Instructions to use QuantFactory/ValueLlama-3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/ValueLlama-3-8B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/ValueLlama-3-8B-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/ValueLlama-3-8B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/ValueLlama-3-8B-GGUF", filename="ValueLlama-3-8B.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/ValueLlama-3-8B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/ValueLlama-3-8B-GGUF with Ollama:
ollama run hf.co/QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/ValueLlama-3-8B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/ValueLlama-3-8B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/ValueLlama-3-8B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/ValueLlama-3-8B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/ValueLlama-3-8B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/ValueLlama-3-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/ValueLlama-3-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ValueLlama-3-8B-GGUF-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:# Run inference directly in the terminal:
llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF:Use Docker
docker model run hf.co/QuantFactory/ValueLlama-3-8B-GGUF: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.
- 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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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/ValueLlama-3-8B-GGUF:# Run inference directly in the terminal: llama-cli -hf QuantFactory/ValueLlama-3-8B-GGUF: