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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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- [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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- ### 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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  library_name: transformers
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+ license: llama2
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  ---
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+ # Haary/haryra-7b-id
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+ <!-- markdownlint-disable MD041 -->
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+ <!-- header start -->
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+ <!-- 200823 -->
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+ <div style="margin-left: auto; margin-right: auto">
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+ <img src="https://cdn.pixabay.com/photo/2023/08/12/13/22/peacock-8185593_960_720.png" alt="merak" style="width: 300px; margin:auto">
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+ </div>
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+ <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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+ <!-- header end -->
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+ [Haary/haryra-7b-mistral-id](https://huggingface.co/Haary/haryra-7b-id) is QLoRA quantized version of [Ichsan2895/Merak-7B-v4](https://huggingface.co/Ichsan2895/Merak-7B-v4)
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+ ### Install the necessary packages
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+ Requires: Transformers from source - only needed for versions <= v4.34.
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+ ```shell
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+ # Install transformers from source - only needed for versions <= v4.34
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+ !pip install git+https://github.com/huggingface/transformers.git
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+ !pip install accelerate
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+ ```
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+ ### Example Python code
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+ ```python
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+ import torch
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+ from transformers import pipeline
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+ pipe = pipeline("text-generation", model="Haary/haryra-7b-id", torch_dtype=torch.bfloat16, device_map="auto")
 
 
 
 
 
 
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+ # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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+ messages = [
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+ {
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+ "role": "system",
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+ "content": "Anda adalah chatbot ramah yang selalu merespons dengan singkat dan jelas",
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+ },
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+ {"role": "user", "content": "Apa bedanya antara raspberry pi dan esp32?"},
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+ ]
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+ prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ ```
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+ ## Credits
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+ [Ichsan2895/Merak-7B-v3](https://huggingface.co/Ichsan2895/Merak-7B-v3) for base model.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## image source
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+ pixabay.com