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What is Codestral Mamba?

In tribute to Cleopatra, whose dramatic story ended with the fateful encounter with a snake, we proudly present Codestral Mamba, a Mamba2 language model tailored for code generation and made available under an Apache 2.0 license. Codestral Mamba marks a pivotal step forward in our commitment to pioneering and refining innovative architectures. This model is available for free use, modification, and distribution, and we hope it will pave the way for new discoveries in architectural research. The Mamba models stand out due to their linear time inference capabilities, coupled with a theoretical ability to manage sequences of infinite length. This unique characteristic allows users to engage with the model seamlessly, delivering quick responses irrespective of the input size. Such remarkable efficiency is especially beneficial for boosting coding productivity; hence, we have integrated advanced coding and reasoning abilities into this model, ensuring it can compete with top-tier transformer-based models. As we push the boundaries of innovation, we are confident that Codestral Mamba will not only advance coding practices but also inspire new generations of developers. This exciting release underscores our dedication to fostering creativity and productivity within the tech community.

What is CodeQwen?

CodeQwen acts as the programming equivalent of Qwen, a collection of large language models developed by the Qwen team at Alibaba Cloud. This model, which is based on a transformer architecture that operates purely as a decoder, has been rigorously pre-trained on an extensive dataset of code. It is known for its strong capabilities in code generation and has achieved remarkable results on various benchmarking assessments. CodeQwen can understand and generate long contexts of up to 64,000 tokens and supports 92 programming languages, excelling in tasks such as text-to-SQL queries and debugging operations. Interacting with CodeQwen is uncomplicated; users can start a dialogue with just a few lines of code leveraging transformers. The interaction is rooted in creating the tokenizer and model using pre-existing methods, utilizing the generate function to foster communication through the chat template specified by the tokenizer. Adhering to our established guidelines, we adopt the ChatML template specifically designed for chat models. This model efficiently completes code snippets according to the prompts it receives, providing responses that require no additional formatting changes, thereby significantly enhancing the user experience. The smooth integration of these components highlights the adaptability and effectiveness of CodeQwen in addressing a wide range of programming challenges, making it an invaluable tool for developers.

Media

Media

Integrations Supported

Hugging Face
AI-FLOW
C
Clojure
Conda
Echo AI
EvalsOne
F#
HTML
Langflow
Lewis
Mathstral
Melies
Mistral AI
Overseer AI
PostgresML
Respan
StarCoder
SydeLabs
Toolmark

Integrations Supported

Hugging Face
AI-FLOW
C
Clojure
Conda
Echo AI
EvalsOne
F#
HTML
Langflow
Lewis
Mathstral
Melies
Mistral AI
Overseer AI
PostgresML
Respan
StarCoder
SydeLabs
Toolmark

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Mistral AI

Company Location

France

Company Website

mistral.ai/news/codestral-mamba/

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

github.com/QwenLM/CodeQwen1.5

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