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AI Models 450

  • h2ovl-mississippi-800m↗

    h2oai

    The H2OVL-Mississippi-800M is a compact yet powerful vision-language model from H2O.ai, featuring 0.8 billion parameters. Despite its small size, it delivers state-of-the-art performance in text recognition, excelling in the Text Recognition segment of OCRBench and outperforming much larger models in this domain. Built upon the robust architecture of our H2O-Danube language models, the Mississippi-800M extends their capabilities by seamlessly integrating vision and language tasks. - 0.8 Billion Parameters: Balance between performance and efficiency, making it suitable for OCR and document processing. - Trained on 19 million image-text pairs, with a focus on OCR, document comprehension, and chart, figure, and table interpretation, the model is optimized for superior OCR performance. This guide demonstrates how to create prompts for extracting information and converting it into structured JSON outputs. It starts with basic examples and progresses to more complex JSON structures, including handling data from images of tables and charts. The objective is to help users design effective prompts that can be used in various applications, such as natural language processing, chatbots, or data extraction from visual inputs.

  • Llama-3.1-Nemotron-Nano-4B-v1.1↗

    NVIDIA

    Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) which is a derivative of nvidia/Llama-3.1-Minitron-4B-Width-Base, which is created from Llama 3.1 8B using our LLM compression technique and offers improvements in model accuracy and efficiency. It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. Llama-3.1-Nemotron-Nano-4B-v1.1 is a model which offers a great tradeoff between model accuracy and efficiency. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K. This model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and RPO checkpoints

  • chinese-hubert-base↗

    TencentGameMate

    Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.

  • blt-1b-hf↗

    itazap

    **BLT (Byte Latent Transformer)** is a tokenizer-free transformer architecture that operates directly on raw byte sequences. Instead of processing text token by token, BLT dynamically groups bytes into **entropy-based patches**, enabling more efficient and scalable processing for byte-level tasks. Key components: - **Local Encoder → Latent Transformer → Local Decoder** architecture. - **Entropy-based patcher (BltPatcher)**: scans byte streams and creates patches when entropy thresholds are met. - **Hash n-gram embeddings**: maintain contextual information over neighboring bytes. BLT achieves competitive performance compared to traditional token-based transformers, supporting multilingual, noisy, or mixed-script input.

  • openai-gpt↗

    openai-community

    - Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** `openai-gpt` (a.k.a. "GPT-1") is the first transformer-based language model created and released by OpenAI. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies. - **Developed by:** Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. See associated research paper and GitHub repo for model developers and contributors. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** MIT License - **Related Models:** GPT2, GPT2-Medium, GPT2-Large and GPT2-XL - **Resources for more information:** - Research Paper - OpenAI Blog Post - GitHub Repo - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt

  • siglip2-person-description-reid↗

    MarketaJu

    This model is part of the family of SigLIP models finetuned for **person visual description and retrieval**. The splits are created as follows: - **CUHK-PEDES**: **test**: 1000 identities following the original split; **validation**: 150 identities sampled from the original validation split; **train**: all remaining identities - **ICFG-PEDES**: **test**: 946 identities following the original split; **validation**: 150 identities sampled from the original training split; **train**: all remaining identities - **IIITD**: **test**: 2500 identities following the original split; **validation**: 150 identities sampled from the original validation split; **train**: all remaining identities - **ITCPR**: Originally designed for zero-shot evaluation, so a new split is created:**test**: 1000 randomly selected identities; **validation**: 150 randomly selected identities; **train**: all remaining identities - **PRW**: **test**: 450 identities following the original split; **validation**: 150 identities sampled from the original training split; **train**: all remaining identities - **PETA**: The original random image-level split can cause identity leakage. Therefore, the identity-based split from PETA-ZS is adopted: **test**: 1706 identities following the original split; **validation**: 150 identities sampled from the original validation split; **train**: all remaining identities - **RSTPReid**: Only the 200 test images are used for evaluation due to annotation errors in the rest of the dataset. - **SYNTH**: 1000 random images are used for the test set because the generated captions are noisy and sometimes inaccurate. The model is evaluated on data mentioned above. The following table summarizes the number of identities, images, and queries for each subset. The final test set is a merge of all subsets.

  • chinese-hubert-large↗

    TencentGameMate

    Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.

  • OLMoE-1B-7B-0125-Instruct↗

    allenai

    OLMoE-1B-7B-0125-Instruct January 2025 is post-trained variant of the OLMoE-1B-7B January 2025 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset and further DPO training on this dataset, and finally RLVR training using this data. Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. Check out the OLMoE paper or Tülu 3 paper for more details! OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The core models released in this batch include the following: - **Model type:** A model trained on a mix of publicly available, synthetic and human-created datasets. - **Language(s) (NLP):** Primarily English - **License:** Apache 2.0 - **Finetuned from model:** allenai/OLMoE-1B-7B-0125-DPO

  • Apertus-v1.1-0.5B↗

    swiss-ai

    Apertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments. The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. To achieve high performance with a minimized memory footprint, this model uses tied embeddings and a deeper, thinner architectural design. Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses.

  • Anima↗

    circlestone-labs

    Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content. The model is designed for making illustrations and artistic images, and will not work well at realism. It is trained on several million anime images and about 800k non-anime artistic images. No synthetic data was used for training. The knowledge cut-off date for the anime training data is September 2025. - Anima-Base - The pretrained, unrefined base model. Maximum flexibility, diversity, and style adherence. - LoRAs should be trained using this version. - Anima-Aesthetic - Fine-tuned for better consistency and a higher quality default art style. - v1.0b is an alternate version that is just an aesthetics full finetune, without additional style adjustment and stabilization loras merged in like 1.0. I personally think 1.0 is better. - Anima-Turbo - Distilled version for fast generations. - Use at CFG 1 and 8-12 steps. - The distillation process also increases stability and gives the model a strong default style, but reduces diversity.