traces, coding discipline, DevOps procedures, and mathematical derivations, while adding Multi-Token Prediction for faster generation. The goal is simple: preserve the depth and structure of a 27B reasoning model while making real interactive use noticeably faster. ⚡ MTP Decoding Auxiliary future-token prediction improves throughput on long reasoning, code, math, and strict-format prompts. 🧩 Structured Reasoning Inherits the Qwopus training recipe built around reconstructed step-by-step reasoning trajectories. 🧪 GB10 Tested Validated on a 30-question local benchmark across Logic, Coding, DevOps, Math, and Edge tasks. 🚀 Practical Speed Designed for workflows where strong answers matter, but waiting several extra minutes per task does not. Qwen3.6-27B provides the dense 27B foundation for this release. Qwopus3.6-27B-v2-MTP focuses on preserving the base model's broad reasoning capability while tuning the output style toward stepwise analysis, tool-aware execution, and practical engineering answers. Attribute Specifications & Details 🧠 Architecture Dense Transformer / 27 Billion Parameters 🎯 Focus Domains Agentic Coding, DevOps, structured logic, mathematics, and strict-format output ⚡ MTP Objective Improve generation throughput through multi-token speculative prediction while retaining final-answer quality.
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詳細を見る ↗Qwopus3.6-35B-A3B-Coder
Jackrong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwopus3.6-35B-A3B-Coder-MTP-GGUF
JackrongA thinking-off, token-efficient coding agent model built on Qwopus3.6-35B-A3B-v1 / Qwen3.6-35B-A3B. 🧠 Thinking-Off Agent ⚡ Token-Efficient Coding 🛠️ Tool Calling & Workflow 🧩 35B-A3B MoE 🎮 Game Demo Ready 💡 What is Qwopus-3.6-35B-A3B-Coder? 🪐 Qwopus-3.6-35B-A3B-Coder is a practical coding-agent fine-tune focused on execution efficiency , not simply longer visible reasoning. It is designed for real agentic coding workflows where the model repeatedly reads files, chooses tools, edits code, runs tests, reacts to errors, and summarizes work. The core goal is to complete more of these steps with less token waste, lower latency, and more stable behavior when explicit long thinking is disabled.
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詳細を見る ↗Qwopus3.6-35B-A3B-v1-GGUF
Jackrong**Qwen3.6-35B-A3B** is an advanced hybrid sparse MoE (Mixture-of-Experts) model developed by Alibaba Cloud. It features 35B total parameters with only 3B active parameters per token, ensuring high inference efficiency. Architecturally, it combines Gated DeltaNet linear attention with standard gated attention layers, routing tokens across **256 experts**. It natively supports a massive **262k context window** and is specifically designed for high-performance agentic coding, deep reasoning, and multimodal tasks. 🪐**Qwopus3.6-35B-A3B-v1** is a reasoning-enhanced MoE (Mixture of Experts) model fine-tuned on top of **Qwen3.6-35B-A3B**. The fine-tuning process for this model is structured into **three distinct stages of distributed SFT (Supervised Fine-Tuning)**, progressively scaling reasoning complexity and data diversity. This systematic approach ensures the model inherits the base MoE capabilities while sharpening its logic-handling depth.
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