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--- |
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license: mit |
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datasets: |
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- cxllin/economics |
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- meta-math/MetaMathQA |
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language: |
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- en |
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metrics: |
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- accuracy |
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tags: |
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- finance |
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- math |
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- economics |
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--- |
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# Llama2-7b-economist |
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Llama2-7b-economist is a state-of-the-art language model with 7 billion parameters, specifically fine-tuned on extensive Macro and Micro Economic theory. It aims to provide data-driven economic insights and predictions. |
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## Model Details |
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### Model Description |
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Llama2-7b-economist represents the intersection of cutting-edge AI modeling and economic theory. By leveraging a vast parameter space and meticulous fine-tuning, this model seeks to transform the way we approach and understand economic data. |
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- **Developed by:** [Collin Heenan](mailto:[email protected]) |
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- **Model type:** Transformer-based Language Model |
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- **Language(s):** English |
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- **License:** MIT |
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- **Finetuned from model:** Llama2-7b Base Model |
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### Model Sources |
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- **Repository:** [More Information Needed] |
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- **Demo:** [More Information Needed] |
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## Uses |
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### Direct Use |
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- Economic predictions based on text inputs. |
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- Answering questions related to Macro and Micro Economic theories. |
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- Analyzing economic texts and extracting insights. |
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### Downstream Use |
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- Potential to be fine-tuned for specific economic tasks, such as economic sentiment analysis or financial forecasting. |
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### Out-of-Scope Use |
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- Non-economic related tasks. |
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- Predictions that require non-textual data, like graphs or charts. |
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## Bias, Risks, and Limitations |
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[More Information Needed] |
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### Recommendations |
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Users should ensure they are using Llama2-7b-economist in appropriate economic contexts and be cautious of extrapolating predictions without expert validation. |
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## How to Get Started with the Model |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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- Comprehensive Macro and Micro Economic theory datasets. |
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### Training Procedure |
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#### Training Hyperparameters |
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- **Training regime:** Training on 1x t4 GPU |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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## Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
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- **Hardware Type:** NVIDIA T4 GPU |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications |
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### Model Architecture and Objective |
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Transformer-based architecture with 7 billion parameters, designed to understand and predict economic patterns and insights. |
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### Compute Infrastructure |
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#### Hardware |
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- 1x t4 GPU |
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## Contact |
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- [Collin Heenan](mailto:[email protected]) |
