# What's your method for benchmarking?
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**Author:**  [@TensorVizion](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FTensorVizion)%5C
**Post date:** [June 24, 2026, 6:02pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhats-your-method-for-benchmarking%2F177127%2F1 "2026-06-24T18:02:04Z")
Hey everyone, my question today is simple.
How do you benchmark your models?
I’m just getting into the world of fine tuning and i want to know how to benchmark my models after i am done making them.
Any help would be appreciated thank you
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**Author:**  [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [June 25, 2026, 9:27am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhats-your-method-for-benchmarking%2F177127%2F2 "2026-06-25T09:27:19Z")
Uh… there are a lot of ways to do this, so I’d split it up a bit. My first suggestion would be: measure the capability you actually care about first.
* * *
Short version:
**Keep a held-out test set that was not used for training, run both the base model and your fine-tuned model on it under the same prompts/settings, and compare them with metrics that match your actual task.**
Public benchmarks and leaderboards are useful, but I would treat them as a second layer. They are good for orientation, not always proof that the fine-tune worked for your use case.
## A practical first workflow
1. **Define the target behavior**
2. **Create a held-out eval set**
3. **Run the base model**
4. **Run the fine-tuned model**
5. **Compare with task-appropriate metrics**
6. **Inspect individual failures**
7. **Add public benchmarks if they match the goal**
* * *
## 1. Start with your actual task, not a leaderboard
Before choosing a benchmark, I would write the goal in plain language.
| If the fine-tune is meant to improve… | A better first eval is usually… |
| --- | --- |
| Support-ticket classification | Accuracy / F1 on held-out tickets |
| Domain QA | Held-out question/answer examples |
| JSON or structured output | JSON validity, schema validity, field accuracy |
| Chat helpfulness | Pairwise comparison, rubric, human spot-checks |
| Summarization | Coverage, factuality, maybe ROUGE/BERTScore as supporting metrics |
| Code generation | Unit tests, hidden tests, code-specific benchmark tasks |
| General reasoning | Public reasoning benchmarks may be relevant |
The key question is:
> Does this benchmark measure the thing I actually fine-tuned for?
A general benchmark can be useful, but it might not see the thing you changed. A small domain fine-tune might help your actual use case without moving a public leaderboard score much. Also, a model can do well on a public benchmark and still be poor at your format, domain, or workflow.
Useful starting reference:
- Hugging Face LLM Course, evaluation section: [https://huggingface.co/learn/llm-course/chapter11/5](https://huggingface.co/learn/llm-course/chapter11/5)
* * *
## 2. Minimal checklist
If you only do one thing, I would do this:
| Step | Check |
| --- | --- |
| Goal | What should improve? |
| Data | Are eval examples excluded from training? |
| Baseline | Did you run the base model too? |
| Fairness | Are prompt/settings the same? |
| Metric | Does the metric match the task? |
| Samples | Did you inspect actual outputs? |
| Regression | Did anything get worse? |
| Notes | Can someone else reproduce the setup? |
That is already a useful benchmark for many fine-tunes.
> **More detail: building a held-out eval set**
>
> A held-out eval set means examples that were not used during fine-tuning.
>
> For a small first pass, I would include several slices:
>
> | Eval slice | Purpose |
> | --- | --- |
> | Normal cases | Representative examples from the real task |
> | Hard cases | Inputs the base model often gets wrong |
> | Regression cases | Inputs the base model already handles well |
> | Format cases | Inputs that test exact response structure |
> | Edge cases | Ambiguous, long, missing-context, or messy inputs |
>
> This does not need to be huge at the beginning. A small, carefully chosen set is often better than a large but unclear one.
>
> For example, if you fine-tuned a model to answer internal policy questions, I would not start with MMLU. I would first create a small set of policy questions with known expected answers, including:
>
> - easy questions
> - tricky questions
> - questions where the answer is not in the source material
> - questions where the model should refuse or say it does not know
> - questions the base model currently gets wrong
> - questions the base model currently gets right
>
> If you evaluate on examples that were used in training, the result may mostly measure memorization or overfitting.
> **More detail: keeping the comparison fair**
>
> A common mistake is to evaluate only the fine-tuned model. That gives a score, but it does not answer:
>
> > Did fine-tuning help compared with the original model?
>
> I would compare at least:
>
> | Model | Purpose |
> | --- | --- |
> | Base model | Baseline |
> | Fine-tuned model | Measures the effect of fine-tuning |
> | Optional similar model | Extra reference point |
>
> Try to keep these fixed:
>
> - prompt template
> - chat template
> - few-shot examples, if any
> - system message
> - decoding parameters
> - `temperature`
> - `top_p`
> - `max_new_tokens`
> - retrieval context, if this is RAG
> - quantization / backend, if possible
> - model revision
> - adapter revision, if using LoRA/PEFT
>
> Otherwise, you may accidentally benchmark a prompt change, decoding change, or serving change instead of the fine-tune.
>
> For LoRA/PEFT models, I would also be careful to record whether you evaluated:
>
> - the base model alone
> - the base model plus adapter
> - a merged model
> - a quantized version
> - a served endpoint version
>
> Those can behave differently enough that the exact setup matters.
* * *
## 3. Pick metrics by task
There is no single metric that works for all fine-tuned LLMs.
A useful reference is Hugging Face Evaluate’s metric guide:
- [https://huggingface.co/docs/evaluate/en/choosing\_a\_metric](https://huggingface.co/docs/evaluate/en/choosing_a_metric)
Very roughly:
| Task type | Possible metrics / checks |
| --- | --- |
| Classification | accuracy, precision, recall, F1 |
| Extraction | exact match, field-level accuracy, schema validity |
| QA with known answers | exact match, F1, semantic correctness, human spot-check |
| Summarization | ROUGE/BERTScore can help, but inspect factuality and coverage |
| Translation | BLEU / chrF / COMET, depending on setup |
| Open-ended chat | rubric scoring, pairwise comparison, human or LLM judge |
| Instruction following | constraint pass rate, format adherence |
| Coding | unit tests, pass rate, hidden tests, code benchmark tasks |
| RAG/document QA | answer correctness, context recall, faithfulness, citation usefulness |
| Deployment | latency, throughput, VRAM, cost per request |
I would avoid relying on only one weak signal, such as:
- “the answer looks good to me”
- training loss
- validation loss
- one public leaderboard score
- one cherry-picked demo
Those can be useful, but they are not the whole evaluation.
> **More detail: eval loss vs task success**
>
> If you trained with `Trainer`, `SFTTrainer`, or another training loop, validation/eval loss is useful as a training signal. It can help you notice overfitting or training instability.
>
> But for final evaluation, especially generation tasks, I would not stop at eval loss.
>
> A lower loss does not automatically mean:
>
> - the answer is factually better
> - the model follows your requested format better
> - the model is safer
> - the model is less hallucination-prone
> - the model is better for your actual users
> - the model did not regress on general behavior
>
> For generation tasks, save actual model outputs and inspect them.
>
> Relevant docs:
>
> - Transformers `Trainer`: [https://huggingface.co/docs/transformers/en/main\_classes/trainer](https://huggingface.co/docs/transformers/en/main_classes/trainer)
> - TRL `SFTTrainer`: [https://huggingface.co/docs/trl/en/sft\_trainer](https://huggingface.co/docs/trl/en/sft_trainer)
* * *
## 4. Inspect failures, not only scores
Aggregate scores are useful, but a lot of the value comes from looking at individual examples.
For each output, I would save something like:
| Field | Why |
| --- | --- |
| Input prompt | Reproduces the case |
| Expected answer / rubric | Defines what “good” means |
| Base model output | Baseline |
| Fine-tuned output | Comparison |
| Score / pass-fail | Aggregate metric |
| Short error label | Helps find patterns |
Useful error labels might be:
- wrong answer
- incomplete answer
- hallucinated detail
- ignored instruction
- wrong format
- invalid JSON
- too verbose
- too short
- unsafe refusal
- should have refused but did not
- correct answer but bad explanation
- regression from base model
This is one reason sample-level logging is useful. Lighteval, for example, emphasizes sample-by-sample results for deeper inspection:
- [https://huggingface.co/docs/lighteval/en/index](https://huggingface.co/docs/lighteval/en/index)
* * *
## 5. Public benchmarks are a second layer
Once your own task-specific eval is in place, public benchmarks can be useful.
But I would choose them by target capability.
| If you care about… | Look at… | Main caution |
| --- | --- | --- |
| General reasoning / knowledge | MMLU-Pro, GPQA, LiveBench, HELM-like suites | May not match your domain |
| Instruction following | IFEval-style tests | Measures verifiable constraints, not all helpfulness |
| Open-ended chat quality | MT-Bench / Arena-style pairwise evals / AlpacaEval-like setups | Judge and preference biases matter |
| Coding | LiveCodeBench, BigCodeBench, SWE-bench depending on task | Code completion and repo-level issue fixing are different |
| RAG / document QA | RAGAS-style component metrics | Retrieval and generation should be separated |
| Deployment | latency, throughput, VRAM, cost | Not the same as quality |
Common benchmark-running tools:
- Lighteval: [https://huggingface.co/docs/lighteval/en/index](https://huggingface.co/docs/lighteval/en/index)
- EleutherAI lm-evaluation-harness: [https://github.com/EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
I would not start with:
> Which leaderboard should I optimize for?
I would start with:
> Which capability did I fine-tune for, and does this benchmark actually measure it?
> **More detail: public benchmark map**
>
> Public benchmarks are useful, but they answer different questions.
>
> | Benchmark / eval style | Closer to measuring | Main caution |
> | --- | --- | --- |
> | MMLU-Pro / GPQA | Broad academic knowledge and reasoning | Not necessarily your domain task |
> | LiveBench | General capability with newer, contamination-aware questions | Still not a replacement for your own eval set |
> | IFEval | Verifiable instruction-following constraints | Does not measure all helpfulness or factuality |
> | Chatbot Arena / MT-Bench style evals | Open-ended assistant preference | Judge bias and preference bias matter |
> | SWE-bench | Repo-level software engineering issue solving | Not the same as simple code completion |
> | Serving benchmarks | Latency, throughput, cost, memory | Not a direct quality benchmark |
>
> Useful links:
>
> - LiveBench: [https://github.com/livebench/livebench](https://github.com/livebench/livebench)
> - IFEval dataset: [https://huggingface.co/datasets/google/IFEval](https://huggingface.co/datasets/google/IFEval)
> - MT-Bench / Chatbot Arena paper: [https://arxiv.org/abs/2306.05685](https://arxiv.org/abs/2306.05685)
>
> The trap is treating a leaderboard score as universal. It is not. A benchmark has:
>
> - a task distribution
> - a prompt format
> - a scoring rule
> - model/backend assumptions
> - possible contamination issues
> - sometimes hidden or changing evaluation details
>
> So I would use leaderboard scores as orientation, not as the only decision rule.
* * *
## 6. Special cases
The right evaluation changes a lot depending on what kind of fine-tune this is.
> **If the task is open-ended chat**
>
> For open-ended chat, there may not be one exact correct answer. In that case, I would compare outputs with a small rubric.
>
> Example rubric:
>
> | Criterion | Question |
> | --- | --- |
> | Correctness | Is the answer factually right? |
> | Completeness | Does it answer the whole question? |
> | Instruction following | Did it follow user constraints? |
> | Grounding | If sources/context are provided, does it stay grounded? |
> | Format | Does it output the requested structure? |
> | Safety | Does it refuse only when appropriate? |
> | Usefulness | Would a user know what to do next? |
>
> A practical method:
>
> 1. Take the same prompt.
> 2. Generate an answer from the base model.
> 3. Generate an answer from the fine-tuned model.
> 4. Hide which is which.
> 5. Compare them side by side.
> 6. Record the winner and the reason.
>
> You can use a human judge, an LLM judge, or both. If using an LLM judge, I would treat it as a useful signal, not ground truth.
>
> References:
>
> - HF cookbook on LLM-as-a-judge: [https://huggingface.co/learn/cookbook/llm\_judge](https://huggingface.co/learn/cookbook/llm_judge)
> - MT-Bench / Chatbot Arena paper: [https://arxiv.org/abs/2306.05685](https://arxiv.org/abs/2306.05685)
>
> If you use LLM-as-a-judge, I would at least:
>
> - use a clear rubric
> - swap answer order A/B vs B/A
> - avoid rewarding longer answers automatically
> - manually inspect a sample of judgments
> - keep judge prompts and judge model/version recorded
> **If the task is formatting or instruction following**
>
> If the fine-tune was meant to improve formatting, do not rely only on a general benchmark.
>
> For example, if the model must output JSON, measure:
>
> - valid JSON rate
> - schema-valid rate
> - required fields present
> - forbidden fields absent
> - correct field values
> - refusal behavior when input is invalid
> - robustness to longer or messy inputs
>
> If the task is instruction following, you can make small tests inspired by IFEval:
>
> - “Use exactly N bullet points”
> - “Mention keyword X at least twice”
> - “Do not use word Y”
> - “Return only JSON”
> - “Answer in one sentence”
> - “Include these fields and no others”
>
> IFEval itself is here:
>
> - [https://huggingface.co/datasets/google/IFEval](https://huggingface.co/datasets/google/IFEval)
>
> The general idea is simple:
>
> > If the desired behavior is objectively checkable, write checks for it.
> **If this is RAG or document QA**
>
> If your fine-tuned model is part of a RAG/document QA system, I would avoid giving the model one single score too early.
>
> Split the evaluation:
>
> | Component | What to check |
> | --- | --- |
> | Retrieval | Did the system retrieve the right documents/chunks? |
> | Context use | Did the answer use the provided context? |
> | Answer correctness | Is the final answer correct? |
> | Faithfulness | Is the answer supported by the retrieved context? |
> | Abstention | Does it say “not enough information” when the answer is not in context? |
> | Citation quality | Do citations actually support the claim? |
>
> This matters because a bad answer can come from different places:
>
> - bad retrieval
> - bad generation
> - bad prompting
> - bad chunking
> - bad source data
> - bad evaluation examples
>
> Those need different fixes.
> **If this is a coding fine-tune**
>
> For coding models, I would avoid evaluating only with general chat/reasoning benchmarks.
>
> Try to match the benchmark to the coding task:
>
> | Coding task | More relevant eval |
> | --- | --- |
> | Function-level generation | Unit tests, HumanEval-like tasks, BigCodeBench-like tasks |
> | Competitive programming | LiveCodeBench-like tasks |
> | Repo-level bug fixing | SWE-bench-like tasks |
> | Internal code assistant | Your own held-out tasks from your codebase, with tests |
>
> Also separate:
>
> - code completion
> - instruction-to-code
> - test repair
> - repo-level patch generation
> - agentic issue solving
>
> Those are different capabilities.
> **If you plan to deploy the model**
>
> Quality is not the only thing you might want to benchmark.
>
> If you plan to serve the model, also measure:
>
> - latency
> - throughput
> - VRAM
> - cost per request
> - context length
> - max output length
> - batch size
> - quantization impact
> - serving backend
>
> But keep this separate from quality evaluation.
>
> A faster model is not automatically a better model, and a more accurate model may still be too slow or expensive for the intended use.
* * *
## 7. Document the benchmark setup
If you publish the model or share results, make the benchmark reproducible enough for someone else to understand what happened.
Record:
- base model name and revision
- fine-tuned model name and revision
- whether it is LoRA/PEFT, QLoRA, or a merged/full fine-tune
- dataset name and split
- whether eval examples were excluded from training
- prompt template
- chat template
- decoding parameters
- metric
- benchmark tool and version
- hardware/backend, if reporting latency or throughput
- sample outputs or failure examples
- known limitations
Hugging Face model cards are a good place to document intended use, limitations, training details, and evaluation results:
- [https://huggingface.co/docs/hub/en/model-cards](https://huggingface.co/docs/hub/en/model-cards)
There is also Hub support for structured evaluation results, although I would still document the plain-English setup clearly:
- [https://huggingface.co/docs/hub/en/eval-results](https://huggingface.co/docs/hub/en/eval-results)
* * *
## 8. What information would help people recommend a concrete benchmark?
If you want more specific suggestions, I would add:
- What base model did you fine-tune?
- Is it LoRA/PEFT, QLoRA, or full fine-tuning?
- What task did you fine-tune for?
- What dataset did you train on?
- Do you have a held-out test set?
- Is the goal accuracy, instruction following, chat quality, formatting, coding, RAG, latency, or something else?
- Are you trying to publish a model card/leaderboard result, or just check whether the fine-tune helped?
With those details, people can suggest a much more specific eval setup.
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**Author:**  [@TensorVizion](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FTensorVizion)%5C
**Post date:** [June 25, 2026, 12:04pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhats-your-method-for-benchmarking%2F177127%2F3 "2026-06-25T12:04:22Z")
thank you so much for the detailed reply that gives me all the info i need. thaks again!
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**Author:**  [@ConnerHobbs](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FConnerHobbs)%5C
**Post date:** [October 6, 2026, 3:16pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhats-your-method-for-benchmarking%2F177127%2F4 "2026-10-06T15:16:42Z")
John’s checklist is solid. One thing I would add is running the eval more than once. Single pass scores move around more than people expect, especially on small sets. Three runs with different seeds tells you whether a two point gain is real or just noise. That has saved me from celebrating a regression more than once.