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Foundation Models

Models in this category

  • AutoML-Image-Classification

    Automated Machine Learning, or AutoML, is a process that automates the repetitive and time-consuming tasks involved in developing machine learning models. This helps data scientists, analysts, and developers to create models more efficiently and with higher quality, resulting in increased product...

  • AutoML-Image-Instance-Segmentation

    Automated Machine Learning, or AutoML, is a process that automates the repetitive and time-consuming tasks involved in developing machine learning models. This helps data scientists, analysts, and developers to create models more efficiently and with higher quality, resulting in increased product...

  • AutoML-Image-Object-Detection

    Automated Machine Learning, or AutoML, is a process that automates the repetitive and time-consuming tasks involved in developing machine learning models. This helps data scientists, analysts, and developers to create models more efficiently and with higher quality, resulting in increased product...

  • AutoML-Named-Entity-Recognition

    Automated Machine Learning, or AutoML, is a process that automates the repetitive and time-consuming tasks involved in developing machine learning models. This helps data scientists, analysts, and developers to create models more efficiently and with higher quality, resulting in increased product...

  • AutoML-Text-Classification

    Automated Machine Learning, or AutoML, is a process that automates the repetitive and time-consuming tasks involved in developing machine learning models. This helps data scientists, analysts, and developers to create models more efficiently and with higher quality, resulting in increased product...

  • bert-base-cased

    BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inpu...

  • bert-base-uncased

    BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate input...

  • bert-large-cased

    BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inpu...

  • bert-large-uncased

    BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inpu...

  • camembert-base

    CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.

It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.

Training Details

Training Data

OSCAR or Open...

  • compvis-stable-diffusion-v1-4

    Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The Stable-Diffusion-v1-4 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 225k steps at resolution 5...

  • databricks-dolly-v2-12b

    Databricks' dolly-v2-12b, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on pythia-12b, Dolly is trained on ~15k instruction/response fine tuning records [databricks-dolly-15k](https://github.com/d...

  • deci-decidiffusion-v1-0

    DeciDiffusion 1.0 is an 820 million parameter latent diffusion model designed for text-to-image conversion. Trained initially on the LAION-v2 dataset and fine-tuned on the LAION-ART dataset, the model's training involved advanced techniques to improve speed, training performance, and achieve su...

  • deepset-minilm-uncased-squad2

    Training Details

Hyperparameters

seed=42
batch_size = 12
n_epochs = 4
base_LM_model = "microsoft/MiniLM-L12-H384-uncased"
max_seq_len = 384
learning_rate = 4e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
grad_acc_steps=4

Evaluation Res...

Training Details

Hype...

When fine-tuned fo...

  • facebook-deit-base-patch16-224

    DeiT (Data-efficient image Transformers) is an image transformer that do not require very large amounts of data for training. This is achieved through a novel distillation procedure using teacher-student strategy, which results in high throughput and accuracy. DeiT is pre-trained and fine-tuned o...

  • facebook-dinov2-base-imagenet1k-1-layer

    Vision Transformer (base-sized model) trained using DINOv2

Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released...

  • Facebook-DinoV2-Image-Embeddings-ViT-Base

    The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion with the DinoV2 method.

Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token ...

  • Facebook-DinoV2-Image-Embeddings-ViT-Giant

    The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion with the DinoV2 method.

Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token ...

  • facebook-sam-vit-base

    The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 bi...

  • facebook-sam-vit-huge

    The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 bi...

  • facebook-sam-vit-large

    The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 bi...

  • finiteautomata-bertweet-base-sentiment-analysis

    Repository: https://github.com/finiteautomata/pysentimiento/

Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.

Uses `POS...

  • google-vit-base-patch16-224

    The Vision Transformer (ViT) model, as introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al., underwent pre-training on ImageNet-21k with a resolution of 224x224. Su...

  • gpt2

    GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generat...

  • gpt2-large

    GPT-2 Large is the 774M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM)

Training Details

See the [associated paper](https://d4mucfpksywv.cloudfront.net/bet...

  • gpt2-medium

    GPT-2 Medium is the 355M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.

Training Details

See the [associated paper](https://d4mucfpksywv.c...

Note: Use of this model is governed by the Meta license. Click on View License above.

Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion ...

  • mask_rcnn_swin-t-p4-w7_fpn_1x_coco

    This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual ...

  • microsoft-beit-base-patch16-224-pt22k-ft22k

    BEiT (Bidirectional Encoder representation from Image Transformers) is a vision transformer(ViT) pre-trained with Masked Image Modeling(MIM), which is a self-supervised pre-training inspired by BERT from NLP, followed by Intermediate fine-tuning using ImageNet-22k dataset. It is then fine-tuned f...

  • microsoft-deberta-base

    DeBERTa (Decoding-enhanced BERT with Disentangled Attention) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data...

  • microsoft-deberta-base-mnli

    DeBERTa (Decoding-enhanced BERT with Disentangled Attention) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.

Please check the [offi...

  • microsoft-deberta-large

    DeBERTa (Decoding-enhanced BERT with Disentangled Attention) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data...

  • microsoft-deberta-large-mnli

    DeBERTa (Decoding-enhanced BERT with Disentangled Attention) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.

Please check the [offi...

  • microsoft-deberta-xlarge

    DeBERTa (Decoding-enhanced BERT with Disentangled Attention) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data...

  • microsoft-Orca-2-13b

    Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 ...

  • microsoft-Orca-2-7b

    Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 ...

  • microsoft-swinv2-base-patch4-window12-192-22k

    The Swin Transformer V2 model is a type of Vision Transformer, pre-trained on ImageNet-21k with a resolution of 192x192, is introduced in the research-paper titled "Swin Transformer V2: Scaling Up Capacity and Resolution" authored by ...

  • mistral-community-Mixtral-8x22B-v0-1

    The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.

Mixtral-8x22B-v0.1 is a pretrained base model and therefore does not have any moderation mechanisms.

Evaluation Results

[Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/H...

Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1:

  • 32k context window (vs 8k context in v0.1)
  • Rope-theta = 1e6
  • No Sliding-Window Attention

For full details...

Mistral-7B-v0.3 has the following changes compared to Mistral-7B-v0.2

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.

For full details of this mod...

The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks tested.

For full details of this model please read paper and [releas...

Inference samples

Inference type Python sample (Notebook) CLI with YAML
Real time <a href="https://aka.ms/...

Mixtral-8x22B-v0.1 is a pretrained base model and therefore does not have any moderation mechanisms.

Evaluation Results

[Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/H...

  • mistralai-Mixtral-8x7B-Instruct-v01

    The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks with 6x faster inference.

Mixtral-8x7B-v0.1 is a decoder-only model with 8 distinct groups or the "experts". At every layer, for every tok...

The Mixtral-8x7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mixtral-8x7B-v0.1 outperforms Llama 2 70B on most benchmarks with 6x faster inference.

For full details of this model please read [release blog post](https://mi...

It can be used for multi-class and multi-label multimodal classification tasks, and is capable of handling datasets with features from diverse modes, includ...

  • ocsort_yolox_x_crowdhuman_mot17-private-half

    ocsort_yolox_x_crowdhuman_mot17-private-half model is from OpenMMLab's MMTracking library. Multi-Object Tracking (MOT) has rapidly progressed with the development of object detection and re-identification. Howev...

  • OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32

    OpenAI's CLIP (Contrastive Language–Image Pre-training) model was designed to investigate the factors that contribute to the robustness of computer vision tasks. It can seamlessly adapt to a range of image classification tasks without requiring specific training for each, demonstrating efficiency...

  • OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336

    The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general ...

  • openai-clip-vit-base-patch32

    OpenAI's CLIP (Contrastive Language–Image Pre-training) model was designed to investigate the factors that contribute to the robustness of computer vision tasks. It can seamlessly adapt to a range of image classification tasks without requiring specific training for each, demonstrating efficiency...

  • openai-clip-vit-large-patch14

    OpenAI's CLIP (Contrastive Language–Image Pre-training) model was designed to investigate the factors that contribute to the robustness of computer vision tasks. It can seamlessly adapt to a range of image classification tasks without requiring specific training for each, demonstrating efficiency...

  • Phi-3-medium-128k-instruct

    The Phi-3-Medium-128K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Ph...

  • Phi-3-medium-4k-instruct

    The Phi-3-Medium-4K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-...

  • Phi-3-mini-128k-instruct

    The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties.

After initi...

  • Phi-3-mini-4k-instruct

    The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3...

  • Phi-3-small-128k-instruct

    The Phi-3-Small-128K-Instruct is a 7B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model supports 128K conte...

  • Phi-3-small-8k-instruct

    The Phi-3-Small-8K-Instruct is a 7B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model supports 8K context l...

  • Phi-3-vision-128k-instruct

    Model Summary

Phi-3 Vision is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 mo...

  • Phi-3.5-mini-instruct

    Phi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. Th...

  • Phi-3.5-MoE-instruct

    Phi-3.5-MoE is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available documents - with a focus on very high-quality, reasoning dense data. The model supports multilingual and comes with 128K context length (in tokens). The mo...

  • Phi-3.5-vision-instruct

    Phi-3.5-vision is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and t...

  • roberta-base

    RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate i...

  • roberta-base-openai-detector

    The RoBERTa base OpenAI Detector functions as a model designed to detect outputs generated by the GPT-2 model. It was created by refining a RoBERTa base model using the outputs of the 1.5B-parameter GPT-2 model. This detector is utilized to determine whether text was generated by a GPT-2 model. O...

  • roberta-large

    RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate i...

  • roberta-large-mnli

    roberta-large-mnli is the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. The model is a pretrained model on English language text using a masked language modeling ...

  • roberta-large-openai-detector

    RoBERTa large OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa large model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as Op...

  • runwayml-stable-diffusion-inpainting

    Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask.

The Stable-Diffusion-Inpainting was initialized with the weights of the Stable-Diffus...

  • runwayml-stable-diffusion-v1-5

    Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 5...

  • Salesforce-BLIP-2-opt-2-7b-image-to-text

The BLIP-2 model, utilizing OPT-2.7b (a large language model with 2.7 billion parameters), is presented in the paper titled "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models". T...

  • Salesforce-BLIP-2-opt-2-7b-vqa

    The BLIP-2 model, utilizing OPT-2.7b (a large language model with 2.7 billion parameters), is presented in the paper titled "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models". Th...

  • Salesforce-BLIP-image-captioning-base

    BLIP (Bootstrapping Language-Image Pre-training) designed for unified vision-language understanding and generation is a new VLP framework that expands the scope of downstream tasks compared to existing methods. The framework encompasses two key contributions from both model and data perspective...

  • Salesforce-BLIP-vqa-base

    BLIP (Bootstrapping Language-Image Pre-training) designed for unified vision-language understanding and generation is a new VLP framework that expands the scope of downstream tasks compared to existing methods. The framework encompasses two key contributions from both model and data perspective...

  • snowflake-arctic-base

    Model Overview

Arctic is a dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI Research Team. We are releasing model checkpoints for both the base and instruct-tuned versions of Arctic under an Apache-2.0 license. This means you can use them freely in your ow...

Arctic is a dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI Research Team. We are releasing model checkpoints for both the base and instruct-tuned versions of Arctic under an Apache-2.0 license. This means you can use them freely in your ow...

The mod...

With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to B...

  • t5-large

    The developers of the Text-To-Text Transfer Transformer (T5) write:

With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to B...

  • t5-small

    The developers of the Text-To-Text Transfer Transformer (T5) write:

With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to B...

Falcon-40B is a large language model (LLM) developed by the Technology Innovation Institute (TII) with 40 billion parameters. It is a causal decoder-only model trained on 1 trillion tokens from the RefinedWeb dataset, enhanced with curated corpora. Falcon-40B supports English, Germa...

Falcon-40B-Instruct is a large language model with 40 billion parameters, developed by TII. It is a causal decoder-only model fine-tuned on a mixture of Baize data and is released under the Apache 2.0 license. This model is optimized for inference and features FlashAttention and mul...

Falcon-7B is a large language model with 7 billion parameters. It is a causal decoder-only model developed by TII and trained on 1,500 billion tokens of RefinedWeb dataset, which was enhanced with curated corpora. The model is available under the Apache 2.0 license. It outperforms c...

Falcon-7B-Instruct is a large language model with 7 billion parameters, developed by TII. It is a causal decoder-only model and is released under the Apache 2.0 license. This model is optimized for inference and features FlashAttention and multiquery architectures. It is primarily d...

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