Fine-Tune LLM for Data Insights
Budget: €2 – €4 EUR
I have an existing large-language-model checkpoint that I want expertly tailored for data-analysis work. The model needs to ingest three main sources—text documents, social-media feeds, and internal financial records—and reliably carry out:
• Sentiment analysis to flag opinions and tone across datasets
• Trend identification that highlights emerging patterns over time
• Predictive modeling that produces forward-looking metrics our team can act on
What I’m looking for:
1. A reproducible training pipeline (Python, PyTorch/Hugging Face preferred) that handles dataset cleaning, tokenization, and class balancing for the three data types above.
2. Fine-tuning on a GPU setup with thoughtful hyper-parameter selection, mixed-precision where sensible, and clear experiment tracking.
3. Quantitative evaluation reports (accuracy, F1, MAE or similar) for each task plus qualitative examples that illustrate model reasoning on real samples.
4. An inference script or lightweight API endpoint so my analysts can test the model locally and integrate it into our dashboards.
5. Concise deployment notes covering compute requirements, expected latency, and steps to re-train when new data arrives.
If you have prior work fine-tuning transformers for multi-source analytics, please mention the libraries and models you’ve used and share a brief sample of your evaluation output. I’m ready to start as soon as I find the right fit and will happily provide anonymized data extracts for initial experiments.
• Sentiment analysis to flag opinions and tone across datasets
• Trend identification that highlights emerging patterns over time
• Predictive modeling that produces forward-looking metrics our team can act on
What I’m looking for:
1. A reproducible training pipeline (Python, PyTorch/Hugging Face preferred) that handles dataset cleaning, tokenization, and class balancing for the three data types above.
2. Fine-tuning on a GPU setup with thoughtful hyper-parameter selection, mixed-precision where sensible, and clear experiment tracking.
3. Quantitative evaluation reports (accuracy, F1, MAE or similar) for each task plus qualitative examples that illustrate model reasoning on real samples.
4. An inference script or lightweight API endpoint so my analysts can test the model locally and integrate it into our dashboards.
5. Concise deployment notes covering compute requirements, expected latency, and steps to re-train when new data arrives.
If you have prior work fine-tuning transformers for multi-source analytics, please mention the libraries and models you’ve used and share a brief sample of your evaluation output. I’m ready to start as soon as I find the right fit and will happily provide anonymized data extracts for initial experiments.