Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,87 @@
+---
+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](https://huggingface.co/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](https://arxiv.org/abs/2501.18492)
+
+## Usage
+
+```python
+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
+
+```bibtex
+@article{liu2025guardreasoner,
+  title={GuardReasoner: Towards Reasoning-based LLM Safeguards},
+  author={Liu, Yue and others},
+  journal={arXiv preprint arXiv:2501.18492},
+  year={2025}
+}
+```