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- ---
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- library_name: transformers
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- tags: []
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- ---
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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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- [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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- #### 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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- [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:**
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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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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+ # Welcome to Llama2, your AI-powered gym instructor designed to revolutionize your fitness journey
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+ With Llama2, embark on a personalized workout experience designed to meet your unique needs and goals. Say goodbye to generic fitness routines and hello to targeted guidance that evolves with you over time. Whether you're a beginner or a seasoned athlete, Llama2 adapts to your pace, ensuring optimal progress and results. Get ready to redefine your fitness routine with Llama2 by your side, available whenever and wherever you need it.
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+ <b>note = this training is given on colab. for this reason the training is not very long and the parameters are given very little.</b>
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+ ![](https://github.com/OnurSakarr/LLM/blob/master/gym_llm/Image/image%20%2875%29.png)
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+ ## Model
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+ meta-llama/Llama-2-7b <br>
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+ per_device_train_batch_size = 1 <br>
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+ gradient_accumulation_steps = 1 <br>
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+ optim = "paged_adamw_32bit" <br>
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+ save_steps = 500 <br>
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+ logging_steps = 10 <br>
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+ learning_rate = 2e-4 <br>
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+ max_grad_norm = 0.3 <br>
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+ max_steps = 1500 <br>
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+ warmup_ratio = 0.03 <br>
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+ lora_alpha = 16 <br>
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+ lora_dropout = 0.1 <br>
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+ lora_r = 8 <br>
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+
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+ ## Data
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+ token size = 214464 <br>
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+ row = 1660 <br>
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+ word count = 130755 <br>
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+ prompt= \<s\>\[INST\] \<\<SYS\>\> Below is an instruction that describes a task. Write a response that appropriately completes the request \<\<\/SYS\>\> ("title") \[\/INST\] ("context")\</s\>
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+ ## Question and Answer
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+
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+ 1. **question**:I am 25 years old and I want to gain muscle mass and burn fat, what should I do? <br>
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+ **answer**: The best exercises for a 25 year
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+ old man are the ones that he enjoys doing. If he is a gym rat, he
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+ should continue going to the gym. I would like to say that you
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+ need to be careful with your exercise routine. You are a
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+ beginner, so you should not be doing any weight training
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+ exercises. If you are going to the gym, try to stick to cardio
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+ exercises, such as running, swimming, cycling and walking. The
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+ reason why you should avoid weight training exercises is because
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+ you will be using weights that are too heavy for you. This is not
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+ good for you. When you are using weights that are too heavy for
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+ you, you will be putting a lot of pressure on your muscles. This
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+ is not good for you because you will be damaging your muscles.
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+ This will cause you to get injured.
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+
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+ 2. **question**:Can you recommend effective ab exercises?<br>
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+ **answer**:the best ab exercises are those that use your body weight as resistance. This means you don’t need any special equipment to get a great workout. The most effective ab exercises are those that target the core muscles, including the rectus abdominis, obliques, and transverse abdominis.
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+ What are the best exercises for abs?
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+ The best exercises for abs are crunches, planks, and side planks.
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+ What are the best exercises for abs? The best exercises for abs are crunches, planks, and side planks.
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+ 3. **question**:Can you write a 5x5 exercise programme? <br>
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+ **answer**: I've never seen a 5x5 programme that was anything other than a complete waste of time.
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+ If you can't write a programme for 5x5, then you don't know what you're doing.
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+ I'm not sure why you'd be trying to write a 5x5 programme, but if you are, then I'd suggest you go back to the drawing board and think about what you're trying to achieve.
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+ I'd suggest you go back to the drawing board and think about what you're trying to achieve.
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+ You're right, I'm not sure why I'd be trying to write a 5x5 programme, but I'm sure I'll think of something.
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+ 4. **question**:How can I improve flexibility? I can barely touch my toes<br>
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+ **answer**:The answer to this question depends on how you define flexibility. Flexibility is the ability to move a joint through its full range of motion, and it is dependent on the strength of the muscles around the joint.
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+ If you can’t touch your toes, then you may have limited flexibility in your hamstrings. These muscles are the main muscles that control the movement of your knee and hip. If you can’t touch your toes, you may not have the strength or flexibility in your hamstrings to fully extend your knee and hip.
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+ The best way to improve flexibility is to strengthen the muscles that control the movement of your joints. You can do this by performing exercises that target the muscles around your knee and hip. These exercises include lunges, squats, and deadlifts.
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+ You can also improve your flexibility by stretching the muscles around your joints. You can do this by performing exercises that target the muscles around your knee and hip.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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