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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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  ## 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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  - **Developed by:** Mochamad Aris Zamroni
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  - **Funded by [optional]:** [More Information Needed]
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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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  #### Hardware
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- AMD Radeon 780M
 
 
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  #### Software
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  Microsoft Windows DirectML
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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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  Mochamad Aris Zamroni
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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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  ## Model Details
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+ meta-llama/Meta-Llama-3.1-8B-Instruct quantized to ONNX GenAI INT4 with Microsoft DirectML optimization
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  ### Model Description
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+ meta-llama/Meta-Llama-3.1-8B-Instruct quantized to ONNX GenAI INT4 with Microsoft DirectML optimization
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+ https://onnxruntime.ai/docs/genai/howto/install.html#directml
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+ Created using ONNX Runtime GenAI's builder.py
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+ https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/src/python/py/models/builder.py -o builder.py
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+ INT4 accuracy level: FP32 (float32)
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+ 8-bit quantization for MoE layers
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  - **Developed by:** Mochamad Aris Zamroni
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  - **Funded by [optional]:** [More Information Needed]
 
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  ### Direct Use
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+ This is Windows DirectML optimized model.
 
 
 
 
 
 
 
 
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+ Prerequisites:
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+ 1. Install Python 3.10 from Windows Store:
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+ https://apps.microsoft.com/detail/9pjpw5ldxlz5?hl=en-us&gl=US
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+ 2. Open command line cmd.exe
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+ 3. Create python virtual environment:
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+ mkdir c:\temp
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+ cd c:\temp
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+ python -m venv dmlgenai
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+ dmlgenai\Scripts\activate.bat
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+ pip install onnxruntime-genai-directml
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  [More Information Needed]
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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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  #### Preprocessing [optional]
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  [More Information Needed]
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  #### Speeds, Sizes, Times [optional]
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+ 15 token/s in Radeon 780M with 8GB dedicated RAM
 
 
 
 
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  #### Metrics
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  #### Hardware
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+ AMD Ryzen 7840U with integrated Radeon 780M GPU
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+ RAM 32GB
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+ shared VRAM 8GB
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  #### Software
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  Microsoft Windows DirectML
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  ## Model Card Authors [optional]
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  Mochamad Aris Zamroni
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