Transformers
GGUF
English
llama-factory
conversational
How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/ValueLlama-3-8B-GGUF",
	filename="",
)
llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

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QuantFactory/ValueLlama-3-8B-GGUF

This is quantized version of Value4AI/ValueLlama-3-8B created using llama.cpp

Original Model Card

Model Card for ValueLlama

Model Description

ValueLlama is designed for perception-level value measurement in an open-ended value space, which includes two tasks: (1) Relevance classification determines whether a perception is relevant to a value; and (2) Valence classification determines whether a perception supports, opposes, or remains neutral (context-dependent) towards a value. Both tasks are formulated as generating a label given a value and a perception.

Paper

For more information, please refer to our paper: Measuring Human and AI Values based on Generative Psychometrics with Large Language Models.

Uses

It is intended for use in research to measure human/AI values and conduct related analyses.

See our codebase for more details: https://github.com/Value4AI/gpv.

BibTeX:

If you find this model helpful, we would appreciate it if you cite our paper:

@misc{ye2024gpv,
      title={Measuring Human and AI Values based on Generative Psychometrics with Large Language Models}, 
      author={Haoran Ye and Yuhang Xie and Yuanyi Ren and Hanjun Fang and Xin Zhang and Guojie Song},
      year={2024},
      eprint={2409.12106},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.12106}, 
}
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GGUF
Model size
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Architecture
llama
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