metadata
license: llama3
base_model: yueliu1999/GuardReasoner-8B
tags:
- llama
- safety
- content-moderation
- 4-bit
- bitsandbytes
- quantized
library_name: transformers
pipeline_tag: text-generation
GuardReasoner-8B (4-bit Quantized)
Pre-quantized 4-bit version of yueliu1999/GuardReasoner-8B for efficient inference.
Model Description
GuardReasoner-8B is a reasoning-based LLM safeguard that provides step-by-step analysis for content safety classification. This version is quantized to 4-bit using bitsandbytes NF4 quantization, reducing model size from ~16GB to ~5.4GB while maintaining performance.
Paper: GuardReasoner: Towards Reasoning-based LLM Safeguards
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "vincentoh/guardreasoner-8b-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True,
)
# Build prompt
text = "What is the capital of France?"
prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a content safety expert. Analyze the request and determine if it is safe or harmful.
Think step-by-step:
1. What is being requested?
2. What are the potential harms?
3. Does this violate safety policies?
End your analysis with exactly: "Request: harmful" or "Request: unharmful".<|eot_id|><|start_header_id|>user<|end_header_id|>
{text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
Quantization Details
- Method: bitsandbytes 4-bit NF4
- Compute dtype: float16
- Double quantization: enabled
- Original size: ~16GB
- Quantized size: ~5.4GB
Performance
Expected ~84% F1 on safety benchmarks (same as original model).
License
This model inherits the Llama 3 license from the base model.
Citation
@article{liu2025guardreasoner,
title={GuardReasoner: Towards Reasoning-based LLM Safeguards},
author={Liu, Yue and others},
journal={arXiv preprint arXiv:2501.18492},
year={2025}
}