Transformers
oil-gas
quantum-computing
hybrid-ai
reservoir-engineering
well-optimization
retrieval-augmented-generation
fine-tuned
quantum-llm
upstrima
Eval Results (legacy)
Instructions to use GainEnergy/OGAI-Quantum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GainEnergy/OGAI-Quantum with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GainEnergy/OGAI-Quantum", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## 📦 **Model Variants**
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| **Model Name** | **Base Model** | **Quantum Features** | **Context Window** | **Use Case** |
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| **OGAI-Quantum** | OGAI-R1 + Quantum | Yes |
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| **OGAI-R1** | TinyR1-32B | No |
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| **OGMOE** | Mixtral-8x7B + MoE | No | 32K tokens | **Drilling Optimization & Decision Support** |
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## 📦 **Model Variants**
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| **Model Name** | **Base Model** | **Quantum Features** | **Context Window** | **Use Case** |
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|-------------------|----------------|----------------|----------------|-------------|
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| **OGAI-Quantum** | OGAI-R1 + Quantum | Yes | TDB tokens | **Hybrid AI for Energy & Engineering** |
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| **OGAI-R1** | TinyR1-32B | No | 128k tokens | **Reservoir AI & RAG** |
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| **OGMOE** | Mixtral-8x7B + MoE | No | 32K tokens | **Drilling Optimization & Decision Support** |
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