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Akshay 🚀

@akshay_pachaar

Mar 13, 2025 • 11 tweets • 3 min read • Read on X

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Model Context Protocol (MCP), clearly explained:

MCP is like a USB-C port for your AI applications.

Just as USB-C offers a standardized way to connect devices to various accessories, MCP standardizes how your AI apps connect to different data sources and tools.

Let's dive in! 🚀

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers.

Key components include:

- Host
- Client
- Server

Here's an overview before we dig deep 👇

The Host and Client:

Host: An AI app (Claude desktop, Cursor) that provides an environment for AI interactions, accesses tools and data, and runs the MCP Client.

MCP Client: Operates within the host to enable communication with MCP servers.

Next up, MCP server...👇

The Server

A server exposes specific capabilities and provides access to data.

3 key capabilities:

- Tools: Enable LLMs to perform actions through your server
- Resources: Expose data and content from your servers to LLMs
- Prompts: Create reusable prompt templates and workflows

The Client-Server Communication

Understanding client-server communication is essential for building your own MCP client-server.

Let's begin with this illustration and then break it down step by step... 👇

1️⃣ & 2️⃣: capability exchange

client sends an initialize request to learn server capabilities.

server responds with its capability details.

e.g., a Weather API server provides available `tools` to call API endpoints, `prompts`, and API documentation as `resource`.

3️⃣ Notification

Client then acknowledgment the successful connection and further message exchange continues.

Before we wrap, one more key detail...👇

Unlike traditional APIs, the MCP client-server communication is two-way.

Sampling, if needed, allows servers to leverage clients' AI capabilities (LLM completions or generations) without requiring API keys.

While clients to maintain control over model access and permissions

I hope this clarifies what MCP does.

In the future, I'll explore creating custom MCP servers and building hands-on demos around them.

Over to you! What is your take on MCP and its future?

That's a wrap!

If you enjoyed this breakdown:

Follow me → @akshay_pachaar ✔️

Every day, I share insights and tutorials on LLMs, AI Agents, RAGs, and Machine Learning!

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More from @akshay_pachaar

Akshay 🚀

@akshay_pachaar

Jun 7

Google just dropped a new LLM!

You can run it locally on just 8GB RAM.

Let's fine-tune this on our own data (100% locally):

Google released Gemma 4 12B, a multimodal model that runs text, images, and audio on 8GB VRAM!

We'll fine-tune it to master chess and predict the exact next move.

Tech stack:
- @UnslothAI for efficient fine-tuning.
- @huggingface transformers to run it locally.

Let's go! 🚀

1️⃣ Load the model

We start by loading Gemma 4 12B and its tokenizer using Unsloth.

Check this 👇

Read 10 tweets

Akshay 🚀

@akshay_pachaar

Jun 3

You're in a Research Scientist interview at OpenAI.

The interviewer asks:

"How would you expand the context length of an LLM from 2K to 128K tokens?"

You: "I will fine-tune the model on longer docs with 128K context."

Interview over.

Here's what you missed:

Extending the context window isn't just about larger matrices.

In a traditional transformer, expanding tokens by 8x increases memory needs by 64x due to the quadratic complexity of attention. Refer to the image below!

So, how do we manage it?

continue...👇

1) Sparse Attention

It limits the attention computation to a subset of tokens by:

- Using local attention (tokens attend only to their neighbors).
- Letting the model learn which tokens to focus on.

But this has a trade-off between computational complexity and performance.

Read 12 tweets

Akshay 🚀

@akshay_pachaar

Dec 18, 2025

Turn any Autoregressive LLM into a Diffusion LM.

dLLM is a Python library that unifies the training & evaluation of diffusion language models.

You can also use it to turn ANY autoregressive LM into a diffusion LM with minimal compute.

100% open-source.

Here's why this matters:

Traditional autoregressive models generate text left-to-right, one token at a time. Diffusion models work differently - they refine the entire sequence iteratively, giving you better control over generation quality and more flexible editing capabilities.

dLLM GitHub:

(don't forget to star 🌟)github.com/ZHZisZZ/dllm

Read 4 tweets

Akshay 🚀

@akshay_pachaar

Dec 6, 2025

You're in a Research Scientist interview at Google.

Interviewer: We have a base LLM that's terrible at maths. How would you turn it into a maths & reasoning powerhouse?

You: I'll get some problems labeled and fine-tune the model.

Interview over.

Here's what you missed:

When outputs are verifiable, labels become optional.

Maths, code, and logic can be automatically checked and validated.

Let's use this fact to build a reasoning model without manual labelling.

We'll use:

- @UnslothAI for parameter-efficient finetuning.
- @HuggingFace TRL to apply GRPO.

Let's go! 🚀

What is GRPO?

Group Relative Policy Optimization is a reinforcement learning method that fine-tunes LLMs for math and reasoning tasks using deterministic reward functions, eliminating the need for labeled data.

Here's a brief overview of GRPO before we jump into code:

Read 11 tweets

Akshay 🚀

@akshay_pachaar

Dec 5, 2025

I have been training neural networks for 10 years now.

Here are 16 ways I actively use to optimize model training:

(detailed explanation ...🧵)

First, lets look at some basic techniques:

1) Use efficient optimizers—AdamW, Adam, etc.

2) Utilize hardware accelerators (GPUs/TPUs).

3) Max out the batch size.

4) Use multi-GPU training through Model/Data/Pipeline/Tensor parallelism.

Check the visual👇

5) Bayesian optimization for hyperparameter optimization:

This technique takes informed steps based on the results of the previous hyperparameter configs.

This way, the model converges to an optimal set of hyperparameters much faster.

Check these results 👇

Read 9 tweets

Akshay 🚀

@akshay_pachaar

Nov 23, 2025

You’re in an ML Engineer interview at Google.

Interviewer: We need to train an LLM across 1,000 GPUs. How would you make sure all GPUs share what they learn?

You: Use a central parameter server to aggregate and redistribute the weights.

Interview over.

Here’s what you missed:

One major run-time bottleneck in multi-GPU training happens during GPU synchronization.

For instance, in multi-GPU training via data parallelism:

- The same model is distributed to different GPUs.
- Each GPU processes a different subset of the whole dataset.

Check this 👇

This leads to different gradients across different devices.

So, before updating the model parameters on each GPU device, we must communicate the gradients to all other devices to sync them.

Let’s understand 2 common strategies next!

Read 14 tweets

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