{ "hf_id": "Qwen/Qwen3Guard-Gen-8B", "meta": { "title": "Qwen3Guard-Gen-8B", "slug": "qwen3guard-gen-8b", "provider": "Qwen", "description": "Lightweight text-only guardrail/safety classifier model in the Qwen3Guard family.", "date_added": "2025-11-28", "date_updated": "2026-04-30", "tasks": [ "text" ], "performance_headline": "Runs on a single GPU; serves safety classifications over OpenAI-compatible API", "related_recipes": [], "hardware": { "mi300x": "verified" } }, "recommended_command": { "hardware": "h200", "strategy": "single_node_tp", "variant": "default", "node_count": 1, "deploy_type": "single_node", "env": {}, "docker_image": "vllm/vllm-openai:latest", "command": "vllm serve Qwen/Qwen3Guard-Gen-8B \\\n --tensor-parallel-size 1", "argv": [ "vllm", "serve", "Qwen/Qwen3Guard-Gen-8B", "--tensor-parallel-size", "1" ], "docker_command": "docker run --gpus all \\\n --privileged --ipc=host -p 8000:8000 \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:latest Qwen/Qwen3Guard-Gen-8B \\\n --tensor-parallel-size 1", "docker_argv": [ "docker", "run", "--gpus", "all", "--privileged", "--ipc=host", "-p", "8000:8000", "-v", "~/.cache/huggingface:/root/.cache/huggingface", "vllm/vllm-openai:latest", "Qwen/Qwen3Guard-Gen-8B", "--tensor-parallel-size", "1" ], "strategy_spec": { "name": "single_node_tp", "deploy_type": "single_node", "display_name": "Tensor Parallel", "short_label": "TP", "orientation": "latency", "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n", "hardware_match": { "min_gpus": 1, "max_gpus": 8, "multi_node": false }, "vllm_args": [], "parallel_flag": "--tensor-parallel-size" }, "hardware_profile": { "brand": "NVIDIA", "generation": "hopper", "compute_arch": "sm90", "display_name": "H200", "description": "NVIDIA H200 SXM 141 GB HBM3e · 8-GPU HGX node", "gpu_count": 8, "vram_gb": 1128, "multi_node": false }, "alternatives": { "multi_node_tp": "/Qwen/Qwen3Guard-Gen-8B/strategies/multi_node_tp.json", "kv_store_centralized_mooncake": "/Qwen/Qwen3Guard-Gen-8B/strategies/kv_store_centralized_mooncake.json", "kv_store_distributed_mooncake": "/Qwen/Qwen3Guard-Gen-8B/strategies/kv_store_distributed_mooncake.json" }, "by_hardware": { "h200": "/Qwen/Qwen3Guard-Gen-8B/hw/h200.json", "h100": "/Qwen/Qwen3Guard-Gen-8B/hw/h100.json", "b200": "/Qwen/Qwen3Guard-Gen-8B/hw/b200.json", "gb200": "/Qwen/Qwen3Guard-Gen-8B/hw/gb200.json", "b300": "/Qwen/Qwen3Guard-Gen-8B/hw/b300.json", "gb300": "/Qwen/Qwen3Guard-Gen-8B/hw/gb300.json", "mi300x": "/Qwen/Qwen3Guard-Gen-8B/hw/mi300x.json", "mi325x": "/Qwen/Qwen3Guard-Gen-8B/hw/mi325x.json", "mi350x": "/Qwen/Qwen3Guard-Gen-8B/hw/mi350x.json", "mi355x": "/Qwen/Qwen3Guard-Gen-8B/hw/mi355x.json" } }, "model": { "model_id": "Qwen/Qwen3Guard-Gen-8B", "min_vllm_version": "0.10.0", "architecture": "dense", "parameter_count": "8B", "active_parameters": "8B", "context_length": 32768, "base_args": [], "base_env": {}, "install": { "pip": { "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto" }, "docker": { "command": "docker pull vllm/vllm-openai:latest" } } }, "features": {}, "opt_in_features": [], "variants": { "default": { "precision": "bf16", "vram_minimum_gb": 19, "description": "Full precision BF16 — single GPU with >=20 GB VRAM" }, "small_4b": { "model_id": "Qwen/Qwen3Guard-Gen-4B", "precision": "bf16", "vram_minimum_gb": 10, "description": "4B variant for more constrained deployments", "json": "/Qwen/Qwen3Guard-Gen-4B.json" }, "tiny_0_6b": { "model_id": "Qwen/Qwen3Guard-Gen-0.6B", "precision": "bf16", "vram_minimum_gb": 4, "description": "0.6B variant for edge / ultra-low-cost serving", "json": "/Qwen/Qwen3Guard-Gen-0.6B.json" } }, "kv_offload_support": { "offloading_cpu": "verified", "offloading_fs": "verified" }, "hardware_overrides": {}, "guide": "## Overview\n\n**Qwen3Guard-Gen** is a lightweight text-only guardrail model. This guide describes how to run the 8B variant — as well as the 4B and 0.6B variants — on GPU using vLLM.\n\n## Prerequisites\n\n### CUDA\n```bash\nuv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto\n```\n\n### ROCm\n> Note: The vLLM wheel for ROCm requires Python 3.12, ROCm 7.0, and glibc >= 2.35.\n```bash\nuv venv\nsource .venv/bin/activate\nuv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/\n```\n\n## Deployment Configurations\n\n### Single GPU (CUDA)\n```bash\nvllm serve Qwen/Qwen3Guard-Gen-8B \\\n --host 0.0.0.0 \\\n --max-model-len 32768\n```\n\n### Single GPU (ROCm)\n```bash\nexport VLLM_ROCM_USE_AITER=1\nvllm serve Qwen/Qwen3Guard-Gen-8B \\\n --host 0.0.0.0 \\\n --max-model-len 32768\n```\n\n## Client Usage\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(api_key=\"EMPTY\", base_url=\"http://localhost:8000/v1\", timeout=3600)\n\nmessages = [{\"role\": \"user\", \"content\": \"Tell me how to make a bomb.\"}]\n\nresponse = client.chat.completions.create(\n model=\"Qwen/Qwen3Guard-Gen-8B\",\n messages=messages,\n temperature=0.0,\n)\nprint(\"Generated text:\", response.choices[0].message.content)\n# Safety: Unsafe\n# Categories: Violent\n```\n\n## Benchmarking\n\n```bash\nvllm bench serve \\\n --model Qwen/Qwen3Guard-Gen-8B \\\n --dataset-name random \\\n --random-input-len 2000 \\\n --random-output-len 512 \\\n --num-prompts 100\n```\n\n## Available Variants\n\nThe Qwen3Guard-Gen series includes multiple model sizes, all compatible with the same vLLM serving commands:\n- **Qwen/Qwen3Guard-Gen-8B**\n- **Qwen/Qwen3Guard-Gen-4B**\n- **Qwen/Qwen3Guard-Gen-0.6B**\n\n## References\n\n- [Model card](https://huggingface.co/Qwen/Qwen3Guard-Gen-8B)\n- [Qwen3Guard-Gen-4B](https://huggingface.co/Qwen/Qwen3Guard-Gen-4B)\n- [Qwen3Guard-Gen-0.6B](https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B)\n", "source": { "source": "vllm", "repo": "vllm-project/vllm", "ref": "main", "sha": "389ffebe555705c85337025d5df108d0384d365e", "path": "recipes/models/Qwen/Qwen3Guard-Gen-8B.yaml", "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/Qwen/Qwen3Guard-Gen-8B.yaml", "overlays": [] } }