Code Llama vs IBM NASA Geospatial
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
It is an LLM that is trained on NASA’s satellite data. It is a foundation model that can be used for various downstream applications, such as classification, object detection, time-series segmentation, and similarity search. | It is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts. It is built on top of Llama 2 and is available for free. |
Openly available on Hugging Face;
Built from NASA’s dataset;
Can be used for a wide range of downstream applications | State-of-the-art LLM for coding;
Free for research and commercial use;
It outperformed state-of-the-art publicly available LLMs on code tasks |
Statistics | |
Stacks 0 | Stacks 5 |
Followers 1 | Followers 4 |
Votes 0 | Votes 0 |
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What are some alternatives to IBM NASA Geospatial, Code Llama?
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.
Google Gemini
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LLaMA
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Whisper
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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.
OpenAI
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Claude
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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.
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.
