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…
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9.4B parameters
262,144 tokens
LucidVitality-9B is a little goer. It's a roleplay or creative focused merge of two Qwen3.5 9b variants for people with absolute potatoes, like myself. It marries the improved prose of Darkhn's Qwen3.5-9B-Animus-V13.0 with the lower looping, higher EOS exit, and slightly more coherency (compared to base) from Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED of DavidAU's making. I haven't merged anything for a long time, as it's been a hard time for finetuning. They rarely increase prose quality, often deeply lose intelligence over base (even when tuned for intelligence or agentic). Base model's getting tough to beat. So it was a pleasant surprise to find two models that each…
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9.4B parameters
262,144 tokens
transformers
Send a text or JSON state and your questions: choice picks from up to 255 options, noul is yes/no, and score takes 2–10 ordered levels. Each question gets probabilities over its offered options from one forward pass, with no generated text. Long or large multi-question requests may use several batches. Personal research release by thegovind, not an official product of any company. No affiliation with TypeSafe AI, Xiaomi, Alibaba Cloud or the Qwen team. Weights are for non-commercial research; see Licence. We ran the full Decision Index 0.2 suite ourselves with the official scoring kit at commit 19ad28e on 2026-09-25. This is a descriptive run, not a leaderboard submission or accepted…
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9.4B parameters
262,144 tokens
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Vinci Bozza is a 9-billion-parameter open-weight model, fine-tuned from Qwen 3.5-9B with SimpleDirect's Constitution and character training. It is a disposition tune, not a capability retrain. We did not try to make the base model smarter. We tried to make it more honest — and then we measured what that cost. Safer and more honest on the measures below, with general knowledge holding. Strict instruction-following, tool abstention and multi-turn task-holding paid for it. All of it is below, at the same prominence as the gains. The rows above are configuration and artifact values read out of this repository, not validated results. A context length in a config file is a limit the model will…
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apache-2.0
9.4B parameters
262,144 tokens
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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…
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9.4B parameters
262,144 tokens
This model is a fine-tuned version of Qwen/Qwen3.5-9B-Base on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
Open weights
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9.4B parameters
262,144 tokens
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