Darrellbest

The image-editing prompt rewriter for Qwen-Image-2.1, a fine-tuned Qwen3.5-VL 9B that turns a short edit instruction plus 1–N input images into a detailed English edit prompt, with its refusal behaviour removed by Heretic directional ablation. bf16, same shapes and parameter count as the source; nothing else was changed. systemprompt.txt is included and required. It defines the output format. It is the unmodified file from the source repo. The second row is an independent evaluation of the exported weights with Heretic's evaluatemodel. Refusals were measured on mlabonne/harmfulbehaviors and KL divergence (damage to ordinary behaviour, first-token distributions) on mlabonne/harmlessalpaca…

The text-to-image prompt rewriter for Qwen-Image-2.1, a fine-tuned Qwen3.5-VL 9B that turns a short request into a detailed English prompt plus a recommended aspect ratio, with its refusal behaviour reduced by Heretic directional ablation. bf16, same shapes and parameter count as the source. systemprompt.txt is included and required (the unmodified file from the source repo). for image edits. Measured by Heretic on mlabonne/harmfulbehaviors (refusals) and mlabonne/harmlessalpaca (KL divergence, the damage to ordinary behaviour), with Heretic's default system prompt. The first row is an independent evaluation of the exported weights (evaluatemodel); the search's own figure for the same trial…

NVFP4 (4-bit floating point, W4A4) build of darrellbest/Qwen-Image-2.1-PE-I2I-Heretic, the refusal-ablated image-editing prompt rewriter for Qwen-Image-2.1. For vLLM on NVIDIA Blackwell GPUs, which run NVFP4 natively. 11 GB instead of 18 GB. systemprompt.txt is included and required, exactly as for the original. The linear-attention layers carry a recurrent state and the vision tower encodes the input image; both were left in bf16, as other quantizations of this model family do. That is why the file is 11 GB rather than ~6 GB. Made with llm-compressor 0.13.0 (QuantizationModifier, scheme="NVFP4"), calibrated on 64 samples in the model's real input format: its own system prompt, an edit…

NVFP4 (4-bit floating point, W4A4) build of darrellbest/Qwen-Image-2.1-PE-T2I-Heretic, the refusal-ablated text-to-image prompt rewriter for Qwen-Image-2.1. For vLLM on NVIDIA Blackwell GPUs, which run NVFP4 natively. 11 GB instead of 18 GB. systemprompt.txt is included and required, exactly as for the original. The linear-attention layers carry a recurrent state and the vision tower encodes the input image; both were left in bf16, as other quantizations of this model family do. That is why the file is 11 GB rather than ~6 GB. Made with llm-compressor 0.13.0 (QuantizationModifier, scheme="NVFP4"), calibrated on 64 samples in the model's real input format: its own system prompt and a short…

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. systemprompt.txt is included and required — it defines the output contract, and the model is useless without it. Ollama packs the file into a single model, so nothing extra is needed at run time. converthftogguf.py from llama.cpp (bd4f514), then llama-quantize. Converting this architecture needed a one-line fix: the Qwen converter reads mtpnumhiddenlayers with a default, but these checkpoints set it to…

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. systemprompt.txt is included and required — it defines the output contract, and the model is useless without it. The vision file. mmproj-F16.gguf is how this model reads the input image. GGUF keeps it in a separate file; both loaders take the pair and behave as one model. Ollama packs the two files into a single model, so nothing extra is needed at run time. converthftogguf.py from llama.cpp…