README.md

---

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

---

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# QuantFactory/ValueLlama-3-8B-GGUF
This is quantized version of [Value4AI/ValueLlama-3-8B](https://huggingface.co/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.

- **Model type:** Language model
- **Language(s) (NLP):** en
- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/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*](https://arxiv.org/abs/2409.12106).

## 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](https://github.com/Value4AI/gpv).


## BibTeX:

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

```bibtex
@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}, 
}
```