library_name: transformers
license: apache-2.0
datasets:
- nampdn-ai/tiny-codes
- nlpai-lab/openassistant-guanaco-ko
- philschmid/guanaco-sharegpt-style
language:
- ko
- en
inference: false
tags:
- unsloth
- phi-3
- gguf
- ollama
pipeline_tag: text-generation
Phi-3-medium-4k-instruct-ko-poc-gguf-v0.1
Model Details
This model converted the daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-v0.1 to gguf 4-bit format.
For detailed instructions, please refer to Microsoft's official repo.
Dataset
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|>.
- Korean textbooks (https://huggingface.co/datasets/nampdn-ai/tiny-codes)
- Korean translation of Guanaco (https://huggingface.co/datasets/nlpai-lab/openassistant-guanaco-ko)
- Guanaco Sharegpt style (https://huggingface.co/datasets/philschmid/guanaco-sharegpt-style)
How to Get Started with the Model using Ollama
- Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh
- Get the Modelfile:
huggingface-cli download daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-gguf-v0.1 Modelfile_q4 --local-dir /path/to/your/local/dir
- Build the Ollama Model: Use the Ollama CLI to create your model with the following command:
ollama create phi3-ko -f Modelfile_q4
- Run the model:
ollama run phi3-ko What is Machine Learning?
Notes
License
apache 2.0; The license of phi-3 is MIT, but I considered the licensing of the dataset and library used for training.
Caution
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)!