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GRM-2.6-Plus-GGUF

morikomorizz

GRM-2.6-Plus is a highly capable **27B-parameter reasoning model** built on the Qwen3.6 architecture. It is specifically engineered for **general-purpose AI** and optimized for **difficult, high-complexity tasks**. These GGUF files are fully compatible with llama.cpp and popular graphical interfaces like **LM Studio**, **Ollama**. ```bash ./llama-cli -m GRM-2.6-Plus-Q8_0.gguf \ -p "System: You are a helpful assistant.\nUser: Create a calculator in a single HTML file backwards.\nAssistant:" \ -n 2048 -c 8192

Text generation
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This repository contains the **GGUF** quantized files for **nex-agi/Nex-N2-Pro**. - **Original Model:** nex-agi/Nex-N2-Pro - **Architecture:** Qwen3.5-397B-A17B - **License:** Apache 2.0 - **MTP Support:** MTP Donor-unsloth/Qwen3.5-397B-A17B-MTP-GGUF Today, we are officially releasing and open-sourcing our next-generation model, **Nex-N2** — an agent model built for real-world productivity scenarios. With first-tier coding and agentic capabilities, Nex-N2 keeps driving complex, long-horizon tasks forward in real environments to deliver stable, end-to-end results. Over the past year, a paradigm shift led by Vibe Coding and Harness Engineering has been redefining the limits of LLM agents. From dialogue, to reasoning, to agents that execute long-horizon tasks with environmental feedback, the tasks models must handle keep growing harder, the contexts longer, and the environments more realistic. The core of next-generation model competition is no longer *whether a model can think*, but whether it can reliably and efficiently turn thinking into actions that are executable, verifiable, and iterable.

Text generation
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The default FP16 model can take up 6-7gb just to load vram and do image Diffusion, with a separate gguf, the unet model only needs 2.8gb vram on KSampler / Processing Image. Base Model: https://civitai.com/models/257749/pony-diffusion-v6-xl All credits to the original creator: https://civitai.com/user/PurpleSmartAI Support My Work & Request Custom Quantization: It takes time, testing, and electricity to compress these models perfectly

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This model is a modified version of **Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive** grafted with the **Multi-Token Prediction (MTP)** module using the MTP donor from the **Qwen 3.6-35B-A3B-MTP-GGUF** series by **Unsloth**. This modification aims to provide faster inference speeds via MTP-based speculative decoding without sacrificing the base model's original quality or capabilities. * **Base Model:** HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive * **MTP Donor:** unsloth/Qwen3.6-35B-A3B-MTP-GGUF * **Architecture:** Mixture of Experts (MoE) — 35B total parameters / ~3B active per forward pass (256 experts, 8 routed per token) * **Context Window:** 262K (262,144 tokens) * **Multimodal Capabilities:** Supports text, image, and video processing * **Uncensored Nature:** Inherits the *Aggressive* variant from HauhauCS (0/465 refusals on standard evaluation datasets, removing default refusal behavior while maintaining base performance and model traits).

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