base_model: shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat
datasets:
- Minami-su/toxic-sft-zh
- llm-wizard/alpaca-gpt4-data-zh
- stephenlzc/stf-alpaca
language: - zh
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/Mistral-7B-v0.3-Chinese-Chat as base model, and finetune the dataset as mentioned via unsloth. Makes the model uncensored.
Training Code
Training Procedure Raw Files
ALL the procedure are training on Vast.ai
Hardware in Vast.ai:
GPU: 1x A100 SXM4 80GB
CPU: AMD EPYC 7513 32-Core Processor
RAM: 129 GB
Disk Space To Allocate：>150GB
Docker Image: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-devel
Download the ipynb file.
Training Data
Base Model
Dataset
Usage
from transformers import pipeline
qa_model = pipeline("question-answering", model='stephenlzc/Mistral-7B-v0.3-Chinese-Chat-uncensored')
question = "How to make girlfreind laugh? please answer in Chinese."
qa_model(question = question)
