BugTraceAI-CORE-Ultra-27B-Q4
BugTraceAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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BugTraceAI-CORE-Ultra-27B-Q6
BugTraceAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Gemma-26B-A4B-VisualNovel-GGUF
build-small-hackathon基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nemotron-nano-4b-escape-room
build-small-hackathon基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗CommunityForensics-DeepfakeDet-ViT
buildborderlessVision Transformer (ViT-Small) trained on 2.7M samples across 4,803 generators for detecting AI-generated images. Presented in Community Forensics: Using Thousands of Generators to Train Fake Image Detectors (CVPR 2025). **Uploaded for community validation as part of OpenSight** — An upcoming open-source framework for adaptive deepfake detection. **Project OpenSight HF Spaces coming soon with an eval playground and eventually a leaderboard. Preview:**
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View details ↗FLUX.MF-lightning_8step-uncen
buildborderless基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kanso-Koi-0.8
BuilderHelm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kanso-Satori-1.0-bf16
BuilderHelm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗kairos-door-rain
builtbyd3vBinary image classifier: `rain` vs `no_rain`, for a camera looking out a dock door. Fine-tuned from facebook/convnext-tiny-224 (Apache-2.0). Part of Kairos, a weather plugin for Boston Dynamics Spot fleets (Knight Hacks IX hackathon build). It feeds one input of a GO / HOLD rule. Python: `pipeline("image-classification", model="builtbyd3v/kairos-door-rain")`. Hold when p(rain) >= **0.4447** for q8, or >= **0.4728** for fp32 (each picked on the calibration split, one image per call). Both are in `eval_results.json` under `thresholds`. 95% CIs are Wilson score intervals. Threshold rule: the highest threshold that keeps calibration rain recall >= 95%, rounded down. Scored with onnxruntime CPU on the shipped ONNX files, one image per call (as in the browser).
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View details ↗spot-wetfloor-convnext-tiny
builtbyd3vBinary image classifier: `wet` (visible standing water or a wet surface) vs `dry`. Fine-tuned from facebook/convnext-tiny-224 (Apache-2.0). Built for SPOT Dispatch, a readiness board for Boston Dynamics Spot inspection routes. Spot's user guide says not to walk on wet or slippery surfaces, so the board can flag route photos that look wet for an operator to review. `onnx/model_quantized.onnx` (33.8 MB vs 111.4 MB fp32) loads with `{ dtype: "q8" }`. It is onnxruntime `quantize_dynamic`, uint8 weights, MatMul/Gemm layers only (convs stay fp32). Scored with onnxruntime on the same test split: q8 misses one more same-source wet image and changes 1 of 752 test predictions vs fp32 (kit images: same calls). Use fp32 when recall matters more than load time. Quantizing the convs too (28 MB) lost 2 same-source wet images, so it is not shipped.
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View details ↗DeepSeek-Coder-V2-Instruct-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V2-Chat-0628-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V3-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4-Flash-0731-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4-Flash-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FLUX.1-Kontext-dev-GGUF
bullerwins`FLUX.1 Kontext [dev]` is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions. For more information, please read our blog post and our technical report. You can find information about the `[pro]` version in here. 1. Change existing images based on an edit instruction. 2. Have character, style and object reference without any finetuning. 3. Robust consistency allows users to refine an image through multiple successive edits with minimal visual drift. 4. Trained using guidance distillation, making `FLUX.1 Kontext [dev]` more efficient. 5. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 6. Generated outputs can be used for personal, scientific, and commercial purposes, as described in the [FLUX.1 \[dev\] Non-Commercial License](https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev). We provide a reference implementation of `FLUX.1 Kontext [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 Kontext [dev]` are encouraged to use this as a starting point.
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View details ↗GLM-5.3-Flash-exl3-4bpw-ablit
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Meta-Llama-3.1-405B-Instruct-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Meta-Llama-3.1-70B-Instruct-GGUF
bullerwins基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-4B-Instruct-2507-GGUF
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View details ↗Qwen3-8B-GGUF
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View details ↗translategemma-12b-it-GGUF
bullerwins+ Technical Report + Responsible Generative AI Toolkit + TranslateGemma on Kaggle + TranslateGemma on Vertex Model Garden TranslateGemma is a family of lightweight, state-of-the-art open translation models from Google, based on the Gemma 3 family of models. TranslateGemma models are designed to handle translation tasks across 55 languages. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art translation models and helping foster innovation for everyone. + Text string, representing the text to be translated + Images, normalized to 896 x 896 resolution and encoded to 256 tokens each + Total input context of 2K tokens
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View details ↗translategemma-27b-it-GGUF
bullerwins+ Technical Report + Responsible Generative AI Toolkit + TranslateGemma on Kaggle + TranslateGemma on Vertex Model Garden TranslateGemma is a family of lightweight, state-of-the-art open translation models from Google, based on the Gemma 3 family of models. TranslateGemma models are designed to handle translation tasks across 55 languages. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art translation models and helping foster innovation for everyone. + Text string, representing the text to be translated + Images, normalized to 896 x 896 resolution and encoded to 256 tokens each + Total input context of 2K tokens
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