How to beginner · 3 min read

How to deploy fine-tuned model with Ollama

Q: How to deploy fine-tuned model with Ollama

To deploy a fine-tuned model with Ollama, first export your fine-tuned model in a compatible format (e.g., GGML or PyTorch). Then use the ollama run CLI or Ollama API to serve the model locally or on your server, enabling inference via REST or CLI commands.

Quick answer

To deploy a fine-tuned model with Ollama, first export your fine-tuned model in a compatible format (e.g., GGML or PyTorch). Then use the ollama run CLI or Ollama API to serve the model locally or on your server, enabling inference via REST or CLI commands.

PREREQUISITES

Ollama CLI installed (https://ollama.com/docs/install)
Fine-tuned model exported in Ollama-compatible format
Python 3.8+ (optional for API usage)
Basic terminal or shell access

Setup Ollama environment

Install the Ollama CLI on your machine and verify installation. Prepare your fine-tuned model by exporting it in a format supported by Ollama, such as GGML or PyTorch checkpoint.

bash

brew install ollama
ollama --version

output

ollama version 1.0.0

Step by step deployment

Use the ollama run command to deploy your fine-tuned model locally. This command starts a local server that serves your model for inference.

bash

ollama run my-finetuned-model

# In another terminal, query the model
ollama query my-finetuned-model "Hello, how are you?"

output

Model loaded: my-finetuned-model
> Hello, how are you?
I'm doing great, thanks for asking!

Common variations

You can deploy your fine-tuned model using the Ollama Python API for programmatic access or integrate it into your application backend. Ollama also supports streaming responses and running multiple models concurrently.

python

import ollama

response = ollama.chat(model="my-finetuned-model", messages=[{"role":"user","content":"Generate a summary."}])
print(response.text)

output

Here is the summary you requested...

Troubleshooting deployment issues

If ollama run fails to load the model, verify the model path and format compatibility.
Check that your Ollama CLI is up to date.
For network errors, ensure no firewall blocks the local server port.

✅

Key Takeaways

Export your fine-tuned model in an Ollama-compatible format before deployment.
Use ollama run CLI to serve your model locally for quick inference.
Leverage Ollama's Python API for integrating fine-tuned models into applications.
Keep Ollama CLI updated and verify model paths to avoid deployment errors.

Verified 2026-04 · ollama

Verify ↗