silver-cedar-d0f4
choogee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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silver-robin-101f
choogee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 ↗swift-cedar-2634
choogee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 ↗yugao
choogee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 ↗Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-4bit-MLX
choppedgarlic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 ↗kompress-base
chopratejas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 ↗kompress-v2-base
chopratejas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 ↗technique-router
chopratejasA fine-tuned MiniLM classifier that routes image queries to optimal compression techniques for the Headroom SDK. This model classifies natural language queries about images into one of four optimization techniques: - **Base examples**: 145 human-written queries - **Expanded dataset**: 1,157 examples (via template expansion + synonyms) - **Split**: 85% train, 15% validation
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View details ↗technique-router-onnx
chopratejas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 ↗yev1-4b
choyiny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 ↗bird-species-classifier
chriamueThe "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species. The model was trained on the "Bird Species" dataset, which is a comprehensive collection of bird images. Key features of this dataset include: These metrics indicate a high level of performance, making the model reliable for practical applications.
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View details ↗flatquant-phi3-mini-4k-g52xlarge
chris320211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 ↗smoothquant-gemma2-2b-g52xlarge
chris320211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 ↗smoothquant-granite33-2b-g52xlarge
chris320211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 ↗smoothquant-qwen25-15b-g52xlarge
chris320211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 ↗minecraft-ai-codellama-34b
chriscelaya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 ↗minecraft-ai-training-tutorial
chriscelaya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 ↗flux1-dev-turbo-GGUF
ChrisColeTech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 ↗flux2-dev-turbo-GGUF
ChrisColeTech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 ↗flux2-klein-GGUF
ChrisColeTech> **What this repo is:** a requantized, split-layout build of the upstream model — weights only, not a retrain. The recommended settings below are the values these weights are actually run with day to day, not copied from the upstream card. **prompt:** `a red apple on a rustic wooden table beside a window, soft daylight, fine skin texture and wood grain, studio photograph, sharp focus` — 1024×1024, 4 steps, guidance 1.0, seed 12345 **prompt:** `portrait of an older fisherman with a weathered face, natural window light, shallow depth of field, photorealistic` — 1024×1024, 4 steps, guidance 1.0, seed 99 **prompt:** `a rain-slicked Tokyo street at night, neon signs reflecting in puddles, cinematic, 35mm photograph` — 1024×1024, 4 steps, guidance 1.0, seed 1234
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View details ↗giga-lora-studio-models
ChrisColeTech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 ↗ideogram4-turbo-GGUF
ChrisColeTech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 ↗krea-2-raw-FP8
ChrisColeTech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 ↗krea2-turbo-edit-GGUF
ChrisColeTech8-step distilled text-to-image and identity-preserving instruction editing — ≈35 s per 1024² image on an RTX 5090, guidance-free. > **What this repo is:** the Krea 2 Turbo Edit denoiser (identity LoRA baked > in) as GGUF at three quant tiers plus a scaled-FP8 build, with the > Qwen3-VL-4B text encoder and Qwen-Image VAE it needs — weights only, not a > retrain. The settings below are the values these weights are actually run > with day to day. **prompt:** `a red apple on a rustic wooden table beside a window, soft daylight, fine skin texture and wood grain, studio photograph, sharp focus` — 1024×1024, 8 steps, guidance 0.0, seed 12345 **prompt:** `portrait of an older fisherman with a weathered face, natural window light, shallow depth of field, photorealistic` — 1024×1024, 8 steps, guidance 0.0, seed 99
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