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runwayml/stable-diffusion-v1-5 cover image
featured
$0.0005 / sec
  • text-to-image

Most widely used version of Stable Diffusion. Trained on 512x512 images, it can generate realistic images given text description

openai/whisper-large cover image
featured
$0.0005 / sec
  • automatic-speech-recognition

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

prompthero/openjourney cover image
featured
$0.0005 / sec
  • text-to-image

Text to image model based on Stable Diffusion.

BAAI/bge-large-en-v1.5 cover image
featured
512
$0.010 / Mtoken
  • embeddings

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

Austism/chronos-hermes-13b-v2 cover image
4k
$0.22 / Mtoken
  • text-generation

This offers the imaginative writing style of chronos while still retaining coherency and being capable. Outputs are long and utilize exceptional prose. Supports a maxium context length of 4096. The model follows the Alpaca prompt format.

BAAI/bge-base-en-v1.5 cover image
512
$0.005 / Mtoken
  • embeddings

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

CompVis/stable-diffusion-v1-4 cover image
$0.0005 / sec
  • text-to-image

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.

Davlan/bert-base-multilingual-cased-ner-hrl cover image
$0.0005 / sec
  • token-classification

A named entity recognition model for 10 high-resource languages, trained on a fine-tuned mBERT base model. The model recognizes three types of entities: location, organization, and person. The training data consists of entity-annotated news articles from various datasets for each language, and the model distinguishes between the beginning and continuation of an entity.

EleutherAI/gpt-j-6B cover image
2k
$0.0005 / sec
  • text-generation

GPT-J 6B is a 6 billion parameter transformer model trained using Ben Wang's Mesh Transformer JAX. It was trained on the Pile, a large-scale curated dataset created by EleutherAI. The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384.

EleutherAI/gpt-neo-1.3B cover image
2k
$0.0005 / sec
  • text-generation

We present GPT-Neo 1.3B, a transformer model designed using EleutherAI's replication of the GPT-3 architecture. With 1.3B parameters, this model was trained on the large-scale curated dataset, Pile, for 380 billion tokens over 362,000 steps. Its intended use is for text generation, where it learns an inner representation of the English language and can generate texts from a prompt.

EleutherAI/gpt-neo-125M cover image
2k
$0.0005 / sec
  • text-generation

A transformer model trained on the Pile dataset for masked autoregressive language modeling. With 125 million parameters, our model is capable of generating high-quality text given a prompt. However, we acknowledge potential limitations and biases in the model's responses, particularly regarding profanity and offensiveness, and advise users to exercise caution when deploying the model for real-world applications.

EleutherAI/gpt-neo-2.7B cover image
2k
$0.0005 / sec
  • text-generation

We present GPT-Neo 2.7B, a transformer model designed using EleutherAI's replication of the GPT-3 architecture. With 2.7B parameters, this model was trained on the large-scale curated dataset, Pile, for 420 billion tokens over 400,000 steps. GPT-Neo 2.7B achieves state-of-the-art results on various benchmarks, including linguistic reasoning, physical and scientific reasoning, and down-stream applications.

EleutherAI/pythia-12b cover image
2k
$0.0005 / sec
  • text-generation

The Pythia Scaling Suite includes 16 models (2 per size) with sizes ranging from 70M to 12B parameters, trained on the Pile dataset. The models are designed for interpretability research and match or exceed performance of similar models. The suite includes 154 intermediate checkpoints per model.

EleutherAI/pythia-2.8b cover image
2k
$0.0005 / sec
  • text-generation

Pythia Scaling Suite is a collection of models for interpretability research, containing 8 models from 70M to 12B, with two models per size (one trained on Pile and the other on deduped Pile). The models are designed for scientific research, esp interpretability research. The model matches or exceeds performance of similar models like OPT and GPT-Neo.

GroNLP/bert-base-dutch-cased cover image
$0.0005 / sec
  • fill-mask

We present BERTje, a Dutch pre-trained BERT model developed at the University of Groningen. BERTje achieved state-of-the-art results on several NLP tasks such as named entity recognition and part-of-speech tagging. We also provide a detailed comparison of BERTje with other pre-trained models such as mBERT and RobBERT.

Jean-Baptiste/camembert-ner cover image
$0.0005 / sec
  • token-classification

A Named Entity Recognition model fine-tuned from CamemBERT on the Wikiner-FR dataset. Our model achieves high performance on various entities, including Persons, Organizations, Locations, and Miscellaneous entities.