Run Spark X2.5 4B locally with Ollama and use it as a coding assistant for a small, reviewable code task. The guide installs the runtime, pulls the exact publisher-published model, launches an interactive session, and verifies the model's output on a concrete coding request.
Run Spark X2.5 4B locally with Ollama and use it as a coding assistant for a small, reviewable code task. The guide installs the runtime, pulls the exact publisher-published model, launches an interactive session, and verifies the model's output on a concrete coding request.
This setup uses the package documented for this task. Review its source and supported platforms before starting.
The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.
This setup uses Ollama. The exact checkpoint format, context, and runtime overhead determine its memory need; a weight-file size alone is not a GPU recommendation.
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.
Install the Ollama runtime on your Windows, macOS, or Linux machine from https://ollama.com/download. Native spark2_5 support requires Ollama v0.34.1 or later, so make sure the installed version meets that minimum before continuing.
Use the repository-specific command from the publisher documentation to download the exact model and register it with Ollama. This uses the model identifier SparkLLM/Spark-X2.5-4B.
ollama run SparkLLM/Spark-X2.5-4BThe command above both pulls the model and starts an interactive Ollama session. Once the prompt appears, you can type coding requests directly, and the model will answer as a local coding assistant.
ollama run SparkLLM/Spark-X2.5-4BAt the interactive prompt, paste this request: "Write a Python function called `merge_intervals` that takes a list of [start, end] intervals and returns a new list with all overlapping intervals merged, sorted by start. Include a docstring and three example calls with expected outputs." Then press Enter and let the model generate the function, docstring, and examples.
Read the generated Python code and confirm it does what the request asked: the function is named `merge_intervals`, it merges overlapping intervals, it returns a sorted list, it includes a docstring, and it includes three example calls with expected outputs. Copy the generated code into a local file such as `merge_intervals.py` and run it with Python to verify the examples produce the expected results.
Ask for a self-contained example, run it in a disposable file, and inspect the code and output before using it in a real project.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
Upgrade Ollama to v0.34.1 or later, which adds native spark2_5 support, then run the model command again.
Confirm your network connection and that you have enough disk space for the 4B model weights, then re-run the same model command; Ollama resumes partial downloads.
Close other memory-heavy applications, then run the model command again. If the issue persists, try a shorter prompt so the model needs less context.
Reread the model output carefully, fix any obvious typos or missing imports in the generated file, and ask the model in the same session to correct the specific error you see.
Original instructions, model files, and compatibility notes behind this setup.
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