Install Ollama
Download and install Ollama for your operating system from the official Ollama website. Ollama is the runtime that will serve the private chat locally.
Install Ollama and run the Qwen2.5-Coder-7B-Instruct model locally with `ollama run qwen2.5-coder:7b`, then send a first coding prompt and confirm the model responds with generated code.
Install Ollama and run the Qwen2.5-Coder-7B-Instruct model locally with `ollama run qwen2.5-coder:7b`, then send a first coding prompt and confirm the model responds with generated code.
This setup uses the package documented for this task. Review its source and supported platforms before starting.
Save your machine in My Hardware to get an automatic starting choice. You can always choose any package yourself.
For the publisher's 4.7 GB Ollama text package at a short 4K context, we estimate at least 10 GB GPU memory or 16 GB Apple unified memory. For a more comfortable starting point, use 16 GB GPU memory or 24 GB unified memory. These are capacity estimates, not speed tests; longer context and other apps need additional headroom.
Pick your operating system. Every command below is for the selected package and runtime.
Install Ollama for this operating system before running the model command.
Download and install Ollama for your operating system from the official Ollama website. Ollama is the runtime that will serve the private chat locally.
Open a terminal and run the model. Ollama will download the exact `qwen2.5-coder:7b` package (about 4.7 GB) the first time, then start an interactive chat session.
ollama run qwen2.5-coder:7bAt the chat prompt, ask for a well-known algorithm so the response is easy to verify. Paste this as your message:
write a quick sort algorithm.The model should reply with a quick sort implementation in code. Confirm that the returned text contains a valid quicksort function or procedure, not just an acknowledgement or empty output. If you see generated code, the private chat is working.
Ask a short question with a known answer. Confirm the selected local model responds and verify the answer yourself before using it for private work.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
Restart your terminal (or your computer) so the updated PATH is loaded, then run the command again.
Run `ollama run qwen2.5-coder:7b` again; Ollama resumes the download automatically.
Close other memory-heavy applications, then retry. The 7B model needs additional memory beyond the 4.7 GB package for runtime and context.
Start a fresh session with `ollama run qwen2.5-coder:7b` and send the prompt exactly as shown.
Original instructions, model files, and compatibility notes behind this setup.
Save your machine to see a personalized rating and its reasoning.
Add my hardwareInstall a local runtime, run Qwen3.5 9B, confirm responses, and know when to choose the smaller 4B package.
Connect an open coding-capable model in Ollama to Cline, run a small repository task, and review the result locally.
Use the official FLUX.2 Klein 4B ComfyUI template with exact model files, a first prompt, and an output check.