A Python fine-tuned LLM is ready to advance when it clearly beats the unchanged base model on a held-out set that represents your real task, does not regress on the slices that matter most, and is scored by graders that have been checked against human judgment. No single number makes that call. The gate is a set of recorded comparisons that you can rerun whenever the model, the data, the prompt, or the grader changes.
Start with the decision, not the metric
Before writing any evaluation code, write three sentences: the capability or behavior the gate protects, the outcome a user should get, and what counts as a regression. Without these, teams tend to pick whichever score is easiest to compute and then argue about whether it matters.
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Consider a fine-tune that turns natural-language questions into SQL for an internal billing database. The capability is producing SQL that runs against the real schema. The user outcome is a query that returns the rows they asked for. A regression is any drop on the invoice-table questions, or any increase in statements that modify data when the user only asked to read it. Those three sentences dictate the dataset, the graders, and the thresholds that follow.
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OpenAI’s Evaluation best practices guide frames the work as a sequence: define the objective, build a dataset, choose metrics, run and compare, then evaluate continuously. The sequence is useful because each step depends on the one before it. The page does not show a publication date, so check it before relying on specific feature details.
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Build a held-out set you never train on
The decision set is the foundation of the gate. If it overlaps with fine-tuning data, or if you repeatedly select checkpoints against the same fixed test file, the scores will drift upward without the model getting better on new inputs.
Where examples should come from
A representative set usually mixes several sources: production feedback where you have it, examples written by domain experts, synthetic examples for coverage, historical cases that caused past incidents, and domain-specific data that reflects how the task is really phrased. Expert annotation matters most when the person scoring the output lacks domain knowledge, such as deciding whether a clinical summary omits a material finding.
Cover typical, edge, and adversarial cases
Label each example by type. Typical cases show whether the model handles the common path. Edge cases include unusual units, empty results, or ambiguous wording. Adversarial cases include prompts designed to trigger the failure you care about most, such as an instruction to ignore the schema. Report results per type, because an aggregate can look healthy while adversarial cases collapse.
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Hold out a fixed decision set and do not tune on it. When you need to iterate, create a new held-out set from recent failures and retire the old one from selection decisions. Treat the decision set as a measurement instrument: if you change it, record the change, because scores from different versions of the set cannot be compared directly.
Match the grader to the criterion
Every grader makes an assumption about what a correct output looks like. The OpenAI dataset guide describes exact-match checks, text similarity, model-based scoring, and custom code as separate tools, and the choice should follow the criterion rather than convenience. The table below summarizes the trade-offs.
| Criterion | Grader | Use when | Main weakness |
|---|---|---|---|
| Output must equal a reference (a label, a fixed JSON key, a canonical value) | Exact-match check | Only one correct surface form exists | Penalizes correct paraphrases and formatting differences |
| Output should be semantically close to a reference, but identity is not required | Text similarity | Several wordings are acceptable | Can miss relevance or factual errors |
| Subjective quality such as coherence, tone, or helpfulness | Model grader returning a score or label | No programmatic test exists | Must be validated against human judgments before use |
| A rule that can be checked in code (valid syntax, passes unit tests, matches a schema) | Custom Python code | The rule can be written down precisely | Checks only what you encoded, so the encoding must be correct |
OpenAI’s guidance, which appears in the Getting started with datasets documentation, makes the same point: choose deterministic checks wherever the desired outcome can be expressed programmatically, and reserve model graders for judgments that cannot.
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A worked Python grader for code-generation tasks
If your fine-tune writes Python functions, a grader can run the output against test cases. The example below returns a fraction of passed cases. It does not execute untrusted model output in the current process, which is unsafe; in production, run it in a sandboxed subprocess with a timeout.
import ast
def grade_completion(source: str, func_name: str, cases: list) -> float:
"""Return the fraction of (args, expected) cases the generated function passes."""
try:
ast.parse(source)
except SyntaxError:
return 0.0
namespace = {}
try:
exec(source, namespace) # sandbox this call in production
fn = namespace[func_name]
except Exception:
return 0.0
passed = 0
for args, expected in cases:
try:
if fn(*args) == expected:
passed += 1
except Exception:
pass
return passed / len(cases)A fractional score gives partial credit, which is useful for diagnosis. It can also hide a requirement that must hold every time, such as “never emits a DROP statement.” Keep hard requirements as separate pass-or-fail checks rather than folding them into an average.
Grading for reinforcement fine-tuning
If the fine-tune uses reinforcement fine-tuning rather than supervised data, the grader does double duty: it is both the metric and the training signal. OpenAI’s Reinforcement fine-tuning use cases page states: “Clear, robust grading schemes are essential for RFT.” Verify that the task is gradable before training, because a task whose baseline score is already at the maximum or minimum offers no signal to learn from.
Validate model graders before trusting them
A model grader is only as reliable as its agreement with people. Have reviewers label a sample of outputs independently, compare the grader’s labels with theirs, and examine the disagreements. Include examples of good, middling, and poor outputs so the grader is tested across the range rather than only at the extremes. Rerun the grader on the same inputs to check consistency.
The sources behind this guide do not provide a universal agreement level that qualifies a model grader. Choose the level from the cost of a wrong decision: a grader that only sorts internal drafts can tolerate more disagreement than one that gates a customer-facing release.
Run baseline and candidate under identical conditions
A comparison is meaningful only when everything except the model is held constant. Use this sequence:
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- Freeze the decision-set version, the prompt template, the grader version, and the decoding settings such as temperature and maximum output tokens.
- Run the unchanged base model on the full decision set.
- Run the fine-tuned candidate on the same set with the same settings.
- Record the task name, prompt format, metric, number of few-shot examples, model revision, and uncertainty for each run.
- Compare aggregate scores, then open the per-example outcomes where the two models disagree.
Read the uncertainty before the difference. A small aggregate gain that is smaller than the reported standard error is not evidence of improvement by itself. A gain concentrated in one slice while another slice drops is a regression that the aggregate hides. Inspect a sample of the disagreements by hand, since the reason for a score change is often visible only in the outputs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Python tooling for the gate
EleutherAI LM Evaluation Harness
The LM Evaluation Harness repository provides a Python API and a command-line interface, standard academic tasks, support for custom prompts and metrics, and several model backends. Its quickstart installs the Hugging Face backend with the command below.
pip install lm-eval[hf]A quick test on a small slice of a standard task looks like this. The model and task shown are examples; substitute your own checkpoint and a task that matches your workload.
lm_eval --model hf --model_args pretrained=EleutherAI/pythia-160m --tasks hellaswag --limit 100The --limit 100 flag restricts the run to 100 examples and is intended only as a fast check. Remove it for a full run before comparing results. The Python equivalent is lm_eval.simple_evaluate(...), which the quickstart demonstrates. The harness also supports evaluating adapters such as LoRA when the underlying stack supports them.
The harness is most useful when you need standardized tasks or a local model-backend workflow. Before comparing its numbers with published or internal scores, confirm the task configuration, prompt formatting, model revision, and inference settings. Two runs that look identical can differ in any of these.
Hugging Face: Evaluate and LightEval
The Evaluate on the Hub documentation covers metrics and model evaluation, and identifies LightEval as a more recently maintained approach to LLM evaluation on the Hub. The Hub also displays community leaderboards and model cards. Model-card evaluation results may be reported by the model’s author, so separate author-reported numbers from independent evaluations when you read them.
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Hosted datasets and graders
OpenAI’s dataset feature, described in the Getting started with datasets guide, supports prompt iteration against shared datasets, human annotations, automated graders, and export to evaluations for larger asynchronous runs with version tracking. It is an option rather than a requirement for a Python-only gate. Feature availability and platform status change, so confirm them in the current documentation before building a process around them.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Option | Strongest fit | Limit to plan for |
|---|---|---|
| LM Evaluation Harness | Standardized tasks, local or model-backend runs, adapter evaluation | Results depend on task configuration and prompt format, which must be recorded |
| Hugging Face Evaluate and LightEval | Metrics and Hub-based evaluation workflows | Model-card results may be author-reported rather than independent |
| OpenAI datasets and graders | Prompt iteration, annotations, and version-tracked hosted runs | Hosted feature; availability should be checked in current docs |
Set thresholds from risk, not borrowed numbers
A threshold from another task does not transfer. OpenAI’s Evaluation best practices page includes an illustrative summarization design: 1,000 held-out reference transcript-to-summary examples, with ROUGE-L of at least 0.40 and coherence of at least 80% as scored by G-Eval. That example shows how a threshold can be tied to a task. It is not a general release criterion, and the sources behind this guide do not give a general sample size or a universal pass mark.
Set thresholds per slice as well as overall. A release can be blocked by a single high-risk slice even when the aggregate improves, which is the practical reason the gate should report by slice and by example rather than as one score.
Failure modes and how to respond
| Failure | What it looks like | Response |
|---|---|---|
| Training and evaluation leakage | Decision-set scores far above live performance | Rebuild the held-out set from data the model never saw, and stop selecting against it |
| Unrepresentative examples | Production failures that the decision set never contains | Add the failing cases and rerun both baseline and candidate |
| Metric mismatch | Strong lexical scores while outputs are irrelevant or wrong | Add criterion-based graders that match the intended outcome |
| Unvalidated model grader | Grader labels disagree with reviewers | Revalidate against human labels before trusting the grader |
| Reward hacking | Score rises while outputs exploit the rule rather than solve the task | Read failing and high-scoring cases; use gradual scoring plus separate hard checks |
| Class imbalance | The model favors an overrepresented label | Balance the examples or weight rare cases deliberately |
| Ceiling or floor effects | Every run scores the maximum or minimum | Change the task difficulty or grader; there is no useful learning signal at the limit |
| Benchmark overclaim | A strong public benchmark result with weak product results | Treat benchmarks as comparisons under a stated protocol, and report the benchmark, dataset version, prompt, shots, metric, and model revision |
Keep the gate running after release
OpenAI’s Evaluation best practices page recommends: “Set up continuous evaluation (CE) to run evals on every change, monitor your app to identify new cases of nondeterminism, and grow the eval set over time.” In practice, that means rerunning the gate whenever the model revision, training data, prompt, decoding settings, or grader changes, and treating nondeterministic outputs in production as candidates for new decision-set cases. A gate that runs only once before launch will not catch the regressions that appear after the next change.
Keep the run records with the model. The record of the task, prompt, metric, uncertainty, and per-slice results is what lets you explain a future change in quality, and it is the evidence you need when a release is questioned later.
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