Instructions to use Value4AI/ValueLlama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Value4AI/ValueLlama-3-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Value4AI/ValueLlama-3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Value4AI/ValueLlama-3-8B")
model = AutoModelForCausalLM.from_pretrained("Value4AI/ValueLlama-3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Value4AI/ValueLlama-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Value4AI/ValueLlama-3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Value4AI/ValueLlama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Value4AI/ValueLlama-3-8B

SGLang

How to use Value4AI/ValueLlama-3-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Value4AI/ValueLlama-3-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Value4AI/ValueLlama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Value4AI/ValueLlama-3-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Value4AI/ValueLlama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Value4AI/ValueLlama-3-8B with Docker Model Runner:
```
docker model run hf.co/Value4AI/ValueLlama-3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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}, 
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Value4AI/ValueLlama-3-8B

Quantizations

4 models

Datasets used to train Value4AI/ValueLlama-3-8B

Paper for Value4AI/ValueLlama-3-8B

Measuring Human and AI Values based on Generative Psychometrics with Large Language Models

Paper • 2409.12106 • Published Sep 18, 2024 • 2