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AI Model Library/keithnull/Qwen3.6-VL-REAP-26B-A3B-GGUF
MODEL PROFILE

Qwen3.6-VL-REAP-26B-A3B-GGUF

keithnull · Qwen3.6-VL-REAP-26B-A3B-GGUF

GGUF quantizations of atbender/Qwen3.6-VL-REAP-26B-A3B, a REAP-pruned variant of Qwen3.6-VL. **Both the language model (text quants) and the vision tower (mmproj) are included** - drop the mmproj alongside any text quant for full multimodal (image + text) inference. Each text quant was scored against the bf16 reference using `llama-perplexity` on the wikitext-2-raw test split (580 chunks, n_ctx=512, ~297k tokens). Bench run on a single RTX PRO 6000 (Blackwell, 96 GB). On Apple Silicon (llama.cpp, q8_0 KV cache): **Q4_K_M** had the best speed/quality trade-off across both standalone code-gen and agentic tasks. Q3_K_S held up reasonably well on quality at a smaller footprint, and IQ4_XS produced correct outputs but ran noticeably slower in the same harness. Your mileage may vary depending on your hardware and setup.

Visual QAmultimodallicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 09:24:37
OVERVIEW

Model overview

GGUF quantizations of atbender/Qwen3.6-VL-REAP-26B-A3B, a REAP-pruned variant of Qwen3.6-VL. **Both the language model (text quants) and the vision tower (mmproj) are included** - drop the mmproj alongside any text quant for full multimodal (image + text) inference. Each text quant was scored against the bf16 reference using `llama-perplexity` on the wikitext-2-raw test split (580 chunks, n_ctx=512, ~297k tokens). Bench run on a single RTX PRO 6000 (Blackwell, 96 GB). On Apple Silicon (llama.cpp, q8_0 KV cache): **Q4_K_M** had the best speed/quality trade-off across both standalone code-gen and agentic tasks. Q3_K_S held up reasonably well on quality at a smaller footprint, and IQ4_XS produced correct outputs but ran noticeably slower in the same harness. Your mileage may vary depending on your hardware and setup.

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?

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Sources & traceability

huggingface · keithnull/Qwen3.6-VL-REAP-26B-A3B-GGUF ↗2026/10/29 21:12:34
huggingface · enrich:980085af-41df-4e83-9efd-72751affceef ↗2026/10/30 09:24:37

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。