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GGUF builds of darrellbest/Qwen-Image-2.1-PE-I2I-Heretic, the refusal-ablated image-editing prompt rewriter: it reads the image you are editing and turns a short instruction into a detailed edit prompt. For **llama.cpp** and **Ollama**, including machines with no CUDA GPU and ARM machines such as the DGX Spark. > **Not affiliated with or endorsed by Alibaba / Qwen.** Derived from > `Qwen/Qwen-Image-2.1-PE-I2I` under the **Qwen Research > License** (copy included). **Non-commercial use only.** **`system_prompt.txt` is included and required.** It defines the output contract, and the model is useless without it.

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GGUF builds of darrellbest/Qwen-Image-2.1-PE-T2I-Heretic, the refusal-ablated text-to-image prompt rewriter: it turns a short request into a detailed prompt plus a recommended aspect ratio. For **llama.cpp** and **Ollama**, including machines with no CUDA GPU and ARM machines such as the DGX Spark. > **Not affiliated with or endorsed by Alibaba / Qwen.** Derived from > `Qwen/Qwen-Image-2.1-PE-T2I` under the **Qwen Research > License** (copy included). **Non-commercial use only.** **`system_prompt.txt` is included and required.** It defines the output contract, and the model is useless without it.

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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.

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GGUF builds of darrellbest/Qwen3.5-4B-Heretic. Qwen/Qwen3.5-4B with its refusal behaviour removed by Heretic using full-weight Arbitrary-Rank Ablation: **6/100 refusals** (original: 99/100) at **KL divergence 0.0220**. See the main repository for how it was made and measured. Converted from the bf16 safetensors with llama.cpp's `convert_hf_to_gguf.py` and quantized with `llama-quantize` (no imatrix). The mmproj file carries the vision encoder; load it alongside any of the three for image input. Every file was loaded in `llama-server` with the mmproj and in Ollama. All three answered ordinary prompts correctly (BF16 and Q8_0 reproduce each other word for word), described a test image (a red circle and a blue square) correctly, and in Ollama answered 17 x 23 = 391 with thinking off. Thinking-mode reasoning, 4 arithmetic and word problems x 10 seeds with Qwen's recommended sampling: BF16 39/40, Q8_0 40/40, Q4_K_M 40/40 finished and correct (the original model in vLLM: 40/40).

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GGUF builds of darrellbest/Qwen3.5-9B-Heretic. Qwen/Qwen3.5-9B with its refusal behaviour removed by Heretic using full-weight Arbitrary-Rank Ablation: **5/100 refusals** (original: 100/100) at **KL divergence 0.0403**. See the main repository for how it was made and measured. Converted from the bf16 safetensors with llama.cpp's `convert_hf_to_gguf.py` and quantized with `llama-quantize` (no imatrix). The mmproj file carries the vision encoder; load it alongside any of the three for image input. Every file was loaded in `llama-server` with the mmproj and in Ollama. All three answered ordinary prompts correctly, described a test image (a red circle and a blue square) correctly, and in Ollama answered 17 x 23 = 391 with thinking off. Thinking-mode reasoning, 4 arithmetic and word problems x 10 seeds with Qwen's recommended sampling: BF16 39/40, Q8_0 37/40, Q4_K_M 35/40 finished and correct (the original model in vLLM: 40/40); the misses ran out of the 4,000-token budget rather than answering wrongly.

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GGUF builds of darrellbest/Qwen3.8-27B-Heretic: Qwen3.8-27B with its refusal behaviour removed (0/100 refusals vs 98/100 for the original, KL divergence 0.0465). The three model files are text-only: this is a vision-language model, and llama.cpp keeps the vision encoder in the separate `mmproj` file. Add it only if you want to send images. The included `Modelfile` sets `RENDERER qwen3.8` and `PARSER qwen3.5`, Ollama's built-in support for this architecture. They matter: without them the model's reasoning text ends up inside the answer, and generation was measured about half as fast (9.5 vs 18.8 tokens/s on an RTX PRO 6000 with Q4_K_M).

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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