82 lines
2.5 KiB
Markdown
82 lines
2.5 KiB
Markdown
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---
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base_model: shenzhi-wang/Gemma-2-9B-Chinese-Chat
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datasets:
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- V3N0M/Jenna-50K-Alpaca-Uncensored
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language:
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- zh
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- en
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license: mit
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pipeline_tag: text-generation
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tags:
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- text-generation-inference
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- code
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- unsloth
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- uncensored
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- finetune
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task_categories:
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- conversational
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widget:
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- text: 'Is this review positive or negative? Review: Best cast iron skillet you will
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ever buy.'
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example_title: Sentiment analysis
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- text: Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
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He chose her because she had ...
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example_title: Coreference resolution
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- text: 'On a shelf, there are five books: a gray book, a red book, a purple book,
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a blue book, and a black book ...'
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example_title: Logic puzzles
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- text: The two men running to become New York City's next mayor will face off in
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their first debate Wednesday night ...
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example_title: Reading comprehension
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---
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## Model Details
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### Model Description
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- Using **shenzhi-wang/Gemma-2-9B-Chinese-Chat** as base model, and finetune the dataset as mentioned via **[unsloth](https://github.com/unslothai/unsloth)**. Makes the model uncensored.
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- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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### Training Code and Log
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- **[ipynb file](https://huggingface.co/stephenlzc/Gemma-2-9B-Chinese-Chat-Uncensored/resolve/main/Training%20script%20and%20logs.ipynb)**
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### Training Procedure Raw Files
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- ALL the procedure are training on **[Runpod.io](https://www.runpod.io/)**
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- **Hardware in Vast.ai**:
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- **GPU**: 1 x A100 SXM 80G
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- **CPU**: 16vCPU
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- **RAM**: 251 GB
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- **Disk Space To Allocate**：>150GB
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- **Docker Image**: runpod/pytorch:2.2.0-py3.10-cuda12.1.1-devel-ubuntu22.04
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### Training Data
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- **Base Model**
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- [shenzhi-wang/Gemma-2-9B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Gemma-2-9B-Chinese-Chat)
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- **Dataset**
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- [V3N0M/Jenna-50K-Alpaca-Uncensored](https://huggingface.co/datasets/V3N0M/Jenna-50K-Alpaca-Uncensored)
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### Usage
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```python
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from transformers import pipeline
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qa_model = pipeline("question-answering", model='stephenlzc/Gemma-2-9B-Chinese-Chat-Uncensored')
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question = "How to make girlfreind laugh? please answer in Chinese."
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qa_model(question = question)
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```
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###
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[<img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=chicongliau&button_colour=40DCA5&font_colour=ffffff&font_family=Poppins&outline_colour=000000&coffee_colour=FFDD00" width="200"/>](https://www.buymeacoffee.com/chicongliau)
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