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Explore AI models 85001
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View details ↗Gemma-4-E2B-IT-SFT-RLVR-Medical-GGUF
lukasdrews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-2bit-MLX
lukaskremla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-2bit-MLX-TextOnly
lukaskremla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-3bit-MLX
lukaskremla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-3bit-MLX-TextOnly
lukaskremla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-4bit-MLX-TextOnly
lukaskremla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 ↗cifar10-vit-classifier
luke12121212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 ↗MiniMax-M2.7-NVFP4
lukealonsoNote: If you're experiencing issues with spurious spaces after punctuation, try downgrading transformers to 0.4.67 **MiniMax-M2.7-NVFP4** is an NVFP4-quantized version of MiniMaxAI/MiniMax-M2.7, a 230B-parameter Mixture-of-Experts language model with 10B active parameters. The original model weights were converted from the official FP8 checkpoint to BF16, then quantized to NVFP4 (4-bit with blockwise FP8 scales per 16 elements) using NVIDIA Model Optimizer.
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View details ↗dspv2_guide
LukeBailey181Pub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 ↗nsfw-image-detector
LukeJacob2023This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8138 - Accuracy: 0.9316 - Accuracy K: 0.9887 A custom dataset about 28k images, if you need to improve your domain's accurate, you can contribute the dataset to me. The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP
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View details ↗chime-sms-2m
lukeslp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 ↗drummer2-540m
lukeslp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 ↗Qwen3.5-9B-abliterated
lukey03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 ↗Qwen3.5-9B-abliterated-GGUF
lukey03GGUF quantized versions of lukey03/Qwen3.5-9B-abliterated for use with Ollama, llama.cpp, and other GGUF-compatible inference engines. Qwen3.5 is **natively multimodal** — vision is built into every Qwen3.5 model via early fusion training. There is no separate "VL" variant. The vision GGUF was created by: 1. Starting with the official `Qwen/Qwen3.5-9B` GGUF (883 tensors: 427 text + 441 vision + 15 MTP) 2. Replacing 400 text model tensors with abliterated weights (the remaining 27 text tensors use different quantization types and are not affected by abliteration — they target `attn_qkv` and `attn_v`, while abliteration only modifies `o_proj`/`output_proj` and `down_proj`) 3. Keeping all 441 vision encoder tensors and 15 MTP (multi-token prediction) tensors from the official model
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View details ↗This is a fully uncensored version of Qwen3.5-9B with **all refusal behavior removed** using a two-stage approach: 3 iterative passes of orthogonal projection (Arditi et al., 2024) followed by LoRA fine-tuning on stubborn refusal categories. MLX runs natively on Apple Silicon with unified memory — no GPU/CPU copying overhead, making it the fastest option for Mac users. See the full model card for complete methodology, benchmarks, example outputs, and system prompt recommendations.
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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 ↗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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luma_video_api
LumaLuma AI API 文生视频，支持上传2张参考图片；可以打造出 Luma 官网同款平台，支持官网所有操作; 兼容 GoAmz; SVIP、SSVIP 分组为快速模式，价格略高，大概40秒出视频，排队优先无水印；VIP、VVIP 分组为慢速模式，价格略低，大概5分钟出视频 点击查看接入文档
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View details ↗Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF
lumen-modelsEste repositorio contiene el modelo **Qwen 2.5 7B Instruct (v2)**, liberado de censura mediante la avanzada técnica *Abliterated* por `huihui-ai`, y convertido al formato ligero **GGUF** (Cuantización **Q4_K_M**) por **Lumen Models**. Está optimizado para ejecutarse de forma 100% local, privada y eficiente en ordenadores comunes (8GB - 16GB de RAM) utilizando programas gratuitos como **LM Studio**, **Ollama**, **Jan** o **Faraday**. 1. Descarga el archivo `.gguf` de la pestaña **Files and versions**. 2. Ábrelo en tu software favorito: * **LM Studio:** Arrastra el archivo a la carpeta de modelos y cárgalo. * **Ollama:** Crea un `Modelfile` apuntando a este archivo. 3. ¡Disfruta de una IA inteligente, rápida y totalmente libre en tu PC!
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View details ↗gnani-evon-v3.3-30B-A3B-GGUF
luminenio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 ↗LuminoLex-1.7B
LuminoLex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 ↗Llama-Poro-2-70B-Instruct
LumiOpen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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