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AI Models 2119

  • PP-OCRv3_mobile_det↗

    PaddlePaddle

    PP-OCRv3_mobile_det is one of the PP-OCRv3_det series models, a set of text detection models developed by the PaddleOCR team. This mobile-optimized text detection model offers higher efficiency, making it ideal for deployment on edge devices. You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine. The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.

  • pe-av-large↗

    facebook

    PE-AV is a state-of-the-art multimodal model that embeds audio, video, audio-video, and text into a joint embedding space. The model enables powerful cross-modal retrieval and understanding across audio, video, and text modalities. PE-AV is trained using contrastive learning to align audio, video, and text representations in a shared embedding space. The model can encode: - **Audio only**: Extract audio embeddings from audio waveforms - **Video only**: Extract visual embeddings from video frames - **Audio-Video**: Extract joint audio-visual embeddings - **Text**: Extract text embeddings optimized for different modality pairs The `-16-frame` variants sample exactly 16 frames (evenly spaced apart) from each video, while the base variants support variable-length videos.

  • snac_24khz↗

    hubertsiuzdak

    Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate. 👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models. SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently, covering a broader time span.

  • Florence-2-large↗

    Microsoft

    **This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8** 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.

  • potion-code-16M↗

    minishlab

    **This model is superseded by potion-code-16M-v2. We recommend using that instead for better performance at the same size.** **potion-code-16M** is a fast static code embedding model optimized for code retrieval tasks. It powers Semble, a code search library for agents. It is distilled from nomic-ai/CodeRankEmbed and trained on the CornStack code corpus using Tokenlearn and contrastive fine-tuning. It uses static embeddings, allowing text and code embeddings to be computed orders of magnitude faster than transformer-based models on both GPU and CPU.

  • mobilesam↗

    nielsr

  • PP-OCRv4_mobile_det↗

    PaddlePaddle

    PP-OCRv4_mobile_det is one of the PP-OCRv4_det series models, a set of text detection models developed by the PaddleOCR team. This mobile-optimized text detection model offers higher efficiency, making it ideal for deployment on edge devices. Its key accuracy metrics are as follows: You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine. The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.

  • Ternary-Bonsai-2-27B-262k-GGUF↗

    CaryPalmer

    **A 27B model with its whole trained context window, at q8_0 KV precision, on one RTX 4070 12 GB. 78 tok/s of new text at 32k tokens of context (175 on code), 69 at 64k, 61 at 112k, and the window keeps going to 262k. Lossless speculative decoding at every depth. A server that answers the requests agents and apps actually send. One command on Windows.** This is PrismML's Ternary Bonsai 2 27B (1.58-bit ternary `PTQ1_0`, 5.9 GB, Apache 2.0), the same weights, nothing re-quantized, with its MTP draft head grafted back on (byte-identical trunk, proof below) and the serving stack from github.com/professorpalmer/bonsai-ada-surgery: a tiered KV cache that puts the first ~119k positions in VRAM and the rest in pinned system RAM with bit-identical output, MTP drafting that pays at every depth, harness-proofing for the apps that send `effort: "high"` or tiny output caps, and an optional server-side layer (exact API cards, an API check, a sandboxed Python tool) that lifts the model's coding and agentic scores without touching the weights. Measured 2026-10-08 (`bench/depth_table.sh`), a fresh prompt at each depth: 400-token greedy answers with the default drafting (lookup + MTP head); new text = an essay, code = a Python module; prefill = cumulative for the whole prompt; display on the CPU's iGPU. Every number has a receipt in the repository (`docs/Q8_FULL_CONTEXT.md`, `docs/RECEIPTS.md`, `docs/QUALITY.md`).

  • pythia-1.4b↗

    EleutherAI

    The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card lists the changes ; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are still available, but we suggest the retrained suite if you are just starting to use Pythia. **This is the current release.**

  • normistral-7b-warm↗

    ltg

    NorMistral-7b-warm is a large Norwegian language model initialized from Mistral-7b-v0.1 and continuously pretrained on a total of 260 billion subword tokens (using six repetitions of open Norwegian texts). This model is a part of the NORA.LLM family developed in collaboration between the Language Technology Group at the University of Oslo, the High Performance Language Technologies (HPLT) project, the National Library of Norway, and the University of Turku. All the models are pre-trained on the same dataset and with the same tokenizer. NorMistral-7b-warm has over 7 billion parameters and is based on the Mistral architecture. The NORA.LLM language model family includes (as of now): - **NorMistral-7b-warm** -- an LLM initialized from Mistral-7b-v0.1 and continuously pretrained on Norwegian data; - **NorMistral-7b-scratch** -- a Mistral-based LLM pretrained from scratch on Norwegian data; - **NorBLOOM-7b-scratch** -- a BLOOM-based LLM pretrained from scratch on Norwegian data.