Need AI Engineer for Fine tune Mistral 7b LLM
Budget: $15 – $25 USD
The objective of this project is to fine-tune the Mistral 7B model to improve its capabilities in analyzing and generating insights from financial reports. The fine-tuned model will assist financial analysts by providing accurate summaries, identifying key financial metrics, and offering insights into financial data.
**Scope:**
1. **Data Collection:**
- Collect a diverse dataset of financial reports, including annual reports, earnings statements, and financial disclosures from various industries.
- Ensure the dataset covers a range of financial topics and includes different types of reports (e.g., quarterly earnings, balance sheets).
2. **Data Preprocessing:**
- Clean and preprocess the collected reports to standardize formatting and remove irrelevant content.
- Annotate the dataset with relevant financial terms, metrics, and summary information.
3. **Fine-Tuning:**
- Fine-tune the Mistral 7B model using the prepared dataset to tailor the model to the specific language and structure of financial reports.
- Apply techniques to prevent overfitting and ensure the model performs well across different financial contexts.
4. **Evaluation:**
- Develop evaluation metrics to assess the model’s performance in summarizing financial reports, identifying key metrics, and generating insights.
- Conduct thorough testing with a separate validation dataset and compare results against baseline models or expert annotations.
5. **Deployment:**
- Integrate the fine-tuned model into a web-based application or API that allows financial analysts to interact with the model.
- Implement interfaces for report upload, querying, and displaying results.
6. **Documentation and Support:**
- Provide comprehensive documentation detailing the model’s capabilities, usage instructions, and limitations.
- Offer ongoing support and maintenance to address any issues or updates post-deployment.
**Scope:**
1. **Data Collection:**
- Collect a diverse dataset of financial reports, including annual reports, earnings statements, and financial disclosures from various industries.
- Ensure the dataset covers a range of financial topics and includes different types of reports (e.g., quarterly earnings, balance sheets).
2. **Data Preprocessing:**
- Clean and preprocess the collected reports to standardize formatting and remove irrelevant content.
- Annotate the dataset with relevant financial terms, metrics, and summary information.
3. **Fine-Tuning:**
- Fine-tune the Mistral 7B model using the prepared dataset to tailor the model to the specific language and structure of financial reports.
- Apply techniques to prevent overfitting and ensure the model performs well across different financial contexts.
4. **Evaluation:**
- Develop evaluation metrics to assess the model’s performance in summarizing financial reports, identifying key metrics, and generating insights.
- Conduct thorough testing with a separate validation dataset and compare results against baseline models or expert annotations.
5. **Deployment:**
- Integrate the fine-tuned model into a web-based application or API that allows financial analysts to interact with the model.
- Implement interfaces for report upload, querying, and displaying results.
6. **Documentation and Support:**
- Provide comprehensive documentation detailing the model’s capabilities, usage instructions, and limitations.
- Offer ongoing support and maintenance to address any issues or updates post-deployment.
Related categories:
Python
Machine Learning (ML)
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Artificial Intelligence
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