Read the paper if you are interested in reasoning models. It is very well written and easy to understand.

🔗Paper: github.com/deepseek-ai/...
🔗Models: huggingface.co/models?sort=...

🔹 Record-setting Performance: Outshines OpenAI's top-tier models in benchmarks like AIME 2024 (79.8% Pass@1) and MATH-500 (97.3% Pass@1).
🔹 Smaller Models, Big Impact: The distilled models (1.5B to 70B parameters) pack the reasoning punch of larger models
🔹 Open-Source Goodness!

The takeaway? Before thinking about fancy algorithms, fix your data foundation. No consultant worth their salt skips this step. It’s the bedrock of ML success.

Performance for Phi-4 is compared to other top models, GPT-4o-mini and Qwen 2.5, side by side in Figure 1 of the Phi-4 Technical Report: arxiv.org/abs/2412.08905.

Is this the future of LLMs? What are your thoughts on scaling quality versus size in AI models?

It outperforms its teacher model, GPT-4o, on STEM QA and math benchmarks. It achieves performance comparable to many larger models such as Llama-3 70B but at a much lower cost. Just because it's small doesn't mean it's shy from acing extremely reasoning-heavy benchmarks!

📄Main questions:

-What is the difference between Agents and LLMs?
-Tools that extend Agent functionality
-Enhancing performance with targeted learning
-Quick start guides with LangChain
-Production-ready applications with Vertex AI

the link to the paper:
🔗https://www.kaggle.com/whitepaper-agents

Enabling them to:

- Plan and execute tasks autonomously with frameworks like ReAct and Chain-of-Thought.
- Interact with real-time systems via Extensions, Functions, and Data Stores
- Adapt to complex workflows with orchestrated reasoning and decision-making cycles.

Want to align your AI roadmap with real-world impact?
Let’s discuss this in a short meeting. You can DM me to schedule a quick call.

What outcomes are you chasing?
Are your teams aligned?
Do your current workflows support growth?
From there, I design tailored strategies that connect AI efforts with measurable business results.

Paper:
🔗https://ai.meta.com/research/publications/large-concept-models-language-modeling-in-a-sentence-representation-space/

GitHub:
🔗https://github.com/facebookresearch/large_concept_model

MLOps consultants cut through the noise and set you up with tech that actually works.
Struggling with tool overload?
⭕ DM me to discuss how to simplify and scale effectively.

What you need is the RIGHT tools for YOUR needs:

Small team?➡️ Use lightweight frameworks.

Enterprise scaling?➡️ Invest in robust CI/CD workflows

New to ML?➡️ Cloud-native tools save headaches.

✔️ Collaborative communication: Will they align with your team’s workflows or create friction?

✔️ A focus on explainability: ML isn't magic, it needs to make sense to your decision-makers.

The right consultant is a partner, not a vendor.