Codestral-Mamba (7B) : Testing the NEW Mamba Coding LLM by Mistral (Beats DeepSeek-V2, Qwen2?)

--------
In this video, I'll be doing an in-depth testing of the new Codestral-Mamba model by Mistral. This is a new Coding LLM on a relatively new Mamba Architecture which is fast and can run on consumer-grade hardwares locally.
It claims to beat the other Opensource Coding LLMs such as DeepSeek-Coder-V2, Qwen2, Llama-3 and others. I'll be testing it to find out if it can really beat other coding LLMs and i'll also be telling you that how you can use it. You can also do Text-To-Application, Text-To-Frontend and other things with it.

--------
Key Takeaways:

📢 New Model Launch: Mistral has introduced two new models, Codestral Mamba 7b and Mathstral, revolutionizing AI technology.

🔍 Focus on Codestral Mamba 7b: This video dives deep into the features and benefits of the Codestral Mamba 7b model, offering insights for tech enthusiasts and AI developers.

🚀 Advanced Architecture: Discover the innovative Selective-State-Spaces (SSM) architecture of Mamba, providing faster inference and efficient hardware usage, unlike traditional GPT models.

📈 Benchmark Performance: Learn how Codestral Mamba 7b outperforms competitors like DeepSeek V1.5 in HumanEval and CruxE benchmarks, showcasing its superior capabilities.

🔧 Easy Setup Guide: Step-by-step instructions on how to set up and use Codestral Mamba 7b locally, making it accessible for developers and AI enthusiasts.

📝 Coding Test Results: See how Codestral Mamba 7b fares in various coding challenges, proving its worth as a reliable AI copilot with a 256k context limit.

💡 Commercial Use: Understand the advantages of Codestral Mamba 7b’s Apache 2 license, making it ideal for commercial projects and professional applications.

----------------
Timestamps:

00:00 - Introduction
00:08 - New Releases by Mistral AI (Mathstral & Codestral-Mamba)
00:29 - About Codestral Mamba
00:55 - About the New Mamba Architecture
01:29 - More About Codestral Mamba
01:45 - Benchmarks
02:39 - Availability
03:09 - How To Use Locally
03:34 - OnDemand (Sponsor)
04:42 - Testing the LLM
09:06 - Conclusion

[ManuelJimenez]

Thanks for the benchmark, we would like to have a benchmark between DeepSeek-v2 and Qwen2 to use as CoPilot using oLlama, it would be great, thanks!

[KevinKreger]

You can use the Mistral endpoint through the API

[MeinDeutschkurs]

I don’t know what to think about it. Maybe this architecture needs a certain way of prompting to get really good results. Could you please try to print out some lines of the training data, by just telling it “complete! Maybe this provides the model’s way of thinking.

[NLPprompter]

although this dia saved for watch later.... I haven't sleepy yet.... but I can't wait anymore to watch this

[ahsanmasood]

Needs one guided system prompt to tame the Codestral-Mamba 💪🏻

[HaraldEngels]

If a Copilot video is not feasable please could you provide more coding tests in multiple popular programming languages. Nothing fancy but somewhat more expressive that the minimal challenges you have in your standard AI questionnaire.

[NLPprompter]

been a fan of mamba it seems the concept is better than tokenize llm it's logically usable... and can't wait to see it's more development

[MrMoonsilver]

Couldn't you test the 256k context window?

[snipe]

What Ai tool do you use to make your intro backgrounds, I'm interested let me thanks

[daryladhityahenry]

Hi! How much vram you needed to run this? I see that it doesn't have quantized version too >, <.. .. Anyway, I still stucked at installing the mamba package ( don't know how to go on lol ). It seems problem with packaging version, but it doesn't tell what version needed...