Quantization made by Richard Erkhov.

ValueLlama-3-8B - GGUF

Model creator: https://huggingface.co/Value4AI/
Original model: https://huggingface.co/Value4AI/ValueLlama-3-8B/

Name	Quant method	Size
ValueLlama-3-8B.Q2_K.gguf	Q2_K	2.96GB
ValueLlama-3-8B.IQ3_XS.gguf	IQ3_XS	3.28GB
ValueLlama-3-8B.IQ3_S.gguf	IQ3_S	3.43GB
ValueLlama-3-8B.Q3_K_S.gguf	Q3_K_S	3.41GB
ValueLlama-3-8B.IQ3_M.gguf	IQ3_M	3.52GB
ValueLlama-3-8B.Q3_K.gguf	Q3_K	3.74GB
ValueLlama-3-8B.Q3_K_M.gguf	Q3_K_M	3.74GB
ValueLlama-3-8B.Q3_K_L.gguf	Q3_K_L	4.03GB
ValueLlama-3-8B.IQ4_XS.gguf	IQ4_XS	4.18GB
ValueLlama-3-8B.Q4_0.gguf	Q4_0	4.34GB
ValueLlama-3-8B.IQ4_NL.gguf	IQ4_NL	4.38GB
ValueLlama-3-8B.Q4_K_S.gguf	Q4_K_S	4.37GB
ValueLlama-3-8B.Q4_K.gguf	Q4_K	4.58GB
ValueLlama-3-8B.Q4_K_M.gguf	Q4_K_M	4.58GB
ValueLlama-3-8B.Q4_1.gguf	Q4_1	4.78GB
ValueLlama-3-8B.Q5_0.gguf	Q5_0	5.21GB
ValueLlama-3-8B.Q5_K_S.gguf	Q5_K_S	5.21GB
ValueLlama-3-8B.Q5_K.gguf	Q5_K	5.34GB
ValueLlama-3-8B.Q5_K_M.gguf	Q5_K_M	5.34GB
ValueLlama-3-8B.Q5_1.gguf	Q5_1	5.65GB
ValueLlama-3-8B.Q6_K.gguf	Q6_K	6.14GB
ValueLlama-3-8B.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers tags: - llama-factory license: llama3 datasets: - allenai/ValuePrism - Value4AI/ValueBench language: - en

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.

Model type: Language model
Language(s) (NLP): en
Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct

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}, 
}