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---
library_name: transformers
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-1.5B
---
# Model Card for Model ID
A 1.5B parameter model fine-tuned from Qwen2.5-Coder-1.5B for Text-to-SQL generation, enabling natural language to SQL query translation with improved accuracy and schema understanding.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is a 1.5B parameter transformer model fine-tuned for Text-to-SQL generation tasks, built upon Qwen/Qwen2.5-Coder-1.5B. It is designed to translate natural language questions into accurate SQL queries across various database schemas. The fine-tuning process enhances its ability to handle complex SQL structures, improve schema grounding, and generate executable queries for downstream applications such as data querying, analytics automation, and natural language interfaces to databases.
- **Developed by:** https://www.linkedin.com/in/kuldeep-rathoree/
- **Model type:** Causal language model
- **Language(s) (NLP):** English (en)
- **License:** Apache 2.0
- **Finetuned from model:** Qwen/Qwen2.5-Coder-1.5B
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** ikuldeep1/Qwen2.5-Coder-1.5B-FullBase677-SQL-PEFT
- **Paper:** N/A
- **Demo:** Coming soon (Colab demo notebook in progress)
## 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. -->
This model is intended for converting natural language questions into SQL queries. It can be used to power natural language interfaces for databases, automate data querying, and support research on Text-to-SQL generation.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
The model can be directly used to generate SQL queries from English text prompts using the 🤗 Transformers pipeline or via the Hugging Face Inference API.
[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 -->
It can be integrated into applications such as:
Data analytics dashboards with natural language interfaces
Chatbots that answer database-related questions
Tools for automating report generation or data retrieval
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
The model is not suitable for:
Executing SQL queries directly on databases without validation
Handling non-English inputs (it’s trained primarily on English)
Use in production systems without proper testing or query sanitization
[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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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