Скачать или смотреть Hugging Face Basics + Open-Source LLMs Explained (Gemma, LLaMA, Mistral) | LLM Bootcamp Hindi 🚀

🔥 Day 2 of the LLM Bootcamp in Hindi!
Today, we dive deep into Hugging Face and Open-Source LLMs like Gemma, LLaMA, and Mistral — and learn how to run them locally or via the cloud.
We’ll also cover model parameters, quantization, GPUs vs CPUs, and how to choose the right model for your system.

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📍 What You’ll Learn in this video:
0:00 – Intro and quick recap (Day 1 summary)
1:05 – Today’s goal: use LLMs to build apps
1:43 – Why Open-Source LLMs (privacy, control, cost)
3:08 – Data privacy concerns with closed models
3:32 – Run open-source LLMs locally or online
4:11 – Examples: Gemma (Google), Llama, Mistral
5:29 – What is Hugging Face? Models, datasets, Spaces, Inference API
7:01 – Hugging Face vs GitHub (analogy)
7:32 – Host vs local: using HF Inference API
9:18 – Plan: pick a model, host/use via API, or run locally
10:46 – Why GPUs for AI (parallel math) vs CPU
12:32 – GPU speed analogy and matrix math in LLMs
15:10 – Can we run on CPU? Yes, but slower (small models)
16:39 – Small vs large models (distil, TinyLlama, GPT‑2 small, Mistral)
18:58 – Model parameters explained (intelligence vs compute)
20:37 – Examples: GPT‑2 ~124M, GPT‑3 ~175B (resource needs)
24:45 – RAM matters: model size vs available memory
26:07 – Practical tip: choose models that fit 4–6GB RAM
27:54 – Why models are large: data type precision (fp32, int8, int4)
30:11 – Quantization explained (smaller, faster models)
31:32 – Real-world use: quantized models work well for dev
32:03 – Reading model names (e.g., Mistral‑7B‑Instruct‑GGUF)
35:02 – Big models (65B–180B): need high-end GPUs or cloud
35:39 – Finding models on Hugging Face (filters: task, params, quantized)
38:22 – Quick demo plan: run GPT‑2 via Transformers on Colab
43:30 – Key params in generation (max length, num return sequences)
45:21 – Recap: HF, open-source, parameters, quantization, Colab
46:26 – CTA: like, comment, subscribe
46:49 – Outro and next video teaser

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