Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteDo not start preparing a GPT-4o mini fine-tuning dataset until you have checked your account and the currently supported models. OpenAI’s model-optimization guide, accessed October 7, 2026, says the fine-tuning platform is winding down, is no longer available to new users, and will let existing users create jobs for “the coming months.” Its current methods table does not list GPT-4o mini as a supported base model. The guide gives no exact end date, so verify current access and model eligibility before committing to training work. OpenAI’s model-optimization guide
Can you fine-tune GPT-4o mini now?
Based on the model list in OpenAI’s guide as accessed October 7, 2026, GPT-4o mini is not listed as a supported base model for the fine-tuning methods shown. That means the documentation reviewed here does not establish that you can fine-tune GPT-4o mini. Model support and platform access can change; check the live guide and your account before designing a training workflow.
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Separate three questions before proceeding: can your account create a new job, is your chosen base model supported for the method you need, and how long will a resulting fine-tuned model remain available for inference? OpenAI says existing platform users can create jobs for the coming months, but does not give a calendar cutoff in the guide. It also says fine-tuned models remain available for inference until their base models are deprecated. OpenAI’s model-optimization guide
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Why evaluate before fine-tuning?
Fine-tuning is an optimization step, not the first diagnostic. OpenAI’s recommended sequence is to measure a baseline with evals, improve the prompt with suitable instructions and context, consider fine-tuning for a specific task, and evaluate again using test data representative of real-world inputs. Evals can be created through the API or dashboard, and can be written before the prompt is finalized. OpenAI’s model-optimization guide
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Build a useful baseline
As a practical design choice, hold back a representative set of inputs for comparison rather than using every example to shape prompts or training. Include ordinary requests as well as edge cases and inputs likely to trigger failures. Decide what counts as success before reviewing outputs; otherwise, it is easy to prefer the version that merely sounds better.
OpenAI’s eval guide describes test datasets containing inputs and reference labels, plus graders that check whether outputs meet defined criteria. For exact classifications or required formats, use deterministic checks where they fit. For qualities such as relevance or tone, use a defined rubric or human review rather than pretending that a single exact-match check captures quality. OpenAI’s guide to working with evals
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Which fine-tuning method fits the task?
The methods differ in the kind of feedback they use. The base-model entries below are those shown in OpenAI’s model-optimization guide accessed October 7, 2026; they are not evidence of GPT-4o mini support. Confirm the live list before selecting a method.
| Method | What it trains on | Base models listed in the guide | When it may fit |
|---|---|---|---|
| Supervised fine-tuning (SFT) | Examples of correct responses. | GPT-4.1 variants. | When you can provide reliable target outputs, such as for classification, nuanced translation, a specific output format, or improved instruction following. |
| Vision fine-tuning | Supervised examples with image inputs. | GPT-4o (2024-08-06). | When the task depends on image inputs; evaluate image-specific cases rather than assuming a text-only test set is sufficient. |
| Direct preference optimization (DPO) | Preferred and dispreferred responses. | GPT-4.1 variants. | When the objective is a preference such as summarization focus or tone and style. |
| Reinforcement fine-tuning (RFT) | Responses graded by an expert grader, with higher-scored results reinforced. | o4-mini (2025-04-16). | For complex, domain-specific reasoning when expert grading is feasible and the task has a sound grading method. |
These descriptions and model entries come from OpenAI’s model-optimization guide. In particular, examples of fine-tuning elsewhere in API documentation should not be taken as proof that a particular base model is currently available for a particular method.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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What data format and API workflow does fine-tuning use?
OpenAI’s API reference describes training data uploaded as JSONL and a fine-tuning job that selects a supported base model and method. The documented workflow is to collect examples, upload the training file, create a job, define a grader for RFT, and evaluate the result. The reference also describes job status, optional validation files, and metrics and checkpoints. Because API fields and supported models can change, use the live reference as the authority for the request schema rather than copying an old example. OpenAI Fine Tuning API reference
- Collect task-specific examples. For SFT, use examples that demonstrate the desired response; for DPO, provide preference pairs; for RFT, define an expert grader appropriate to the task.
- Prepare the file as JSONL. Validate each record against the current API schema and keep training data distinct from the representative test cases used for final comparison.
- Check eligibility, then create the job. Confirm account access, the supported base model, and the selected method in the live documentation before submitting.
- Monitor and evaluate. Follow job status and available metrics or checkpoints, then test the resulting model against the same held-out cases used for the prompt-only baseline.
Do not assume one training-set size or hyperparameter setting is right for every task. The reviewed official documentation does not establish a universal minimum number of examples or a generally best setting; it notes that learning-rate choices have risks and that defaults may be applied. Use the current API reference for available options, then let task-specific evaluation—not an unsupported rule of thumb—guide adjustments. OpenAI Fine Tuning API reference
How should you compare a tuned model with the prompt-only baseline?
Run both versions on the same representative held-out inputs under the same success criteria. Match each grader to the requirement being tested: string checks for exact output requirements, text similarity for appropriate comparison tasks, Python graders for programmable criteria, or model-based graders when their judgment is suitable. OpenAI’s API reference describes these grader options; the eval guide explains test data and grading. Fine-tuning API reference · Working with evals
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- For constrained labels or formats, measure whether outputs meet the required form and are correct.
- For nuanced qualities, apply a transparent rubric or human review and keep the criteria consistent across versions.
- Inspect individual failures and regressions alongside aggregate scores; a higher overall score can conceal a costly weakness on a particular input type.
Fine-tuning is worth pursuing only if the measured result improves the behavior that matters enough to justify the training and maintenance effort. The reviewed OpenAI sources promise neither a particular percentage gain nor a universal example count. Outcomes depend on the task, the quality of examples, eligibility, and what the evaluation actually shows.
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