This Hub repository contains a HuggingFace's transformers implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Use the code below to get started with the…
Open weights
mit
232M parameters
1,024 tokens
transformers
SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited. SmolVLM2 can be used for inference on multimodal (video / image / text) tasks where the input consists of text queries along with video or one or more images.…
Open weights
apache-2.0
507M parameters
8,192 tokens
transformers
This is our first dedicated retriever for financial time-series forecasting, Financial TimeSeries Retriever (FinSeer). Paper or resources for more information: https://arxiv.org/pdf/2502.05878 The primary use of FinSeer is research on financial time-series forecasting using retrieval-augmented generation (RAG) framework. Install Package pip install InstructorEmbedding pip install -U FlagEmbedding pip install sentence-transformers==2.2.2 pip install protobuf==3.20.0 pip install yahoo-finance python -m pip install -U angle-emb pip install transformers==4.33.2 # UAE This repository and its contents are provided for academic and educational purposes only. None of the material constitutes…
Access requested at publisher
109M parameters
transformers
Model · Image and text to text
Zoushun
Scaling Agentic Data for Long-Horizon Terminal Intelligence TerminalHorizon is a fully automated data synthesis engine for long-horizon terminal agents. Grounded in real-world professional work, it constructs executable environments and generates training trajectories that connect agentic behavioral patterns across stages toward a shared goal. This repository releases TerminalHorizon-35B-A3B, obtained by supervised fine-tuning Qwen3.5-35B-A3B on The terminal is a shared interaction interface, not a restriction to software tasks. The training environments span 25 professional domains and 463 subdomains, including software engineering, machine learning, education, agriculture, healthcare…
Open weights
apache-2.0
6M parameters
262,144 tokens
transformers
Model · Image and text to text
Zoushun
Scaling Agentic Data for Long-Horizon Terminal Intelligence TerminalHorizon is a fully automated data synthesis engine for long-horizon terminal agents. Grounded in real-world professional work, it constructs executable environments and generates training trajectories that connect agentic behavioral patterns across stages toward a shared goal. This repository releases TerminalHorizon-27B, obtained by supervised fine-tuning Qwen3.5-27B on The terminal is a shared interaction interface, not a restriction to software tasks. The training environments span 25 professional domains and 463 subdomains, including software engineering, machine learning, education, agriculture, healthcare, finance…
Open weights
apache-2.0
3M parameters
262,144 tokens
transformers
This model is a fine-tuned version of Qwen/Qwen3.8-27B on the on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 16 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 128 - totalevalbatchsize: 128 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
Open weights
other
3M parameters
262,144 tokens
transformers
