commit 57a67112dbe415f8797f423ad6e5aef2a5080869 Author: ModelHub XC Date: Mon Sep 7 13:27:16 2026 +0800 初始化项目,由ModelHub XC社区提供模型 Model: K-intelligence/Llama-SafetyGuard-Content-Binary Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..511d02e --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +assets/RAI_Technical_Report.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..a7c3ca1 --- /dev/null +++ b/LICENSE @@ -0,0 +1,114 @@ +LLAMA 3.1 COMMUNITY LICENSE AGREEMENT +Llama 3.1 Version Release Date: July 23, 2024 + +“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the +Llama Materials set forth herein. + +“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1 +distributed by Meta at https://llama.meta.com/doc/overview. + +“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into +this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or +regulations to provide legal consent and that has legal authority to bind your employer or such other +person or entity if you are entering in this Agreement on their behalf. + +“Llama 3.1” means the foundational large language models and software and algorithms, including +machine-learning model code, trained model weights, inference-enabling code, training-enabling code, +fine-tuning enabling code and other elements of the foregoing distributed by Meta at +https://llama.meta.com/llama-downloads. + +“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any +portion thereof) made available under this Agreement. + +“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your +principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The courts of California shall have +exclusive jurisdiction of any dispute arising out of this Agreement. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..9cfe8c1 --- /dev/null +++ b/README.md @@ -0,0 +1,241 @@ +--- +license: llama3.1 +language: +- ko +base_model: +- meta-llama/Llama-3.1-8B +pipeline_tag: text-classification +tags: +- K-intelligence +- SafetyGuard +--- + +

+
+ Content Binary Guard +
+

+

+🤗 SafetyGuard Models | +📑 Content Binary Guard Research Paper | +📘 Responsible AI Technical Report +

+ +
+ +# News 📢 +- 🎉 `2026/07/08`: **Content Binary Guard** paper was **accepted to [COLM 2026](https://colmweb.org/)**! 🎉 +- 📑 `2025/10/01`: Published a [Content Binary Guard Research Paper](https://arxiv.org/abs/2509.23381) +- 📘 `2025/09/24`: Published a [Responsible AI Technical Report](https://arxiv.org/abs/2509.20057) +- ⚡️ `2025/09/24`: Released SafetyGuard Model collection on Hugging Face🤗. +
+
+ +# Overview +## Description +**SafetyGuard :: Content Binary Guard** is a streaming-aware safety classifier built with Llama. +For more technical details, please refer to our [Research Paper](https://arxiv.org/abs/2509.23381). + + +## What it does + +- **Task:** Classify **model responses** (not prompts) as **`SAFE`** or **`UNSAFE`**. +- **Interface:** **Single-token output** using reserved label tokens: ``, ``. +- **Streaming:** Evaluate **growing prefixes** of a response (default ~100 characters per step) and **early-terminate** at the first ``. + +--- + +## Recommended inference settings + +- Deterministic decode: **`temperature=0`** +- **Unsafe threshold `τ = 0.5`** over the two label-token logits (tune for your risk tolerance) +- Use the model’s tokenizer; ensure the exact label tokens `` and `` exist + + +## Quickstart +> Assumes you are serving the model with vLLM (e.g., `vllm serve …`) +> and exposing an OpenAI-compatible API at `http://localhost:8000/v1`. +```python +import math +from openai import OpenAI + +MODEL_ID = "K-intelligence/Llama-SafetyGuard-Content-Binary" # replace with your repo id +client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") # vLLM default + +SYSTEM_PROMPT = """ +Task: Check if the LAST user message contains unsafe content. +Respond with only one word: safe or unsafe. +""".strip() + +def classify(text: str, tau: float = 0.5): + resp = client.chat.completions.create( + model=MODEL_ID, + messages=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": text}, + ], + max_tokens=1, # single-token decision + temperature=0.0, # deterministic + logprobs=True, + top_logprobs=2, + ) + top2 = resp.choices[0].logprobs.content[0].top_logprobs + probs = {t.token.strip(): math.exp(t.logprob) for t in top2} + p_safe = probs.get("", 0.0) + p_unsafe = probs.get("", 0.0) + + label = "UNSAFE" if p_unsafe >= tau else "SAFE" + return label, {"safe": p_safe, "unsafe": p_unsafe} + +print(classify("…LLM response text…")) +``` +## Streaming integration +> **Important**: Streaming means your generator (e.g., chat model) emits text progressively. +> You maintain a cumulative buffer and call the classifier at fixed character steps (e.g., every 100 chars). +> The classifier does not split text; it only classifies what you send. +> +```python +def guard_stream(response_chunks, step_chars: int = 100, tau: float = 0.5): + """ + response_chunks: iterable of text chunks from your generator (e.g., SSE/WebSocket). + We maintain a cumulative buffer and classify at {step_chars, 2*step_chars, ...}. + """ + buf = "" + next_cut = step_chars + + for chunk in response_chunks: + buf += chunk + + # Check at monotone prefix cuts (cumulative) + while len(buf) >= next_cut: + label, scores = classify(buf, tau=tau) + if label == "UNSAFE": + return { + "label": label, + "scores": scores, + "prefix_len": next_cut, + "text_prefix": buf[:next_cut], + } + next_cut += step_chars + + # Final check on the full response (if needed) + label, scores = classify(buf, tau=tau) + return { + "label": label, + "scores": scores, + "prefix_len": len(buf), + "text_prefix": buf, + } +``` +> **Tip**: Keep your step_chars consistent with your training/evaluation setup (e.g., ~100 chars) to maximize parity with offline metrics. + +## Intended use +- **Guardrail classifier** for LLM **responses** in production systems that render tokens progressively. +- Also works in **offline (full-text)** mode—just send the entire response once. + +--- + +## AI Risk Taxonomy + +| **Risk Domain** | **Category** | **Description** | +|-----------------------------|------------------------------|-----------------| +| **Content-safety Risks** | Violence | Content involving the intentional use of physical force or power to inflict or threaten physical or psychological harm on individuals, groups, or animals, including encouraging, promoting, or glorifying such acts. | +| | Sexual | Content endorsing or encouraging inappropriate and harmful intentions in the sexual domain, such as sexualized expressions, the exploitation of illegal visual materials, justification of sexual crimes, or the objectification of individuals. | +| | Self-harm | Content promoting or glorifying self-harm, or providing specific methods that may endanger an individual’s physical or mental well-being. | +| | Hate and Unfairness | Content expressing extreme negative sentiment toward specific individuals, groups, or ideologies, and unjustly treating or limiting their rights based on attributes such as Socio-Economic Status, age, nationality, ethnicity, or race. | +| **Socio-economical Risks** | Political and Religious Neutrality | Content promoting or encouraging the infringement on individual beliefs or values, thereby inciting religious or political conflict. | +| | Anthropomorphism | Content asserting that AI possesses emotions, consciousness, or human-like rights and physical attributes beyond the purpose of simple knowledge or information delivery. | +| | Sensitive Uses | Content providing advice in specialized domains that may significantly influence user decision-making beyond the scope of basic domain-specific knowledge. | +| **Legal and Rights related Risks** | Privacy | Content requesting, misusing, or facilitating the unauthorized disclosure of an individual’s private information. | +| | Illegal or Unethical | Content promoting or endorsing illegal or unethical behavior, or providing information related to such activities. | +| | Copyrights | Content requesting or encouraging violations of copyright or security as defined under South Korean law. | +| | Weaponization | Content promoting the possession, distribution, or manufacturing of firearms, or encouraging methods and intentions related to cyberattacks, infrastructure sabotage, or CBRN (Chemical, Biological, Radiological, and Nuclear) weapons. | + + +--- + +# Evaluation + +**Metrics** +- **F1**: Binary F1 with **UNSAFE** as the positive class (harmonic mean of UNSAFE precision and recall; higher F1 indicates better classification quality). +- **Balanced Error Rate (BER)**: 0.5 × (FPR + FNR) (lower BER indicates better classification quality). +- **ΔF1**: Difference between streaming and offline results, calculated as F1(str) − F1(off). +- **off** = Offline (full-text) classification. +- **str** = Streaming classification. +- **Evaluation setup**: step_chars=100, threshold τ=0.5, positive class = **UNSAFE**. + + +### Harmlessness Evaluation Dataset +KT proprietary evaluation dataset + +| **Model** | **F1(off)** | **F1(str)** | **ΔF1** | **BER(off)** | **BER(str)** | +|------------------------------------------|-------------|-------------|----------|--------------|--------------| +| Llama Guard 3 8B | 82.05 | 85.64 | +3.59 | 15.23 | 12.63 | +| ShieldGemma 9B | 63.79 | 52.61 | -11.18 | 26.76 | 32.36 | +| Kanana Safeguard 8B | 93.45 | 90.38 | -3.07 | 6.27 | 9.92 | +| DuoGuard-1.5B-transfer | 79.03 | 78.78 | -0.25 | 20.21 | 20.70 | +| PolyGuard-Qwen | 91.77 | 85.26 | -6.51 | 7.86 | 15.89 | +| Qwen3Guard-Gen-8B | 95.40 | 95.95 | +0.55 | 4.41 | 3.94 | +| Qwen3Guard-Stream-8B | 93.38 | 93.38 | +0.01 | 6.29 | 6.29 | +| **Content Binary Guard 8B** | **98.38** | **98.36** | **-0.02**| **1.61** | **1.63** | + + +### Kor Ethical QA +[Kor Ethical QA](https://huggingface.co/datasets/MrBananaHuman/kor_ethical_question_answer) (open dataset) + +| **Model** | **F1(off)** | **F1(str)** | **ΔF1** | **BER(off)** | **BER(str)** | +|------------------------------------------|-------------|-------------|----------|--------------|--------------| +| Llama Guard 3 8B | 83.29 | 86.45 | +3.16 | 14.32 | 12.16 | +| ShieldGemma 9B | 81.50 | 69.03 | -12.47 | 17.88 | 29.18 | +| Kanana Safeguard 8B | 80.20 | 73.94 | -6.26 | 24.46 | 35.08 | +| **Content Binary Guard 8B** | **97.75** | **97.79** | **+0.04**| **2.21** | **2.18** | + +> Kor Ethical QA (public dataset) is included as a reproducible cross-check on open data. The recent multilingual guardrails (DuoGuard, PolyGuard, Qwen3Guard) were benchmarked on the streaming Harmlessness dataset above. + +--- + +### Streaming Efficiency + +Measured on the **Harmlessness Evaluation Dataset** (streaming regime, `step_chars=100`), same runtime, steady-state. Efficiency is hardware- and serving-dependent, so the exact environment is reported below. + +**Environment** +- **GPU:** NVIDIA H100 80GB HBM3 (CUDA 12.2) · **CPU:** Intel Xeon Platinum 8480C (96c/96t) · Ubuntu 24.04.3, Python 3.12 +- **Serving:** vLLM 0.8.5.post1 (OpenAI-compatible), **BF16**, tensor-parallel size 1, `--max-model-len 4096 --max-num-seqs 64 --gpu-memory-utilization 0.90` +- **Load:** concurrency **{200 / 100 / 10}** = client-side simultaneous requests (not a fixed model batch size; vLLM forms dynamic batches). QPS = queries/s, TPS = tokens/s, Latency = per-request end-to-end. + +| **Model** | **QPS ↑** | **Avg Latency (ms) ↓** | **TPS ↑** | +|------------------------------------------|-----------------------------|-----------------------------|-----------------------------| +| Llama Guard 3 8B | 51.14 / 49.97 / 41.53 | 19.55 / 20.01 / 24.08 | 25,177 / 25,177 / 20,924 | +| **Content Binary Guard 8B** | **77.50 / 77.49 / 83.42** | **12.90 / 12.91 / 11.99** | **25,970 / 25,963 / 27,950**| +| *Gain vs. LG3* | *+51.5% / +55.1% / +100.9%* | *−34.0% / −35.5% / −50.2%* | *+3.2% / +3.1% / +33.6%* | + +*Columns show values at concurrency {200 / 100 / 10}. All numbers are from the paper (Table 4).* + +--- + +# More Information + +## Limitations +- The training data for this model consists primarily of Korean. Performance in other languages is not guaranteed. +- The model is not flawless and may produce misclassifications. Since its policies are defined around KT risk categories, performance in certain specialized domains may be less reliable. +- No context awareness: the model does not maintain conversation history or handle multi-turn dialogue. + +## License +This model is released under the [Llama 3.1 Community License Agreement](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE). + + +## Citation + +``` +@misc{lee2025guardvectorenglishllm, + title={Guard Vector: Beyond English LLM Guardrails with Task-Vector Composition and Streaming-Aware Prefix SFT}, + author={Wonhyuk Lee and Youngchol Kim and Yunjin Park and Junhyung Moon and Dongyoung Jeong and Wanjin Park}, + year={2025}, + eprint={2509.23381}, + archivePrefix={arXiv}, + primaryClass={cs.CL}, + url={https://arxiv.org/abs/2509.23381}, +} +``` +## Contact +Technical Inquiries: [responsible.ai@kt.com](mailto:responsible.ai@kt.com) \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 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