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Pixtral (Fully Tested): Mistral's NEW VISION LLM is Finally Here & Beats Qwen-2 VL?
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In this video, I'll be fully testing the New Pixtral Vision Model by Mistral that's based on the Opensource Mistral Nemo 12B model. We'll check if it's really good. I'll also be trying to find out if it can really beat Llama-3.1, Claude 3.5 Sonnet, GPT-4O, DeepSeek & Qwen-2 in vision and language tests. Pixtral Vision model is fully opensource and can be used for FREE. Pixtral Vision is even better in Coding Tasks and is also really good at doing Text-To-Application, Text-To-Frontend and other things as well. I'll be testing it to find out if it can really beat other LLMs and i'll also be telling you that how you can use it.
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Key Takeaways:
🔥 Mistral's Pixtral: The new multimodal model can now process both text and images, bringing advanced AI capabilities from Mistral to the forefront!
👀 Built on Nemo 12b: Pixtral is based on the powerful Mistral Nemo 12b model, but now with added image recognition features—ideal for advanced AI tasks!
📊 Controversial benchmarks: Mistral’s benchmarks have raised eyebrows again, with comparisons to Qwen2 Vision showing signs of data manipulation—learn more in the video!
🚀 128k context & Apache 2.0: Pixtral boasts a massive 128k context capacity, ensuring smoother long-form content generation with a reliable Apache 2.0 license.
🔧 Local hosting made easy: Learn how to set up and run Pixtral locally using VLLM commands for fast deployment and OpenAI compatibility—perfect for AI developers!
✅ Image-to-code tests: Watch Pixtral tackle real-world AI image-to-code tasks, from generating Python programs to creating HTML/CSS interfaces—find out how it compares to Qwen2 VL!
💡 AI humor struggles: While great at vision tasks, Pixtral still stumbles on understanding humor and memes—will Qwen2 VL outperform?
----
Timestamps:
00:00 - Introduction
00:14 - About Pixtral
01:17 - Benchmarks
02:52 - Testing
06:06 - Conclusion
07:41 - Ending
In this video, I'll be fully testing the New Pixtral Vision Model by Mistral that's based on the Opensource Mistral Nemo 12B model. We'll check if it's really good. I'll also be trying to find out if it can really beat Llama-3.1, Claude 3.5 Sonnet, GPT-4O, DeepSeek & Qwen-2 in vision and language tests. Pixtral Vision model is fully opensource and can be used for FREE. Pixtral Vision is even better in Coding Tasks and is also really good at doing Text-To-Application, Text-To-Frontend and other things as well. I'll be testing it to find out if it can really beat other LLMs and i'll also be telling you that how you can use it.
-----
Key Takeaways:
🔥 Mistral's Pixtral: The new multimodal model can now process both text and images, bringing advanced AI capabilities from Mistral to the forefront!
👀 Built on Nemo 12b: Pixtral is based on the powerful Mistral Nemo 12b model, but now with added image recognition features—ideal for advanced AI tasks!
📊 Controversial benchmarks: Mistral’s benchmarks have raised eyebrows again, with comparisons to Qwen2 Vision showing signs of data manipulation—learn more in the video!
🚀 128k context & Apache 2.0: Pixtral boasts a massive 128k context capacity, ensuring smoother long-form content generation with a reliable Apache 2.0 license.
🔧 Local hosting made easy: Learn how to set up and run Pixtral locally using VLLM commands for fast deployment and OpenAI compatibility—perfect for AI developers!
✅ Image-to-code tests: Watch Pixtral tackle real-world AI image-to-code tasks, from generating Python programs to creating HTML/CSS interfaces—find out how it compares to Qwen2 VL!
💡 AI humor struggles: While great at vision tasks, Pixtral still stumbles on understanding humor and memes—will Qwen2 VL outperform?
----
Timestamps:
00:00 - Introduction
00:14 - About Pixtral
01:17 - Benchmarks
02:52 - Testing
06:06 - Conclusion
07:41 - Ending
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