Phi-4 vs. Llama3.3: A Math Showdown in AI
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Real discussions and feedbacks of Phi-4 vs. Llama3.3: A Math Showdown in AI
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App Description
This weekend, I tested AI models to see how they handle reasoning and iterative feedback. Here’s how they performed on a tricky combinatorial problem:
• Phi-4 (14B, FP16): Delivered the correct answer on its first attempt, then adjusted accurately when prompted to recheck.
• Llama3.3:70b-instruct-q8_0: Corrected its mistake on the second try—showing some adaptability.
• Llama3.3:latest: Repeated the same incorrect answer despite feedback, highlighting reasoning limitations.
• Llama3.3:70b-instruct-fp16: Couldn’t utilize GPU resources and failed to perform on my hardware.🤔 Key Takeaways:
1️⃣ Smaller models like Phi-4 outperformed larger ones, proving that quantization (e.g., FP16 vs. Q8_0) is crucial.
2️⃣ Iterative reasoning and feedback adaptability matter as much as raw size.
3️⃣ Hardware compatibility significantly impacts usability.🎥 Curious about the results? Watch my live demo here: https://youtu.be/CR0aHradAh8
See how these models handle accuracy, feedback, and time-to-answer in real time!🔗 What are your thoughts? Have you tested Phi-4 or Llama models? Let me know ur findings please? 🙏🏾
Project Overview
The post discusses a comparative analysis of AI models, specifically Phi-4 and various versions of Llama3.3, focusing on their performance in handling a combinatorial problem. The key findings highlight the importance of model size, quantization, iterative reasoning, feedback adaptability, and hardware compatibility. The author shares a live demo video to showcase the models’ performance in real-time.
Links
🌐 Website: https://youtu.be/CR0aHradAh8
Media
Videos
Features & Benefits
✅ Phi-4 (14B, FP16) delivered the correct answer on its first attempt and adjusted accurately when prompted to recheck.
✅ Llama3.3:70b-instruct-q8_0 corrected its mistake on the second try, showing adaptability.
✅ Smaller models like Phi-4 outperformed larger ones, proving the importance of quantization.
✅ Iterative reasoning and feedback adaptability are crucial for AI model performance.
✅ Hardware compatibility significantly impacts the usability of AI models.
Areas for Improvement
🔄 Llama3.3:latest repeated the same incorrect answer despite feedback.
🔄 Llama3.3:70b-instruct-fp16 couldn’t utilize GPU resources and failed to perform on the author’s hardware.