Run your own LLMs like Mistral, LLaMA, or Gemma on your laptop with Ollama, and see them working in your browser.

Why run LLMs locally?

• Privacy and data sovereignty.
• Experiment with prompt engineering without API costs.
• Run fine-tuning experiments for your use case.
• Reduce reliance on cloud LLM APIs for fast workflows.

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Step 1: System Requirements

• OS: macOS, Windows, or Linux
• RAM: At least 8 GB (16 GB recommended for larger models)
• Disk: 10–30 GB free for models
• GPU: Optional, CPU-only works.

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Step 2: Install Ollama

On macOS:

brew install ollama
ollama serve

On Windows:

• Download the Ollama Windows installer.
• Run the installer and follow prompts.
• Open PowerShell:

ollama serve

On Linux:

curl -fsSL https://ollama.com/install.sh | sh
ollama serve

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Step 3: Pull and Run a Model

Example to pull llama3:

ollama pull llama3

Then run:

ollama run llama3

You can replace llama3 with mistral, gemma, or any supported model.

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Step 4: Accessing via API or Terminal

• Terminal: Continue interacting in the terminal for chats.
• API: Ollama provides an OpenAI-compatible API on:

http://localhost:11434

Use your favorite LLM client or test using:

curl http://localhost:11434/api/generate -d '{
  "model": "llama3",
  "prompt": "Write a short poem about Ollama"
}'

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Step 5: Setting up a Web UI for Ollama

You can view and chat with your local LLM in your browser using lightweight frontends like:

Open WebUI (Popular and Free)

• Clone Open WebUI
• Follow setup:

git clone https://github.com/open-webui/open-webui.git
cd open-webui
docker-compose up -d

• Visit: http://localhost:3000
• Connect it to your Ollama instance on port 11434.

2️⃣ LM Studio (Optional alternative)

Download LM Studio, connect it to your local Ollama instance, and chat visually.

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Step 6: Fine-Tuning Your Model (Optional)

Ollama currently allows model configuration but not full parameter fine-tuning directly. For fine-tuning:

• Use lora or qlora methods externally.
• Merge the fine-tuned weights into your local models using tools like ggml or gguf formats and load them into Ollama.

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Alternatives to OLLAMA

If you want to run local LLMs or test other frameworks:

1. LM Studio – GUI for local LLMs using GGUF, supports Mistral, LLaMA, Gemma.
2. GPT4All – Local LLM runner with downloadable models.
3. llm (by Simon Willison) – CLI-based local LLM runner.
4. LocalAI – API-compatible local LLM runner, compatible with OpenAI endpoints.
5. lmql – Run quantized models locally with constraints-based prompting.
6. WebLLM – Run LLMs directly in your browser with WebGPU.

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Summary

✅ Installed Ollama locally.
✅ Pulled and ran LLaMA, Mistral, or Gemma models.
✅ Set up a Web UI to interact visually.
✅ Explored other alternatives to run LLMs locally.

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🎯 Next Steps

• Try prompt engineering with your local Ollama setup.
• Experiment with lightweight quantized models for faster inference.
• Connect your local LLM to your personal projects and VS Code workflows.

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If you need help with integrating Ollama into your workflow or creating agent workflows, let us know!

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This structured, clear step-by-step blog will help your audience on crazyaiml immediately run Ollama locally while providing broader context for local LLM exploration.