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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

Screenshots View All

No images available

Integrations

AGAT Secure AI Platform
Admix
Aerogram
Agenta
Alumnium
Amazon Bedrock
Axolotl
Chatbot Arena
Clore.ai
Decopy AI
Entry Point AI
Jspreadsheet
LLaMA-Factory
Llama 4 Maverick
Llama Guard
Nurix
PostgresML
Scout
SheetMagic
Trusys AI

Integrations

AGAT Secure AI Platform
Admix
Aerogram
Agenta
Alumnium
Amazon Bedrock
Axolotl
Chatbot Arena
Clore.ai
Decopy AI
Entry Point AI
Jspreadsheet
LLaMA-Factory
Llama 4 Maverick
Llama Guard
Nurix
PostgresML
Scout
SheetMagic
Trusys 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

Product Features

Alternatives

Alternatives

Gemini Reviews

Gemini

Google
voyage-4-large Reviews

voyage-4-large

Voyage AI
Llama 2 Reviews

Llama 2

Meta
Voyage AI Reviews

Voyage AI

MongoDB
Alpaca Reviews

Alpaca

Stanford Center for Research on Foundation Models (CRFM)