Mixed-precision **GSQ-RCO** GGUF quantizations of TokenRhythm/NeoHorse-1-9B, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 262,144 tokens (262K). Standard quantization severely degrades hybrid SSM-Transformer models (e.g. stock low-bit quantizations cause recurrent state drift and degradation in reasoning coherence). **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Keeps all 48 critical SSM parameters in unquantized **BF16**. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
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View details ↗GGUF files for Nex-N2.5-mini, a long-context agentic model for coding, tool use, computer use, and multimodal workloads. The source checkpoint supports a native context length of **262,144 tokens (256K)**. *Benchmark results reported by Nex-AGI for the original Nex-N2.5 family. These figures are not measurements of this GGUF conversion.* The projector is optional for text-only use. Use it with a current llama.cpp build that supports the model's multimodal path.
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View details ↗Mixed-precision **GSQ-RCO** GGUF quantizations of ornith-ai/Ornith-1.5-9B, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 262,144 tokens (262K). Standard quantization severely degrades hybrid SSM-Transformer models (e.g. stock IQ3_M perplexity drops to ~75.2). **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Keeps all 48 critical SSM parameters in unquantized **BF16**. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
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View details ↗GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1-Turbo for local image generation using the official turbo base weights. **Q4_K_M** is recommended for the best balance of size and quality for GGUF. **INT8 ConvRot** and **NVFP4** offer high performance on supported GPUs, while **MLX** formats are optimized for Apple Silicon. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.
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View details ↗GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. **Q4_K_M** is recommended for the best balance of size and quality for GGUF. **INT8 ConvRot** and **NVFP4** offer high performance on supported GPUs, while **MLX** formats are optimized for Apple Silicon. Use the model with ComfyUI and ComfyUI-GGUF.
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View details ↗> [!IMPORTANT] > **Compatibility:** These GGUF files require `llama.cpp` **b10828** or later, which includes official support for the Spark-X2.5 (`spark2_5`) architecture. Applications with a bundled runtime must use an equivalent or newer build. llama.cpp support GGUF quantizations of XHToken/Spark-X2.5-4B, a 4B general-purpose language model for reasoning, coding, tool use, and agentic workflows. Native context: **1,048,576 tokens (1M)**. Includes the upstream chat_template.jinja. Checksums: SHA256SUMS.txt.
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