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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- nampdn-ai/tiny-codes |
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- nlpai-lab/openassistant-guanaco-ko |
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- philschmid/guanaco-sharegpt-style |
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language: |
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- ko |
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- en |
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inference: false |
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tags: |
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- unsloth |
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- phi-3 |
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- gguf |
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- ollama |
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pipeline_tag: text-generation |
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--- |
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# Phi-3-medium-4k-instruct-ko-poc-gguf-v0.1 |
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## Model Details |
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This model converted the [daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-v0.1](https://huggingface.co/daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-v0.1) to gguf 4-bit format. |
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For detailed instructions, please refer to [Microsoft's official repo](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf). |
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### Dataset |
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The dataset used for training is as follows. To prevent catastrophic forgetting, I included non-Korean corpus as training data. Note that we did not use all of the data, but only sampled some of it. Korean textbooks were converted to Q&A format. The Guanaco dataset has been reformatted to fit the multiturn format like <|user|>\n{Q1}<|end|>\n<|assistant|>\n{A1}<|end|>\n<|user|>\n{Q2}<|end|>\n<|assistant|>\n{A2}<|end|>. |
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- Korean textbooks (https://huggingface.co/datasets/nampdn-ai/tiny-codes) |
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- Korean translation of Guanaco (https://huggingface.co/datasets/nlpai-lab/openassistant-guanaco-ko) |
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- Guanaco Sharegpt style (https://huggingface.co/datasets/philschmid/guanaco-sharegpt-style) |
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## How to Get Started with the Model using Ollama |
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1. **Install [Ollama](https://ollama.com/):** |
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``` |
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curl -fsSL https://ollama.com/install.sh | sh |
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``` |
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2. **Get the Modelfile:** |
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``` |
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huggingface-cli download daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-gguf-v0.1 Modelfile_q4 --local-dir /path/to/your/local/dir |
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``` |
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3. **Build the Ollama Model:** |
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Use the Ollama CLI to create your model with the following command: |
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``` |
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ollama create phi3-ko -f Modelfile_q4 |
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``` |
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4. **Run the model:** |
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``` |
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ollama run phi3-ko What is Machine Learning? |
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``` |
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## Notes |
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### License |
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apache 2.0; The license of phi-3 is MIT, but I considered the licensing of the dataset and library used for training. |
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### Caution |
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This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)! |
