Mistral-7B-v0.3-Chinese-Chat-uncensored/README.md

base_model, datasets, language, license, pipeline_tag, tags, task_categories, widget

base_model	datasets	language	license	pipeline_tag	tags	task_categories	widget
shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat	Minami-su/toxic-sft-zh llm-wizard/alpaca-gpt4-data-zh stephenlzc/stf-alpaca	zh	mit	text-generation	text-generation-inference code unsloth uncensored finetune	conversational	text example_title Is this review positive or negative? Review: Best cast iron skillet you will ever buy. Sentiment analysis text example_title Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ... Coreference resolution text example_title On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ... Logic puzzles text example_title The two men running to become New York City's next mayor will face off in their first debate Wednesday night ... 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.
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Training Code

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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

  • shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat

• Dataset

  • Minami-su/toxic-sft-zh
  • llm-wizard/alpaca-gpt4-data-zh
  • stephenlzc/stf-alpaca

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)