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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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  ### Model Description
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+ CodeGen-Mono 350M Fine-Tuned for Phaser.js
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+ This model is a fine-tuned version of the CodeGen-Mono 350M, specifically optimized for generating Phaser.js code based on user prompts.
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+ Phaser.js is a popular 2D game framework used for creating web-based games, and this model aims to assist developers by generating relevant
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+ game code snippets, enhancing productivity and creativity.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ - **Developed by:** Toontech Ltd.
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+ - **Funded by:** Toontech Ltd.
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+ - **Language(s) (NLP):** English
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+ - **Finetuned from model:** CodeGen (CodeGen-Mono 350M)
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+ **Model Overview**
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+ Base Model: CodeGen-Mono 350M
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+ Training Data: Fine-tuned on a custom dataset of Phaser.js code examples, covering various aspects like User Input Handling,
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+ Audio Management, Text Display, Tilemap Handling, Scene Management, Camera Controls, and Particles and Effects.
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+ **Purpose:** To generate Phaser.js code snippets based on natural language prompts, helping developers to quickly prototype and implement game
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+ features.
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+ **Key Features**
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+ User-Friendly: Accepts plain English prompts and translates them into functional Phaser.js code.
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+ Versatile: Capable of handling a variety of game development tasks, from simple input handling to complex scene management.
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+ Optimized for Game Development: Specifically fine-tuned on a wide range of Phaser.js use cases to provide accurate and efficient code
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+ generation.
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+ **Use Cases**
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+ Game Development Prototyping: Quickly generate code snippets to prototype game mechanics and features.
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+ Learning Tool: A valuable resource for beginners learning Phaser.js, providing examples and code structure.
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+ Coding Assistance: Acts as an intelligent code assistant for experienced developers, helping to reduce development time and improve
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+ workflow.
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+ **How to Use**
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+ You can use this model with the Hugging Face transformers library or integrate it into your custom applications using the provided API.
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+ Here’s a basic example:
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+ from transformers import pipeline
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+ codegen_pipeline = pipeline("text2text-generation", model="your-huggingface-username/CodeGen-Mono-350M-Phaser")
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+ prompt = "Create a Phaser.js code snippet to move a sprite with arrow keys."
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+ response = codegen_pipeline(prompt)
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+ print(response[0]['generated_text'])
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+ **Model Limitations**
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+ Phaser.js Specific: The model is specialized for Phaser.js and may not generalize well to other frameworks or coding languages.
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+ Code Quality: Generated code might require additional refinement and testing for edge cases.
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+ **Future Work**
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+ Planned improvements include expanding the training dataset, incorporating more advanced game development scenarios, and enhancing model
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+ accuracy and usability.
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+ **Feedback and Contributions**
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+ Your feedback is invaluable! If you encounter any issues or have suggestions, please feel free to open an issue or contribute directly to
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+ the repository.