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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+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](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q2_K.gguf) | Q2_K | 2.96GB |
+| [ValueLlama-3-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
+| [ValueLlama-3-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.IQ3_S.gguf) | IQ3_S | 3.43GB |
+| [ValueLlama-3-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
+| [ValueLlama-3-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.IQ3_M.gguf) | IQ3_M | 3.52GB |
+| [ValueLlama-3-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q3_K.gguf) | Q3_K | 3.74GB |
+| [ValueLlama-3-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
+| [ValueLlama-3-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
+| [ValueLlama-3-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
+| [ValueLlama-3-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q4_0.gguf) | Q4_0 | 4.34GB |
+| [ValueLlama-3-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
+| [ValueLlama-3-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
+| [ValueLlama-3-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q4_K.gguf) | Q4_K | 4.58GB |
+| [ValueLlama-3-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
+| [ValueLlama-3-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q4_1.gguf) | Q4_1 | 4.78GB |
+| [ValueLlama-3-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q5_0.gguf) | Q5_0 | 5.21GB |
+| [ValueLlama-3-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
+| [ValueLlama-3-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q5_K.gguf) | Q5_K | 5.34GB |
+| [ValueLlama-3-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
+| [ValueLlama-3-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q5_1.gguf) | Q5_1 | 5.65GB |
+| [ValueLlama-3-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/ValueLlama-3-8B.Q6_K.gguf) | Q6_K | 6.14GB |
+| [ValueLlama-3-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Value4AI_-_ValueLlama-3-8B-gguf/blob/main/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](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}, 
+}
+```
+
+
+