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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - microsoft/orca-agentinstruct-1M-v1
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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+ base_model:
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+ - Qwen/QwQ-32B-Preview
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+ new_version: Qwen/QwQ-32B-Preview
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+ pipeline_tag: text2text-generation
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+ library_name: adapter-transformers
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+ tags:
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+ - exercise
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+ - fitness
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+ - gym
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+ - workout
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+ ---
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. --> personalize workout plans
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+
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+ This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->This model will be given data on exercises, what kind should be used to improve strength, endurance, etc.It will be able to create personalized workout plans based on what the user wants,wether an increase in muscle size, stregnth etc.This will be specified with questions asked by the AI
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+ .
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+
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+
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+
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+ - **Developed by:** Brock Herczeg
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+ - **Funded by [optional]:** Brock Herczeg
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+ - **Shared by [optional]:** Brock Herczeg
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+ - **Model type:** 1A
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+ - **Language(s) (NLP):** English
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. --> https://1drv.ms/x/c/8e3dcbe577acf4d4/EdyUE9jl8spHmDzCL9i3b1wBVyVdmo2J2svnn4lm1K8AVg?e=ZbqhR5
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+
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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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+
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+ ## Uses
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+
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+ <!-- This AI model is intended to be used as a virtual fitness assistant that generates personalized workout plans.
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+ The primary users are individuals seeking structured and effective exercise programs, including beginners, intermediate, and advanced fitness enthusiasts.
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+ Personal trainers may also use the tool to supplement their work with clients.
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+ The model positively impacts users by promoting consistency, efficiency, and accessibility in fitness planning.
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+ It is not a substitute for professional medical advice or in-person coaching, and it is not intended for use in diagnosing or treating medical conditions.-->
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+
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+ ### Direct Use
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+
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+ <!-- This model, in its base form, is a conversational AI designed to respond to text inputs with relevant and coherent text-based outputs.
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+ Without fine-tuning or integration into a larger ecosystem, it can engage users by answering fitness-related questions, suggesting general exercise routines, and providing motivational advice.
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+ Foreseeable users include fitness enthusiasts seeking general guidance, hobbyist developers exploring fitness applications, and educators teaching natural language processing.
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+ However, the model is not capable of delivering highly personalized fitness programs or addressing complex user requirements without additional customization.
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+ It is not intended for medical advice or professional training guidance-->
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+
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+ ### Downstream Use
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+
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+ <!-- When fine-tuned for fitness tasks and integrated into a larger app ecosystem, the model serves as a dynamic workout recommendation engine. It generates tailored exercise plans by analyzing user profiles, including fitness goals, physical limitations, and available equipment. The model adapts plans over time based on progress data, such as wearable device metrics (e.g., steps, calories burned, heart rate) or user feedback.
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+
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+ In this ecosystem, the model powers a conversational interface where users can interact with a virtual trainer for guidance, motivation, and routine adjustments. The integration enhances user engagement through real-time updates, personalized notifications, and progress tracking.
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+
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+ The primary audience includes individuals seeking structured fitness plans, athletes aiming for peak performance, and professionals using the app to support clients. Ethical considerations include safeguarding user data, ensuring recommendations are accurate, and providing disclaimers for users with health conditions to consult professionals when necessary.-->
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+
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+
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+ ### Out-of-Scope Use
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+
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+ <!--This model is not intended for use in scenarios requiring medical diagnoses, physical therapy, or specialized rehabilitation programs for individuals with chronic illnesses, injuries, or disabilities. It is also out of scope for creating precision exercise programs for elite athletes without further fine-tuning and expert input.
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+
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+ Malicious use, such as propagating unsafe fitness practices, promoting fraudulent health claims, or exploiting user vulnerabilities, is strictly prohibited. The model must not be used to create deceptive fitness advice or harm individuals through excessive or inappropriate exercise recommendations.
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+
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+ Technical limitations include the inability to provide biomechanical analyses, culturally localized advice without further customization, or accurate guidance for users without fitness equipment if exercises require it. It is also not suited for unsupervised minors or individuals medically advised against physical activity.
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+ The model should be used responsibly and in compliance with applicable laws and ethical standards, ensuring that user safety and privacy are prioritized at all times.-->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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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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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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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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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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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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+
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+ #### Factors
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+
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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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+
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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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+
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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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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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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:**
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+ [More Information Needed]
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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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+ [More Information Needed]
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+ ## Model Card Authors [optional]
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+ [More Information Needed]
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+ ## Model Card Contact
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+ [More Information Needed]