OpenAI vs Stanford Alpaca
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
Creating safe artificial general intelligence that benefits all of humanity. Our work to create safe and beneficial AI requires a deep understanding of the potential risks and benefits, as well as careful consideration of the impact. | It is a model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. It behaves qualitatively similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce. |
Pioneering research on the path to AGI;
Transforming work and creativity with AI | Instruction-following model;
Fine-tuned from a 7B LLaMA model on 52K instruction-following data
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Statistics | |
GitHub Stars - | GitHub Stars 30.2K |
GitHub Forks - | GitHub Forks 4.0K |
Stacks 924 | Stacks 0 |
Followers 192 | Followers 0 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to OpenAI, Stanford Alpaca?
Grok-1
It is the base model weights and network architecture of Grok-1, the large language model. Grok-1 is a 314 billion parameter Mixture-of-Experts model trained from scratch by xAI.
Grok 4
Try Grok 4 on GPT Proto. Access xAI’s most advanced 1.7T LLM with 130K context, multimodal support, and real-time data integration for dynamic analysis.
Claude
It is a next-generation AI assistant. It is accessible through chat interface and API. It is capable of a wide variety of conversational and text-processing tasks while maintaining a high degree of reliability and predictability.
Google Gemini
It is Google’s largest and most capable AI model. It is built to be multimodal, it can generalize, understand, operate across, and combine different types of info — like text, images, audio, video, and code.
LLaMA
It is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI.
GPT-4 by OpenAI
It is a large multimodal model (accepting text inputs and emitting text outputs today, with image inputs coming in the future) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities.
Cohere.com
It offers an API to add cutting-edge language processing to any system. Through training, users can create massive models customized to their use case and trained on their data.
Whisper
It 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.
Mistral 7B
It is a small, yet powerful model adaptable to many use cases. It is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. We made it easy to deploy on any cloud.
DeepSeek Coder
It is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language. There are various sizes of the code model, ranging from 1B to 33B versions.
