Advanced NLP Model Research & Development
Budget: $8 – $15 AUD
My goal is to push our Natural Language Processing stack forward by continually scouting, testing, and refining state-of-the-art models in three core areas: text generation, sentiment analysis, and machine translation.
Scope of work
— Track current research and emerging repositories (Hugging Face, arXiv, GitHub) to spot promising architectures and training techniques.
- klaud8 / hrm ai / chat gpt / claude
— Spin up controlled experiments in Python using PyTorch/TensorFlow, comparing baseline performance with fine-tuned variants on representative datasets.
— Optimise inference speed, memory footprint, and prompt-engineering workflows so models transition smoothly from notebook to production API.
— Document findings in concise experiment reports and integrate successful models into our existing CI/CD pipeline.
Deliverables
1. A living benchmark report that ranks at least five candidate models per task on accuracy, latency, and cost.
2. Clean, reproducible training scripts plus environment files (Docker/Conda) for every retained model.
3. Modular inference endpoints or notebooks demonstrating:
• coherent long-form text generation,
• reliable sentiment scoring on unseen data,
• fluent bidirectional translation for at least two language pairs.
4. A short roadmap outlining next-step research opportunities and expected gains.
Acceptance criteria: code runs on a standard A100 or equivalent GPU instance without modification; reported metrics replicate within ±1 % on rerun; documentation is sufficient for another engineer to extend the work.
Scope of work
— Track current research and emerging repositories (Hugging Face, arXiv, GitHub) to spot promising architectures and training techniques.
- klaud8 / hrm ai / chat gpt / claude
— Spin up controlled experiments in Python using PyTorch/TensorFlow, comparing baseline performance with fine-tuned variants on representative datasets.
— Optimise inference speed, memory footprint, and prompt-engineering workflows so models transition smoothly from notebook to production API.
— Document findings in concise experiment reports and integrate successful models into our existing CI/CD pipeline.
Deliverables
1. A living benchmark report that ranks at least five candidate models per task on accuracy, latency, and cost.
2. Clean, reproducible training scripts plus environment files (Docker/Conda) for every retained model.
3. Modular inference endpoints or notebooks demonstrating:
• coherent long-form text generation,
• reliable sentiment scoring on unseen data,
• fluent bidirectional translation for at least two language pairs.
4. A short roadmap outlining next-step research opportunities and expected gains.
Acceptance criteria: code runs on a standard A100 or equivalent GPU instance without modification; reported metrics replicate within ±1 % on rerun; documentation is sufficient for another engineer to extend the work.