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Want to build your own generative AI applications?
Heres a list of AI models to help you get started.
AI modelsare neural connection architectures that perform extremely well on specific tasks.
Which is both due to advances in research and access to high-performance computing.
Here is a quick summary of the popular AI models Ill be discussing below.
Here, we list some interesting AI models you might explorealong with their key capabilities.
Its OpenAIs most powerful AI system with better reasoning capabilities and performance than ChatGPT.
you’ve got the option to enter the ChatGPT interface with afree OpenAI account.
To access GPT-4, however, you should have a ChatGPT Plus subscription.
LLaMA
Meta AI releasedLLaMA,a foundational large language model with 65B parameters, in February 2023.
Subsequently, LLama 2 was released with substantial improvements over the previous release.
All the models in the Falcon LLM suite are open source and are available for open access.
So you’re able to use them to build LLM-powered applications.
Currently, there are four model sizes: 1.3B, 7.5B, 40B, and 180B.
to perform better than on several benchmarks the 180B model was trained on a dataset of 3.5T tokens.
The Falcon LLM performs at par with other leading open-source LLMs.
The Falcon 180B open-source LLM achieves performance close to that of GPT-4.
It can also be used for image upscaling and inpainting.
DALL-E 2
DALL-E 2from Open AI is another popular text-to-image generation model.
you’ve got the option to use it to generate realistic images and art from textnatural language description.
StableLM
StableLMis an open-source LLM suite from Stability AI.
The 3B and 7B parameters are currently available.
Subsequent releases will include larger models with 15B 65B parameters.
CLIP
CLIPstands forContrastive Language-Image Pre-training.
The model leverages natural language data, tries to learnfrom the natural language descriptionsthe semantics of the images.
The CLIP model is capable of predicting the most relevant text given an image.
With CLIP, you’re free to perform zero-shot image classificationwithout expensive pre-training and fine-tuning.
you’re able to use prompts to specifywhatto segment in an image.
SAM currently supports the following prompts: bounding boxes, masks, and foreground and background points.
The model also has excellent zero-shot generalization performance on previously unseen images.
So no explicit training is required.
Try out theSAM model in your web app!
InternLM
InternLMis an open-source language model.
you’re able to try out the 7B base model and the open-source chat model.
The model supports a context window of 8K.
Additionally, InternLM supports code interpreter and function calling capabilities.
InternLM is also available in the HuggingFacetransformerslibrary.
it’s possible for you to leverage the lightweight pre-training framework.
It also supports building and deploying applications usingLMDeploy.
So, you’re free to build end-to-end generative NLP applications with InternLM.
WaveGAN
WaveGANis a model for audio generation.
It helps synthesize raw audio from samples of real audio data.
But what if you want to perform image-to-image translation?
CycleGAN is well-suited for such tasks.
you’re able to find the PyTorch implementations of CycleGANon GitHub.
It uses the sequence-to-sequence model implementations provided byfairseq.
you’re free to download the model either from the URL or from the HuggingFace hub.
The BioGPT models are also part of the HuggingFacetransformerslibrary.
So, if youre working in the biomedical space, you might use BioGPT to build domain-specific applications.
And you may face limitations when fine-tuning models as the fine-tuning process is often resource-intensive.
So if youre a developer, its time to join the AI revolution and start building interesting AI applications!
you’re free to try out these models in Google Colab or othercollaborative data science notebooks.
