LFM2.5-2.6B-RLCD
monotykamary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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Explore AI models 85003
bert-base-thai
monsoon-nlp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 ↗hindi-bert
monsoon-nlp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 ↗control_v1p_sd15_qrcode_monster
monster-labsThis model is made to generate creative QR codes that still scan. Keep in mind that not all generated codes might be readable, but you can try different parameters and prompts to get the desired results. Introducing the upgraded version of our model - Controlnet QR code Monster v2. V2 is a huge upgrade over v1, for scannability AND creativity. As with the former version, the readability of some generated codes may vary, however playing around with parameters and prompts could yield better results.
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View details ↗control_v1p_sdxl_qrcode_monster
monster-labsThis model is made to generate creative QR codes that still scan. Illusions should also work well. Keep in mind that not all generated codes might be readable, but you can try different parameters and prompts to get the desired results. Feel free to experiment with prompts, parameters, and the Image-to-Image feature to achieve the desired QR code output. Good luck and have fun!
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View details ↗gpt2_alpaca-lora
monsterapi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 ↗SECourses_Rock
MonsterMMORPG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 ↗Wan_GGUF
MonsterMMORPGI am keeping this list up-to-date. I got upcoming new awesome video ideas. Trying to find time to do that. **I am open to any criticism you have. I am constantly trying to improve the quality of my tutorial guide videos. Please leave comments with both your suggestions and what you would like to see in future videos.** **All videos have manually fixed subtitles and properly prepared video chapters. You can watch with these perfect subtitles or look for the chapters you are interested in.**
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View details ↗english_us_arpa
MontrealCorpusTools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 ↗mandarin_mfa
MontrealCorpusTools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 ↗This repository contains the OpenVLA-OFT checkpoint for LIBERO-Long (also called LIBERO-10), as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?search_models=oft This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.
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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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View details ↗This repository contains the OpenVLA-OFT checkpoint for LIBERO-Spatial, as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.
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View details ↗This repository contains the OpenVLA-OFT checkpoint trained on 4 LIBERO task suites combined (-Spatial, -Object, -Goal, -Long), as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?search_models=oft This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.
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View details ↗mlx-qwen3-asr-1.7b-8bit
moona3k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 ↗moondream-2b-2025-04-14-4bit
moondreamThis repository contains the 2025-04-14 **4-bit** release of Moondream. On an Nvidia RTX 3090, it uses 2,450 MB of VRAM and runs at a speed of 184 tokens/second. We used quantization-aware training techniques to build this version of the model, allowing us to achieve a 42% reduction in memory usage with only an 0.6% drop in accuracy. There's more information about this version of the model in our release blog post. Other revisions, as well as release history, can be found here.
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View details ↗moondream2-gguf
moondream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 ↗Moondream 3.1
moondreamMoondream 3.1 is a vision-language model from Moondream, the successor to Moondream 3 Preview, with 9B total parameters and 2B active (mixture-of-experts). It handles visual question answering, captioning, OCR, and...
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$0.3 / M input tokensOpenRouter · moondream/moondream3.1-9b-a2b
View details ↗Moondream 3 Preview
moondreamMoondream 3 Preview is a vision-language model from Moondream with 9B total parameters and 2B active (mixture-of-experts). It handles visual question answering, captioning, OCR, and visual reasoning over images, with...
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$0.3 / M input tokensOpenRouter · moondream/moondream3-preview
View details ↗parakeet-redux
moondreamA 1.58-bit version of parakeet-tdt-0.6b-v3. Same architecture, same tokenizer, but every encoder weight is -1, 0 or +1. It fits in 178 MB, runs at 113× real time on eight x86 CPU cores, 2.5× the fastest other Parakeet runtime we measured, and stays within 0.3 WER of the original on English while beating it on the 25-language FLEURS set and on long-form audio. Its sibling Parakeet Ultra is the full-precision version of the same architecture, trained further, for GPUs: better than the original on every benchmark. Read the release post for the story behind both models. Run it with Photon, which reads the packed weights directly: AVX-512 VNNI on x86, NEON on ARM, Metal on Apple GPUs. Every speed number on this page is Photon.
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View details ↗parakeet-ultra
moondreamA post-trained version of parakeet-tdt-0.6b-v3. Same architecture, same tokenizer, same 0.6B parameters in full precision. It is better than the original on every benchmark group below, by a wide margin on the 25-language FLEURS set, in background noise and on long-form audio, and it runs faster than the original does in NeMo. Its sibling Parakeet Redux is the ternary version of the same architecture, 178 MB, built for CPUs and Apple silicon. Read the release post for the story behind both models. Run it with Photon. Every accuracy number on this page is Photon on an NVIDIA GPU.
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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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