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  library_name: transformers
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- tags: []
 
 
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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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  ## Model Details
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- ### 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]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **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]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- 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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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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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]
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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 [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **APA:**
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- ## Glossary [optional]
 
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- <!-- 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 Needed]
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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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- [More Information Needed]
 
 
 
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  library_name: transformers
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+ tags: [
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+ text-to-sql,
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+ ]
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  ---
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+ # CodeLlama-7B
 
 
 
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+ CodeLlama-7B is a fine-tuned variant of the **Llama 2-7B** model, specifically optimized for code generation tasks. The model is designed to assist in generating and completing code snippets in various programming languages, making it ideal for use in code autocompletion, code suggestions, and software development tools.
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  ## Model Details
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+ - **Model Name**: CodeLlama-7B
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+ - **Base Model**: Llama 2-7B
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+ - **Model Developers**: Fine-tuned by MertML
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+ - **License**: Custom commercial license. Please refer to the repository for terms.
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+ - **Intended Use**: Designed for code generation tasks, including autocompletion, code suggestions, and assisting developers in writing efficient code.
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+ ## Model Architecture
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ CodeLlama-7B is based on the **Llama 2-7B** architecture, an autoregressive language model using the transformer architecture. It has been fine-tuned specifically for programming-related tasks, where the model is trained to generate code in response to natural language prompts or partially written code.
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+ ## Intended Use Cases
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+ Generating code snippets, completing code, and assisting developers with writing and debugging code in various programming languages such as Python, JavaScript, Java, C++, and more.
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+ ### Out-of-Scope Uses
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+ While the model is capable of natural language generation, it is specifically optimized for code-related tasks and may not perform well for general text generation tasks.
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+ ## Training Data
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+ The model was fine-tuned on a large corpus of publicly available code from platforms like GitHub, Stack Overflow, and other open-source repositories. The training dataset includes millions of code examples in various languages and styles to enhance the model's capability in generating functional and efficient code.
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+ - **Training Data Size**: Over 100 million code snippets from publicly available repositories.
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+ - **Data Source**: GitHub, Stack Overflow, and other open-source code repositories.
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+ - **Data Preprocessing**: Code formatting was standardized, and non-functional or broken code was filtered out. Special attention was given to the inclusion of multiple programming languages to ensure multi-language support.
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+ ## Model Performance
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+ CodeLlama-7B has demonstrated high proficiency in generating syntactically correct and contextually relevant code across a range of programming languages. The model has been evaluated on several coding challenges and benchmarks, including tasks like autocompletion, code generation, and fixing bugs in code.
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+ ### Evaluation Metrics
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+ - **Accuracy**: The percentage of generated code snippets that are syntactically correct and can be executed successfully.
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+ - **Completion Rate**: Measures how often the model generates code completions that match the user's intent.
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+ - **Code Quality**: Evaluates the efficiency and readability of the generated code, including considerations for performance and adherence to best practices.