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README.md

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 library_name: transformers
-tags: []
+tags: [text-to-sql]
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-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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+# TinyLlama-7B (Fine-tuned for Text-to-SQL)
 
+TinyLlama-7B is a fine-tuned version of the Llama-2 7B model, specifically trained to handle Text-to-SQL tasks. It is designed to efficiently translate natural language queries into structured SQL queries, making it ideal for use in applications requiring database interactions from natural language instructions. With a smaller model size compared to larger variants, TinyLlama-7B offers a balance between performance and efficiency for environments with limited computational resources.
 
 ## Model Details
 
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **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]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## 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. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- 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
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below 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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-<!-- 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
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-<!-- 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]
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-#### 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. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- 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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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### 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]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
+- **Model Name**: TinyLlama-7B (Fine-tuned for Text-to-SQL)
+- **Base Model**: Llama-2 7B
+- **Model Type**: Fine-tuned Transformer-based language model
+- **Parameter Size**: 7 billion parameters
+- **Fine-tuning Tasks**: Text-to-SQL, Natural Language to Structured Query Translation
+- **License**: Custom commercial license (please refer to original Llama-2 model license)
 
-[More Information Needed]
+## Intended Use
 
-## Glossary [optional]
+### Use Cases:
+- **Text-to-SQL**: Translating natural language questions into executable SQL queries for database retrieval tasks.
+- **Database Management**: Assisting with creating, modifying, and querying databases using natural language.
+- **General NLP**: Handling basic language tasks like question answering, summarization, and text classification when combined with SQL-related tasks.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Out-of-scope Uses:
+- **Harmful Content Generation**: Generating biased or harmful content, or violating local laws.
+- **Languages Other Than English**: Primary focus is on English for database queries and SQL generation.
+- **Non-SQL Tasks**: Not intended for use in tasks outside of text-to-SQL or related language generation.
 
-[More Information Needed]
+## Model Performance
 
-## More Information [optional]
+TinyLlama-7B has been fine-tuned specifically for the task of converting natural language queries into SQL queries. The fine-tuning data included a diverse set of SQL query generation tasks, making the model capable of handling complex queries, joins, aggregations, and filtering operations.
 
-[More Information Needed]
+**Evaluation on Text-to-SQL Tasks**:
+- **High accuracy in translating natural language into SQL**: TinyLlama-7B performs well in generating accurate SQL queries from user inputs, even for complex requests involving multiple tables and conditions.
+- **Efficient and Fast**: The smaller size ensures lower latency and faster inference time compared to larger models, making it more suitable for environments with limited resources.
 
-## Model Card Authors [optional]
+## Training Data
 
-[More Information Needed]
+TinyLlama-7B was fine-tuned using a variety of publicly available datasets focused on SQL generation, including text-to-SQL datasets and query-generation tasks. The model was trained on data that includes diverse table schemas, SQL queries, and natural language questions to improve its performance on Text-to-SQL tasks.
 
-## Model Card Contact
 
-[More Information Needed]