Update README.md

README.md

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 ---
-library_name: transformers
+language: en
 tags:
-- unsloth
+- data-analytics
+- dashboard
+- AI
+- visualization
+license: mit
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# AutoDashAnalyticsV1GGUF
 
+AutoDashAnalyticsV1GGUF is a powerful tool designed to automate the creation of dashboards from various databases using advanced AI techniques. This model connects to SQL databases and provides interactive data visualizations through user prompts.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
-
-#### Software
+- **Model Name:** AutoDashAnalyticsV1GGUF
+- **Version:** 1.0
+- **Language:** English
+- **License:** MIT
+- **Tags:** data-analytics, dashboard, AI, visualization
 
-[More Information Needed]
+## Model Description
 
-## Citation [optional]
+AutoDashAnalyticsV1GGUF is developed to simplify and enhance the data analysis process for companies. It leverages a fine-tuned large language model trained on extensive datasets specifically for data analysis. The tool enables users to create detailed and interactive dashboards with minimal effort.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Features
 
-**BibTeX:**
+- **Automated Dashboard Creation:** Automatically generates dashboards from SQL databases.
+- **Interactive Visualizations:** Allows users to interact with the data through prompts.
+- **Advanced AI Capabilities:** Utilizes a fine-tuned LLM for comprehensive data analysis.
+- **Customization:** Provides options for customizing the visualizations and data representation.
 
-[More Information Needed]
+## Training Data
 
-**APA:**
+The model is trained on a diverse dataset comprising various SQL databases and data visualization examples. This ensures robust performance across different data types and structures.
 
-[More Information Needed]
+## Performance
 
-## Glossary [optional]
+AutoDashAnalyticsV1GGUF has been tested extensively to ensure high accuracy and reliability in generating dashboards. The model can handle large datasets and provide insightful visualizations efficiently.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Limitations
 
-[More Information Needed]
+- The model is currently optimized for Relational databases.
+- Future versions will include support for other database types.
 
-## More Information [optional]
+## License
 
-[More Information Needed]
+This project is licensed under the MIT License.
 
-## Model Card Authors [optional]
+## Acknowledgements
 
-[More Information Needed]
+We acknowledge the contributions of the open-source community and the developers who have supported this project.
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+If you use this model in your research, please cite it as follows:
+@misc{AutoDashAnalyticsV1GGUF,
+title = {AutoDashAnalyticsV1GGUF},
+year = {2024},
+publisher = {Hugging Face},
+journal = {Hugging Face repository},
+howpublished = {\url{https://huggingface.co/techcodebhavesh/AutoDashAnalyticsV1GGUF}}
+}