Shinzmann/naija-petro-8b
Shinzmann/naija-petro-8b is an 8 billion parameter Qwen3-based instruction-tuned causal language model developed by the Naija-Petro project. Fine-tuned using QLoRA on approximately 20,000 synthetic petroleum-engineering instruction-response pairs, this model is optimized for technical question answering and explanation across various petroleum subdomains. It serves as a lightweight, fast-inference variant designed for study aids, engineering decision support, and as a backbone for retrieval-augmented systems in the petroleum industry.
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Naija-Petro 8B: Specialized Petroleum Engineering LLM
Naija-Petro 8B is an 8 billion parameter, decoder-only causal language model, instruction-tuned from the Qwen3-8B architecture. Developed by the Naija-Petro project, this model is specifically fine-tuned using QLoRA and Unsloth on a dataset of approximately 20,000 synthetic petroleum-engineering instruction-response pairs.
Key Capabilities & Features
- Domain Expertise: Excels in technical question answering and explanations across core petroleum engineering subdomains, including drilling, reservoir, production, completions, EOR, well testing, and petroleum geoscience.
- Instruction-Tuned: Designed to provide precise, technically accurate answers, often including equations, units, and practical considerations, making it suitable as a study aid or engineering decision-support tool.
- Lightweight & Fast: Positioned as a lightweight, fast-inference variant within the Naija-Petro family, ideal for integration into retrieval-augmented generation (RAG) systems.
- Training Data: Fine-tuned on synthetic data generated from a comprehensive scraped corpus including arXiv, Semantic Scholar, OpenAlex, Crossref, DOE/OSTI, PetroWiki, SLB glossary, and EIA.
Use Cases & Limitations
Good for:
- Direct technical question answering and explanation in petroleum engineering.
- Serving as a backbone for retrieval-augmented assistants, especially when paired with the Naija-Petro RAG system for Nigeria-specific facts.
- Further domain-specific fine-tuning or model distillation.
Important Considerations:
- The model's knowledge is primarily based on general/global petroleum knowledge. For Nigeria-specific regulations or economics, it should be used with a RAG system.
- Trained on synthetic data, it may occasionally "hallucinate" or be confidently wrong, particularly on numerical specifics. Outputs should always be validated by qualified engineers and primary sources.
- It is not intended for autonomous operational, safety-critical, or financial decisions, nor as a substitute for licensed engineering judgment.