MODEL PROFILE

Qwen-Image-2.1-Text-Encoder-GGUF

darrellbest · Qwen-Image-2.1-Text-Encoder-GGUF

GGUF builds of the text encoder shipped in Qwen/Qwen-Image-2.1 (`text_encoder/`). That encoder is stock Qwen/Qwen3-VL-8B-Instruct: all 750 tensors match in name, shape and dtype, and every sampled tensor is byte-identical. So these files also work as a general Qwen3-VL-8B-Instruct GGUF. **Q6_K and Q4_K_M are not stock recipes.** Plain Q6_K visibly garbled rendered text, so both use: - an importance matrix computed on ~1500 real image prompts wrapped in the pipeline's own encoder template (`llama-imatrix --parse-special`); - the last 4 decoder layers (32-35) kept at Q8_0, because the pipeline reads the last layer's output directly; - token embeddings at Q8_0. **Recommendation:** Q8_0 if it fits. Q6_K is the smallest build that rendered text correctly in testing. Q4_K_M is usable, but at the same seed it changes composition and can misspell rendered text.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:52
OVERVIEW

Model overview

GGUF builds of the text encoder shipped in Qwen/Qwen-Image-2.1 (`text_encoder/`). That encoder is stock Qwen/Qwen3-VL-8B-Instruct: all 750 tensors match in name, shape and dtype, and every sampled tensor is byte-identical. So these files also work as a general Qwen3-VL-8B-Instruct GGUF. **Q6_K and Q4_K_M are not stock recipes.** Plain Q6_K visibly garbled rendered text, so both use: - an importance matrix computed on ~1500 real image prompts wrapped in the pipeline's own encoder template (`llama-imatrix --parse-special`); - the last 4 decoder layers (32-35) kept at Q8_0, because the pipeline reads the last layer's output directly; - token embeddings at Q8_0. **Recommendation:** Q8_0 if it fits. Q6_K is the smallest build that rendered text correctly in testing. Q4_K_M is usable, but at the same seed it changes composition and can misspell rendered text.

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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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

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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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。