This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL dataset for 200 epochs, using the TimeSformer architecture to process video sequences.

Computer VisionTransformers

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL100 dataset for 200 epochs and has the ability to recognize video actions.

Computer VisionTransformers

This is a video understanding model fine-tuned on an unknown dataset based on the VideoMAE-base architecture, specifically designed for sign language recognition tasks. The model achieved an accuracy of 18.64% after 20 training epochs.

Computer VisionTransformers

This is a video understanding model based on the VideoMAE-base architecture, which has been fine-tuned for 20 epochs on an unknown dataset. The model's performance on the evaluation set is limited, with an accuracy of 0.0041 and a loss value of 7.7839.

Computer VisionTransformers

This is a video understanding model fine-tuned on an unknown dataset based on the MCG-NJU/videomae-base model. After 20 epochs of training, it achieved an accuracy of 13.31% on the evaluation set. This model is specifically optimized for video analysis tasks.

Computer VisionTransformers

This is a video action recognition model fine-tuned on the WLASL dataset based on the VideoMAE-Base architecture. After 200 epochs of training, it achieved a top-1 accuracy of 52.96% and a top-5 accuracy of 79.88% on the evaluation set, specifically designed for sign language action recognition tasks.

Computer VisionTransformers

metatune-gpt20b is a large language model prototype with self-improvement ability. It can generate new data for itself, evaluate its own performance, and adjust hyperparameters according to improvement indicators. This model performs excellently in scientific and mathematical understanding at the postdoctoral level and can also be used for coding tasks.

Natural Language ProcessingTransformersEnglish

This is a fine-tuned version of the Qwen-Coder-14B model on the network dataset, specifically optimized for network data scenarios. It performs better in specific network tasks compared to the base version. The model was trained for 3 epochs with a learning rate of 5e-5.

Natural Language ProcessingTransformers

This is the first - generation powerful vibe - code large language model, optimized to directly generate natural language and code completion based on loosely structured 'atmosphere coding' prompts. Compared with early models, it has lower prompt engineering overhead, smoother latent space interpolation, and is easier to guide the generation of usable code.

Natural Language ProcessingTransformersEnglish

This model is based on GPT-OSS-20B and fine-tuned using the Unsloth reinforcement learning framework. The aim is to optimize inference efficiency and reduce vulnerabilities that occur during reinforcement learning from human feedback (RLHF) training. The fine-tuning process focuses on the robustness and efficiency of alignment, ensuring that the model maintains inference depth without incurring excessive computational overhead.

Natural Language ProcessingTransformersEnglish

simpletuner-lora is a LoRA fine-tuning model based on PEFT, derived from stabilityai/stable-diffusion-3.5-large, specifically designed for text-to-image and image-to-image conversion tasks. This model has been trained for 162 epochs, featuring efficient inference capabilities and good image generation quality.

MultimodalDiffusers

A text-to-image model based on the Stable Diffusion XL architecture,擅长生成高度逼真的图像，特别在人脸和手部细节处理上表现出色。

Computer VisionDiffusersEnglish

This is a Korean natural language processing model based on the 🤗 Transformers library, trained for one epoch on the public Korean dataset from Hugging Face. It is suitable for various Korean NLP tasks.

Natural Language ProcessingTransformers

A Stable Diffusion model fine-tuned on epic images of spaceships and space stations, capable of generating high-quality images of space machinery and complex mechanical structures.

Computer VisionDiffusers

TinyLLaVA-Video-R1 is a small-scale video reasoning model based on the traceable training model TinyLLaVA-Video. It significantly enhances reasoning and thinking abilities through reinforcement learning and exhibits the emergent property of 'epiphany moments'.

MultimodalTransformers

Gemma 3 is a lightweight open-source multimodal model introduced by Google, built on the same technology as Gemini, supporting text and image inputs to generate text outputs.

MultimodalTransformersEnglish

viⓍTTS is a voice generation model capable of cloning voices into different languages using a 6-second short audio clip.

A Telugu text-to-speech model fine-tuned based on Microsoft's SpeechT5 architecture, optimized for the unique phoneme structure and text patterns of Telugu.

Audio ProcessingTransformersOther

LLaVAction is a multimodal large language model for action recognition, based on the Qwen2 language model, trained on the EPIC-KITCHENS-100-MQA dataset.

MultimodalTransformersEnglish

SegFormer model optimized for coral reef semantic segmentation tasks, fine-tuned on the Coralscapes dataset at 1024x1024 resolution

Computer VisionTransformers