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): ...

Agent Testing Methodology - Traditional testing methods are inadequate for evaluating Agents due to the lack of fixed inputs and exact outputs when dealing with language [1] - The industry utilizes Agent-based testing, simulating Users and Judges to assess Agent performance [1] - The process involves testing Agents with Agents to evaluate their responses in a more realistic and dynamic environment [1]

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 ...

MCP standardizes Agent to Tool communication.The thread covers:- MCP with a simple analogy.- Building an MCP server 100% locally.- Integrating a browser MCP server with Claude Desktop.Check the thread below. https://t.co/X572vSn9hbAvi Chawla (@_avichawla):Let's build an MCP server (100% locally): ...