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Explore AI models 84999
hypa-tiny-keys
hypaai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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hyper-accel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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hyper-accelThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗ci-random-gpt2-350m
hyper-accel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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hyper-accel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 ↗ci-random-llama3-8b
hyper-accelThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗ci-random-pygwin-4x7b
hyper-accel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 ↗ci-random-qwen2-moe-a3b
hyper-accelThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗ci-random-tinyllama-1b
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View details ↗ci-random-vit-large-patch16-224
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View details ↗tiny-random-cohere
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View details ↗tiny-random-gpt_bigcode
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View details ↗tiny-random-gpt2
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View details ↗tiny-random-olmo
hyper-accel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 ↗tiny-random-qwen2
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View details ↗tiny-random-starcoder2
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View details ↗Qwen3.5-9B-fp8
Hyper-AI**lightvl Developed by Myself is a lightweight Vision-Language Model (VLM) quantization toolkit supporting FP8, INT8, FP8-Block. It integrates with vLLM for high-throughput inference and supports Qwen3-VL, Qwen3.5, InternVL-Chat, and Gemma-4 models.** > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
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Hyper-SD
字节跳动Official Repository of the paper: *Hyper-SD*. * Aug.26, 2024. 💥💥💥 Our 8-steps and 16-steps **FLUX.1-dev-related LoRAs** are available now! We recommend LoRA scales around 0.125 that is adaptive with training and guidance scale could be kept on 3.5. Lower step LoRAs would be coming soon. 💥💥💥 * Aug.19, 2024. SD3-related CFG LoRAs are available now! We recommend setting guidance scale to 3.0/5.0/7.0 at 4/8/16-steps. Don't forget to fuse lora with a relatively small scale (e.g. 0.125 that is adaptive with training) before inference with diffusers. Note that 8-steps and 16-steps LoRA can also inference on a little bit smaller steps like 6-steps and 12-steps, respectively. Hope to hear your feedback, FLUX-related models will be coming next week. * May.13, 2024. The 12-Steps CFG-Preserved Hyper-SDXL-12steps-CFG-LoRA and Hyper-SD15-12steps-CFG-LoRA is also available now(support 5~8 guidance scales), this could be more practical with better trade-off between performance and speed. Enjoy! * Apr.30, 2024. Our 8-Steps CFG-Preserved Hyper-SDXL-8steps-CFG-LoRA and Hyper-SD15-8steps-CFG-LoRA is available now(support 5~8 guidance scales), we strongly recommend making the 8-step CFGLora a standard configuration for all SDXL and SD15 models!!! * Apr.28, 2024. ComfyUI workflows on 1-Step Unified LoRA 🥰 with TCDScheduler to inference on different steps are released! Remember to install ⭕️ ComfyUI-TCD in your `ComfyUI/custom_nodes` folder!!! You're encouraged to adjust the eta parameter to get better results 🌟! * Apr.26, 2024. Thanks to @Pete for contributing to our scribble demo with larger canvas right now 👏. * Apr.24, 2024. The ComfyUI workflow and checkpoint on 1-Step SDXL UNet ✨ is also available! Don't forget ⭕️ to install the custom scheduler in your `ComfyUI/custom_nodes` folder!!! * Apr.23, 2024. ComfyUI workflows on N-Steps LoRAs are released! Worth a try for creators 💥! * Apr.23, 2024. Our technical report 📚 is uploaded to arXiv! Many implementation details are provided and we welcome more discussions👏. * Apr.21, 2024. Hyper-SD ⚡️ is highly compatible and work well with different base models and controlnets. To clarify, we also append the usage example of controlnet here. * Apr.20, 2024. Our checkpoints and two demos 🤗 (i.e. SD15-Scribble and SDXL-T2I) are publicly available on HuggingFace Repo. Hyper-SD Scribble demo host on 🤗 scribble
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View details ↗Qwen3.6-35B-A3B-W4A16-AWQ
hyperhuzaifa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 ↗diffusiongemma-26B-A4B-iq2xxs-GGUF
hyperspaceai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 ↗Juggernaut-Reborn
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