metadata
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
task_categories:
- conversational
base_model: shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat
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. 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
Docker Image: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-devel
Training Data
Base Model shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat
Dataset