Finally! You can reliably test AI Agents without humans:- One Agent asks questions.- The Agent being tested responds.- Another Agent Judges.Here’s a complete breakdown (with code): https://t.co/wDm2a4zN0bAvi Chawla (@_avichawla):How Agents test Agents, clearly explained (with code): ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):How Agents test Agents, clearly explained (with code): ...

To recap:Traditional testing relies on fixed inputs and exact outputs. But agents speak in language, and there’s no single “correct” response.That’s why we test Agents with Agents by simulating Users and Judges.Here's the process again for your reference👇 https://t.co/BFuBQxVPwF ...

How Agents test Agents, clearly explained (with code): ...

Model Fine-tuning Overview - The document outlines the process of fine-tuning models like DeepSeek-R1 [1] - The process includes dataset preparation, LoRA configuration, trainer definition, fine-tuning, and exporting to Ollama [1] Technical Implementation - The fine-tuning process of DeepSeek-R1 (distilled Llama) can be done 100% locally [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally: ...

We have fine-tuned DeepSeek (distilled Llama).Now we can interact with it like any other model running on Ollama using:- The CLI- Ollama's Python package- Ollama's LlamaIndex integration, etc. https://t.co/bCNUqtLgaJ ...

Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally: ...

RT Avi Chawla (@_avichawla)A @3blue1brown style animation of KMeans clustering: https://t.co/aGs15e2YaR ...

A @3blue1brown style animation of KMeans clustering: https://t.co/aGs15e2YaR ...