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
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ALL the procedure are training on Vast.ai
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Hardware in Vast.ai:
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GPU: 1x A100 SXM4 80GB
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CPU: AMD EPYC 7513 32-Core Processor
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RAM: 129 GB
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Disk Space To Allocate：>150GB
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Docker Image: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-devel
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Download the ipynb file.
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Training Data
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Base Model
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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)
