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⚡ OPENLLM HUB • OPEN-SOURCE AI • LLM DISCOVERY
──────────────────────────────────────────────────────────────────────────────
All-in-one open-source LLM hub to explore, test, benchmark,
and download custom AI models for free.
🌐 Live Demo → openllm-hub.vercel.app
OpenLLM Hub is a free, open-source platform to explore, test, benchmark, and download custom fine-tuned AI language models. It supports multiple model formats including GGUF, Safetensors, and Ollama — all available for free download.
Whether you're a researcher, developer, or AI enthusiast — OpenLLM Hub gives you access to custom fine-tuned LLMs optimized for Bengali NLP, Coding, Reasoning, and Edge devices.
- 🔍 Explore Models — Browse a curated collection of custom fine-tuned LLMs
- 🧪 Test Models — Try out models directly before downloading
- 📊 Benchmark — Compare model performance side by side
- 📥 Free Downloads — Download models in GGUF, Safetensors, and Ollama formats
- 🌏 Bengali NLP — Custom models fine-tuned for Bengali language processing
- 💻 Coding Models — Specialized models for code generation and completion
- 🧠 Reasoning Models — Advanced reasoning and problem-solving models
- 📱 Edge Device Models — Lightweight models optimized for low-resource devices
- 💰 100% Free — No subscription, no signup required
| Format | Description | Best For |
|---|---|---|
| 🔷 GGUF | Optimized quantized format | Running locally with llama.cpp |
| 🟣 Safetensors | Safe, fast tensor format | Python/HuggingFace projects |
| 🦙 Ollama | Ready-to-run Ollama format | Easy local deployment |
| Category | Description |
|---|---|
| 🌏 Bengali NLP | Fine-tuned for Bengali language understanding and generation |
| 💻 Coding | Optimized for code generation, completion, and debugging |
| 🧠 Reasoning | Advanced logical reasoning and problem solving |
| 📱 Edge Devices | Lightweight models for low-RAM and mobile devices |
# Pull and run directly
ollama run <model-name>./llama-cli -m model.gguf -p "Your prompt here"from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("model-path")
tokenizer = AutoTokenizer.from_pretrained("model-path")👉 Download Node.js — Choose the LTS version
Step 1 — Download the project:
Click the green "Code" button → "Download ZIP" → Extract the ZIP file
Step 2 — Open in VS Code:
Open VS Code → File → Open Folder → Select the extracted folder
Step 3 — Install dependencies:
npm installStep 4 — Start the app:
npm run devStep 5 — Open your browser:
http://localhost:5173
- Install Node.js and VS Code from the links above
- Download the project ZIP → Extract → Open VS Code → Open the folder
- Press Ctrl + Shift + P → type "Chat" → open AI assistant
- Paste this prompt:
I downloaded OpenLLM Hub. Please help me:
1. Run: npm install
2. Run: npm run dev
3. Open http://localhost:5173 in my browser
Do each step one by one and fix any errors.
| Platform | Steps |
|---|---|
| Vercel | Connect GitHub repo → Auto-deploy instantly |
| Netlify | Connect GitHub repo → Auto-deploy instantly |
| GitHub Pages | Run npm run build → Upload dist/ folder |
Q: Is OpenLLM Hub really free?
Yes! Completely free. All models are available for free download, no account needed.
Q: What is GGUF format?
GGUF is an optimized quantized model format for running LLMs locally with llama.cpp. It's fast, efficient, and works on regular computers without a GPU.
Q: Can I use these models commercially?
It depends on each model's individual license. Check the model page for specific license details.
Q: Do I need a GPU to run these models?
Not necessarily! Edge device models are designed to run on CPU-only systems. GGUF models with quantization also work well on regular computers.
Q: How do I run models with Ollama?
Install Ollama from ollama.com, then use
ollama run <model-name>in your terminal.
This project is licensed under the MIT License — free to use, share, and modify.
Rongon Kairy — @RongonKairy
⭐ If you like this project, please give it a star on GitHub! READMEEOF