MODEL PROFILE

Qwen3.8-Flash-Next-GSQ-RCO-GGUF

ISTA-DASLab · Qwen3.8-Flash-Next-GSQ-RCO-GGUF

**Non-uniform GGUF quantizations** of a 512-expert MoE, produced with **GSQ** and **RCO**, with a vision projector for multimodal use. This repository provides GGUF quantizations of **Qwen3.8-Flash-Next** at four sizes, together with the model's vision projector (`mmproj`) for multimodal use. In contrast to uniform quantization, which applies a single quantization type to all weight tensors, each model here assigns a separate quantization type to every tensor. The assignment is obtained by a gradient-based search that allocates precision according to per-tensor sensitivity, subject to a total size budget. The resulting files are standard GGUF and run unmodified in `llama.cpp`, Ollama, and LM Studio. A code-specialized variant is released separately as Qwen3.8-Flash-Next-GSQ-RCO-Coder, which retains 256 of the 512 routed experts per layer at an effective 1.89 bpw over the original parameters, for a 58.4 GB total.

Text generationmultimodallicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsOpen weights
Profile updated2026/10/28 14:36:02
OVERVIEW

Model overview

**Non-uniform GGUF quantizations** of a 512-expert MoE, produced with **GSQ** and **RCO**, with a vision projector for multimodal use. This repository provides GGUF quantizations of **Qwen3.8-Flash-Next** at four sizes, together with the model's vision projector (`mmproj`) for multimodal use. In contrast to uniform quantization, which applies a single quantization type to all weight tensors, each model here assigns a separate quantization type to every tensor. The assignment is obtained by a gradient-based search that allocates precision according to per-tensor sensitivity, subject to a total size budget. The resulting files are standard GGUF and run unmodified in `llama.cpp`, Ollama, and LM Studio. A code-specialized variant is released separately as Qwen3.8-Flash-Next-GSQ-RCO-Coder, which retains 256 of the 512 routed experts per layer at an effective 1.89 bpw over the original parameters, for a 58.4 GB total.

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.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

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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Frequently asked questions

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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。