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Description
Codestral Embed marks Mistral AI's inaugural venture into embedding models, focusing specifically on code and engineered for optimal code retrieval and comprehension. It surpasses other prominent code embedding models in the industry, including Voyage Code 3, Cohere Embed v4.0, and OpenAI’s large embedding model, showcasing its superior performance. This model is capable of generating embeddings with varying dimensions and levels of precision; for example, even at a dimension of 256 and int8 precision, it maintains a competitive edge over rival models. The embeddings are organized by relevance, enabling users to select the top n dimensions, which facilitates an effective balance between quality and cost. Codestral Embed shines particularly in retrieval applications involving real-world code data, excelling in evaluations such as SWE-Bench, which uses actual GitHub issues and their solutions, along with Text2Code (GitHub), which enhances context for tasks like code completion or editing. Its versatility and performance make it a valuable tool for developers looking to leverage advanced code understanding capabilities.
Description
Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain.
Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.
API Access
Has API
API Access
Has API
Screenshots View All
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Integrations
AGAT Secure AI Platform
Admix
Aerogram
Agenta
Alumnium
Amazon Bedrock
Axolotl
Chatbot Arena
Clore.ai
Decopy AI
Integrations
AGAT Secure AI Platform
Admix
Aerogram
Agenta
Alumnium
Amazon Bedrock
Axolotl
Chatbot Arena
Clore.ai
Decopy AI
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Mistral AI
Founded
2023
Country
United States
Website
mistral.ai/news/codestral-embed
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
www.llama.com