The official Ollama FAQ documents server configuration through environment variables, including OLLAMA_HOST. Follow the instructions for your installation type when reviewing its configuration.

• Share your OS, Ollama version, and client or integration version.
• Describe the connection path and whether it stays on one machine.
• Provide the sanitized endpoint, exact error, and server-log timestamp.
• State whether a direct request from the client environment succeeds.
• List the checks already attempted and what each returned.

Keep credentials out of examples and describe the intended access boundary. Please share diagnostic observations before applying a configuration change, so replies can focus on the specific point where the connection fails.

For a useful help request, collect the output while the relevant model is loaded and include:

• Ollama version and the exact model identifier.
• Operating system, GPU, available GPU memory, and system RAM.
• The context setting and other concurrent model requests.
• A short, sanitized prompt and the behavior you are investigating.
• Relevant server-log messages from the same attempt.

If you share a screenshot, check it for private paths and other sensitive details. Avoid inferring a cause from placement alone; explain the symptom and compare observations from a controlled test.

What does your setup report, and which part of the behavior would you like help understanding?

Ollama's official troubleshooting guide describes where to find server logs. It lists the Ollama folder under %LOCALAPPDATA% for Windows logs, ~/.ollama/logs/server.log on macOS, and journalctl -u ollama for Linux installations managed by systemd. Container logs are available through the container runtime.

Share only the short log section around the failure, along with its timestamp. Describe whether the problem occurs in a direct local request, an application integration, or both.

• What did you expect?
• What happened?
• What changed recently?
• Which checks have you already tried?

Keep proposed diagnoses separate from confirmed findings, and update your thread with the check that eventually identified the issue.

• The page URL and the section or link involved.
• What you expected to see or happen.
• What happened instead.
• The date observed and, for interface issues, your browser and device.
• A screenshot or short excerpt with private information removed.

For a technical guide, include the tool version you used and the exact step that differs. If you have an official documentation link supporting a correction, add it alongside the relevant claim.

For a problem with a community tool, share the non-sensitive inputs and the result you expected. Keep one issue per reply where practical. If you find a security-sensitive issue, describe the affected area without posting credentials or instructions that expose private data.

A useful request explains:

• The task you are trying to complete.
• Your starting knowledge and relevant setup.
• The step where existing explanations leave you stuck.
• The result you would like to reach.
• Whether a written guide, worked example, checklist, or calculator would help most.

For example, a hypothetical request might be a walkthrough showing how an editor reviews a generated WordPress draft, including the checks used before publication.

Link an existing site page if the request is an extension or clarification. If you can contribute a small example or test a draft guide, mention that too. Suggestions help shape the editorial backlog; please state any deadline as context rather than assuming a delivery date.

You can start with any of these:

• Your current AI experience, including if you are just starting.
• A task or project you are interested in.
• The tools or local setup you are exploring.
• One question you hope the community can help answer.
• A topic where you would enjoy sharing knowledge.

Share only personal details you are comfortable making public. A short introduction is plenty, and a public profile or project link is optional.

If your introduction includes a technical problem, describe the goal briefly here and open a focused question in the relevant help section with your setup and sanitized error. That gives the problem its own discussion while keeping this thread easy to browse.

Propose a test case with:

• A task name and the capability you want to examine.
• A public, self-authored, or clearly synthetic input.
• The complete instructions.
• An expected result or scoring rubric.
• Known ambiguities and what the test does not measure.

Possible task shapes include following a fixed output format, extracting fields from supplied text, or identifying missing information. These are suggestions for test design, with no model performance implied.

Keep cases short enough for another member to inspect manually. When sharing a result, include the model identifier, settings, date, and output. Which first test would help you evaluate models for the work you actually do?

A useful contribution includes:

• The official announcement or release-note link and publication date.
• The exact product and version or model identifier.
• The change described by the maintainer.
• The workflow you think could be affected.
• A small test you ran or propose to run.
• Observed results, limitations, and questions still open.

If you compare before and after behavior, record both environments and use the same inputs. Label reports from other people as second-hand and link the original source.

What would you need to verify before adopting the update in your own project?

• Describe the input and the output you need.
• List required features and constraints.
• Name the candidates and the versions or access tiers being compared.
• Prepare the same shareable tasks for each candidate.
• Define how you will score correctness, usability, and review effort.
• Record relevant costs from current official sources and note the date checked.

Keep provider claims separate from results you observed. If you have only read an announcement, link it and explain what you still want to test.

Share the comparison plan or a completed result set, including limitations. Please explain the use case behind any preference so another reader can judge whether the conclusion applies to their workflow.

• Goal: What the workflow was meant to do.
• Symptom: What failed and how you noticed.
• Evidence: The logs, outputs, or observations you examined.
• Investigation: What you tested, including unsuccessful attempts.
• Change: What you modified and why.
• Verification: How you checked the result and what remains uncertain.

Keep private data and credentials out of examples. If the issue is unresolved, say so and identify the next observation you need.

Replies should distinguish a confirmed cause from a plausible explanation. A useful lesson can be a better test or recovery procedure even when the project is still unfinished.