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| """Benchmark NVIDIA models on the real pipeline (score + keyword + tailor). | |
| For each model it times the three LLM-heavy calls and reports whether structured | |
| output came back populated, so we can lock the fastest model that is still correct. | |
| Run: python -m scripts.bench_models | |
| Override the list: BENCH_MODELS="modelA,modelB" python -m scripts.bench_models | |
| """ | |
| import os | |
| import time | |
| # Models to compare (fastest -> strongest). Override via BENCH_MODELS. | |
| DEFAULT_MODELS = [ | |
| "nvidia/llama-3.3-nemotron-super-49b-v1", | |
| "nvidia/nemotron-3-super-120b-a12b", | |
| ] | |
| TARGET = 85 | |
| RESUME = """Name: Manthan Rampal | |
| Current position: Senior Software Engineer | |
| Location: Pune, India | |
| Email: manthan@example.com | |
| Summary: Backend engineer with 8 years building enterprise web applications in C# and .NET. | |
| Skills: C#, .NET, ASP.NET MVC, Web API, SQL Server, Entity Framework, JavaScript, jQuery, Azure App Service, Git, Agile | |
| Experience: | |
| - Senior Software Engineer @ TechCorp (2019 - Present): Built and maintained ASP.NET Web API services for a logistics platform. Wrote SQL Server stored procedures and optimized queries. Integrated Azure App Service deployments. Mentored two junior developers. Worked in Agile sprints. | |
| - Software Engineer @ DataSoft (2016 - 2019): Developed ASP.NET MVC web applications. Built REST APIs consumed by Angular front ends. Used Entity Framework for data access. Fixed production defects and improved performance.""" | |
| JOB = """Company: CloudScale | |
| Position: Senior .NET Engineer (Cloud / Microservices) | |
| Years of experience required: 7 | |
| Job description: | |
| Senior .NET Engineer to design and build cloud-native microservices. | |
| Required: strong C# and .NET Core, RESTful Web APIs, microservices architecture, | |
| Azure (App Service, Azure Functions, Azure Service Bus), Docker and Kubernetes, | |
| CI/CD pipelines, SQL Server and Entity Framework Core, unit testing, message-based / event-driven systems. | |
| Nice to have: RabbitMQ or Kafka, Redis caching, gRPC, Domain-Driven Design, observability.""" | |
| def _time(label, fn): | |
| t0 = time.perf_counter() | |
| out = fn() | |
| dt = time.perf_counter() - t0 | |
| print(f" {label:<10} {dt:6.1f}s") | |
| return out, dt | |
| def bench(model: str) -> None: | |
| # fast_model()/tailor_model() read these at call time, so set both to `model`. | |
| os.environ["NVIDIA_MODEL_FAST"] = model | |
| os.environ["NVIDIA_MODEL_TAILOR"] = model | |
| from phase3_score.score_match import score_resume_against_job | |
| from phase4_tailor.keyword_coverage import ( | |
| analyze_keyword_coverage, | |
| backfill_uncovered_keywords, | |
| measure_resume_coverage, | |
| ) | |
| from phase4_tailor.tailor_resume import tailor_resume_for_job, tailored_to_text | |
| print(f"\n=== {model} ===") | |
| total = 0.0 | |
| base, dt = _time("score", lambda: score_resume_against_job(RESUME, JOB)) | |
| total += dt | |
| report, dt = _time( | |
| "keyword", lambda: analyze_keyword_coverage(RESUME, JOB, target_score=TARGET) | |
| ) | |
| total += dt | |
| tailored, dt = _time( | |
| "tailor", | |
| lambda: tailor_resume_for_job( | |
| RESUME, JOB, keyword_report=report, original_score=base.match_score, | |
| target_score=TARGET, | |
| ), | |
| ) | |
| total += dt | |
| tailored = backfill_uncovered_keywords(tailored, report) | |
| measured = measure_resume_coverage(tailored_to_text(tailored), report) | |
| ok = bool( | |
| (tailored.tailored_summary or "").strip() | |
| and tailored.tailored_skills | |
| and tailored.tailored_experience | |
| ) | |
| print( | |
| f" -> total {total:5.1f}s | baseline_match={base.match_score} " | |
| f"ATS_coverage={measured['score']} | structured_ok={ok} " | |
| f"(direct={len(report.direct_matches)} adjacent={len(report.adjacent_matches)} " | |
| f"missing={len(report.missing_keywords)})" | |
| ) | |
| if __name__ == "__main__": | |
| models = [m.strip() for m in os.getenv("BENCH_MODELS", "").split(",") if m.strip()] | |
| for m in models or DEFAULT_MODELS: | |
| try: | |
| bench(m) | |
| except Exception as exc: # keep going so one bad model doesn't abort the run | |
| print(f" !! {m} failed: {exc}") | |