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Codestral Mamba (mistral.ai)

485 points by tosh · 755 days ago · 138 comments on HN

Article summary

Mistral AI has released Codestral Mamba, a new AI model designed for code productivity use cases, which offers linear time inference and the ability to model sequences of infinite length. The model is available for free use, modification, and distribution, and can be deployed using the mistral-inference SDK or through TensorRT-LLM. Codestral Mamba has been tested on in-context retrieval capabilities up to 256k tokens and is expected to be a great local code assistant. The model has 7,285,403,648 parameters and is available under the Apache 2.0 license.

Main themes

  • AI models for code productivity
  • Local code assistants
  • Model deployment and integration
  • LLM development and research
  • Coding tools and platforms
  • AI licensing and accessibility

What commenters say

  • Some commenters are excited about the potential of Codestral Mamba and its ability to run locally, while others are skeptical about its performance compared to other models.
  • There is a need for more user-friendly and accessible tools for running LLMs locally, with some commenters recommending ollama and others suggesting alternative solutions.
  • The development of LLMs is rapidly advancing, with new models and tools being released regularly, and some commenters are struggling to keep up with the latest developments.
  • The choice of code assistant or LLM depends on individual needs and preferences, with some commenters preferring certain models or tools over others.
  • Some commenters believe that Mistral AI has missed an opportunity to provide a clear call to action for users to try out Codestral Mamba, particularly in terms of integration with popular coding platforms like VS Code.
  • There is a debate about the effectiveness of different LLMs for coding tasks, with some commenters arguing that certain models are better suited for specific tasks or languages.
  • The licensing and accessibility of LLMs is an important consideration, with some commenters appreciating the open-source nature of Codestral Mamba and others discussing the limitations of commercial licenses.
  • The performance of LLMs can vary significantly depending on the specific use case and hardware, with some commenters reporting positive experiences with certain models and others encountering issues with performance or compatibility.