LLM fine-tuning with evidence before and after training
Adapt a language model to a task, response format or domain-specific behavior with OctOpus. Prepare examples, choose a feasible training strategy and compare the tuned result with the base model. Keep training code, evaluation results and saved artifacts together as you develop the model for an application.
Choose the behavior you want to improve
Fine-tuning is useful when you have examples of the responses or behavior a model should learn. Define a task such as structured extraction, support-answer formatting or a specific writing style. For answers that need changing company information, also consider the company-knowledge workflow, which retrieves relevant documents at answer time.
Prepare examples in a supported format
Provide prompt-and-completion pairs, instruction-and-output examples, message conversations or text for supported continued-training paths. OctOpus checks the dataset contract before training. Keep the examples consistent with the intended task, include the difficult cases, and reserve evaluation examples that were not used to teach the model.
Match the model and strategy to your resources
OctOpus uses the selected model, dataset and available hardware to prepare a feasible training strategy. Review the base model, sequence length, compute requirements and configured limits before launch. The saved configuration makes the run inspectable and gives the next experiment a clear starting point for comparison.
Compare the base and tuned model
Evaluate both models on the same held-out task. The workflow records available measures such as loss, response quality, format adherence and general regression probes, together with examples you can inspect. Check model access, licensing and supported training compatibility, then use the evaluation evidence to decide whether the result fits the intended application.
Keep the code and artifact ready for the next step
Open the training source, configuration and saved results from the project. Supported fine-tuning paths can produce an adapter used with its specified base model. Preserve that dependency with the evaluation report, verify reload through a compatible serving path, and connect the result to the application you are developing.
Example brief for structured support answers
“Adapt this model to answer support requests in our required JSON format. Use the reviewed examples and compare it with the base on held-out requests.” Inspect the responses, format compliance and difficult cases together. Refine the examples or training strategy based on that evidence, then evaluate the next version on the same task.
Key capabilities
- Dataset checks for supported training formats.
- Resource-aware base and strategy selection.
- Base-vs-tuned evaluation with examples and regression checks.
- Training code, artifact dependencies and results saved together.
Frequently asked questions
Is fine-tuning the same as giving an assistant company documents?
No. Fine-tuning changes model behavior from training examples. A company-knowledge workflow retrieves relevant documents at answer time. Choose according to the problem and how often the information changes.
Is the resulting adapter a standalone model?
Not necessarily. An adapter depends on the specified base model and compatible serving configuration. Preserve those dependencies and verify reload before using it in an application.
Can I assume the tuned model is better if training completes?
No. Compare it with the base on the same held-out task and inspect regressions. Completion confirms that a job ended, not that its output is suitable for deployment.