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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Choose the checkpoint that performs best on your coding task under the constraints you will actually deploy—not the model with the biggest name or the highest score on an unrelated leaderboard. Define the task, compare a prompt-only baseline with fine-tuned candidates on held-out examples, and check license, training access, context limits, compute, and serving cost before committing.
Define the coding task before choosing a model
“Coding” covers different input and output patterns. A model that generates a short Python function from an instruction may not be the right choice for inline completion, code repair, explanation, or changes spanning a repository. Decide what the model must do and what information it will receive at inference time.
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- Completion or fill-in-the-middle: Test the actual prefix/suffix or editor context format used by your product. Instruction-to-code benchmarks do not establish autocomplete quality.
- Instruction-to-code generation: Evaluate whether the model produces code that follows the requested language, APIs, constraints, and output format.
- Repair: Provide representative broken code, error messages, and any context available in production; score whether the result fixes the defect without introducing new failures.
- Explanation: Assess correctness and usefulness of explanations separately from code generation.
- Repository-level work: Use tasks requiring the same repository context and tools the deployed system will have. Small function-generation datasets cannot establish repository-level competence.
Fine-tuning is most useful when you can provide representative examples of the behavior you want and verify the results. If the system needs changing private or current facts, supply those as context; fine-tuning is not a reliable substitute for updating information at inference time.
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Choose a few viable checkpoints, not a long list selected by reputation. Record the exact repository or model ID and revision so your results can be reproduced. For each candidate, note its training status, language and domain fit, context capacity, license, supported fine-tuning methods, and the infrastructure needed to train and serve it.
#1 Best Overall
| Decision factor | What to establish for each candidate | Why it matters |
|---|---|---|
| Task and data format | Pretrained or instruction-tuned; expected input/output format; programming languages and domain fit | A checkpoint that matches your examples and production format is a more relevant candidate than one selected by a broad coding label. |
| Held-out task performance | Pass rate, functional correctness, instruction adherence, and other task-specific outcomes under a fixed evaluation protocol | A general benchmark score alone does not predict results on your workload. |
| License and rights | Exact license and use constraints for the checkpoint revision | Rights can vary by model; do not infer them from a family name. |
| Training access and limits | Whether your platform supports that exact model and training method; context limits and truncation behavior | A model is not a practical choice if you cannot train it or fit examples into its usable context. |
| Infrastructure and operation | Memory, throughput, latency, training and serving cost, and maintenance requirements on your target setup | Training feasibility and production feasibility are separate decisions. |
For a concrete licensing example, the Qwen2.5-Coder-32B-Instruct repository lists Apache-2.0. Verify the exact revision and current terms of any model you consider rather than generalizing from that example to other Qwen models or to a model family.
Compare base and instruction-tuned checkpoints in the right format
A pretrained checkpoint is a plausible starting point when the target behavior is continuation or code completion. An instruction-tuned checkpoint may be a better fit when examples are conversational requests followed by code or explanations. Neither type is universally superior: the relevant question is which one performs better with your data format and task.
| Checkpoint type | Often worth testing when | Evaluation detail |
|---|---|---|
| Pretrained | The target is continuation, inline completion, or another code-native format | Use the same prompt boundaries, surrounding code, and completion format as production. |
| Instruction-tuned | The target is instruction-response behavior and examples are written as requests and responses | Include the expected instruction framing and output constraints in both training and evaluation. |
An ICLR 2025 code-generation study selected instruction-tuned models for higher zero-shot compatibility and more accurate evaluation in its experiment. That explains the study’s choice; it does not prove instruction-tuned checkpoints are always the better fine-tuning base.
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Set up a fair evaluation before fine-tuning
First create a prompt-only baseline for each shortlisted candidate. Then fine-tune and test against a held-out set that resembles the task data in diversity without reusing examples from training. OpenAI’s supervised fine-tuning guidance recommends establishing evals before fine-tuning and describes 50–100 examples as a practical starting range, including a suggestion to begin with 50 well-crafted demonstrations. Treat that as provider guidance, not a guaranteed sample size or a rule for code tasks; the amount needed depends on the use case.
- Hold out representative examples. Include the languages, task types, difficulty, and relevant edge cases expected in use. Keep a separate training set so evaluation does not reward memorization.
- Run the same protocol on every candidate. Fix prompts, available context, tools, decoding settings, test harness, and scoring rules. Record these choices with the results.
- Measure outcomes that match the task. For executable code, use compilation and tests where appropriate; also track instruction adherence, latency, and cost if they matter to deployment.
- Compare the tuned model with its own baseline. A fine-tuned model should be judged against the same checkpoint without fine-tuning as well as against other candidates.
- Inspect failures, not only averages. Categorize failures by language, task, or constraint so an apparent gain does not conceal regressions important to users.
HumanEval and MBPP are established small Python code-generation benchmarks, not general tests of coding ability. The ICLR 2025 paper describes evaluation sets of 164 HumanEval problems and 378 MBPP problems. EvalPlus describes HumanEval+ as expanding HumanEval’s test coverage with 80 times more test cases. Those figures describe the cited benchmarks and suite; they do not mean any benchmark covers every production failure mode. Scores can change with the test suite, decoding, harness, and task definition, so report the full protocol and prefer execution-based checks relevant to your application.
Verify training access, license, and context limits
Availability is platform- and checkpoint-specific. AWS’s JumpStart guide lists multiple Code Llama variants, but that does not establish that every variant, training method, or deployment is available in every region or configuration. OpenAI’s model-optimization documentation, accessed in 2026, says the company is winding down its fine-tuning platform: new users can no longer access it, while existing users may create jobs for the coming months. Because provider availability changes, verify current access for the exact model and account before designing a workflow around it.
Rank #4
Check model-specific context limits rather than assuming all members of a family share one. OpenAI’s fine-tuning best practices list different limits by model ID and warn that oversized training examples are truncated at the end. Confirm how your chosen training system handles long examples, and test that the retained portion still contains the information needed to learn the target behavior.
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- Confirm the exact checkpoint revision, license, and applicable use constraints.
- Check that the intended platform supports training—not just inference—for that model.
- Verify context limits, truncation behavior, and supported fine-tuning methods.
- Confirm that deployment rights and serving options fit your intended use.
Estimate compute and deployment cost from your recipe
Model size alone is not enough to size training. Feasibility depends on context length, precision, batch size, optimizer, and whether you are doing full fine-tuning or a parameter-efficient method, along with the available hardware and software stack. Estimate using the exact recipe you intend to run and include serving costs in the decision.
Best Value
The ICLR 2025 study reported using four NVIDIA A100 GPUs for its experiment. That is a description of its setup, not a minimum hardware recommendation for fine-tuning code models. Your requirements may differ substantially with model, training method, and configuration.
Make the selection from a decision record
For each finalist, keep a short record containing the exact checkpoint and revision, task and data format, license, platform support, context limits, evaluation results, and estimated training and serving requirements. Prefer the candidate that meets operational and rights requirements and performs best on representative held-out tasks at an acceptable cost. If no candidate clearly improves on a strong prompt-only baseline, fine-tuning may not be the right next step.
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