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