granite-guardian-4.1-8b
Granite Guardian 4.1 8B is a specialized safety and evaluation model designed to judge whether LLM inputs and outputs meet specified criteria. It includes pre-built detectors for jailbreaks, profanity, hallucinations, and bias, and allows users to define custom criteria. The model operates in two modes: thinking (with step-by-step reasoning) or non-thinking (fast yes/no judgements). It is fine-tuned from IBM's base Granite 4.1 8B model and released under Apache 2.0.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | ibm-granite |
| Parameters | 8.4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 52.4k |
| Likes | 31 |
| Last updated | 2026-04-29 |
| Source | ibm-granite/granite-guardian-4.1-8b |
What granite-guardian-4.1-8b is
An 8.3B-parameter transformer-based judge model trained on human annotations and synthetic red-teaming data. Supports hybrid thinking/non-thinking modes via structured prompting with `<think>`, `<score>` tags. Can function as a reward model, hallucination detector in RAG pipelines, and function-call validator in agentic systems. Benchmarked on IFEval, InfoBench, JETTS, and custom safety datasets; reported improvements over Granite Guardian 3.3 (e.g., IFEval no-think BAcc 0.458→0.844).
Run granite-guardian-4.1-8b locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="ibm-granite/granite-guardian-4.1-8b")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: 8.3B parameters at fp32 ≈ 33 GB VRAM; fp16/bfloat16 ≈ 17 GB. Small enough for single A100 (80GB) or two A6000 (48GB) GPUs. Exact precision and quantization strategy not stated in card. CPU inference feasible for offline/batch scoring. Context length unknown; consult official docs for max token windows.
Unknown. Model card does not specify LoRA, QLoRA, or full fine-tuning recipes. GitHub repo and cookbooks referenced; review those for adapter-based approaches. Intended use strongly emphasizes prompt-based judging, not weights modification. If fine-tuning needed, verify compatibility with base Granite 4.1 infrastructure and obtain guidance from IBM Research.
When to avoid it — and what to weigh
- Latency-Critical Applications with Thinking Mode — Think mode with detailed reasoning traces will add inference overhead. Use no-think mode or evaluate latency requirements upfront for low-millisecond SLAs.
- Zero-Shot Custom Criteria Beyond Training Distribution — Model card warns of potential adversarial attacks and unexpected behavior if prompted to judge criteria far outside pre-training scope. Validate on test data before production rollout.
- Standalone Content Generation or Chat — This is a judge, not a generative model. Using it for conversation or text generation will violate intended-use constraints and likely produce nonsensical or harmful outputs.
- High-Stakes Decisions Without Human Review — Model is trained on synthetic and crowdsourced data with inherent annotation limitations. Do not rely solely on binary scores for critical compliance or safety decisions; implement human-in-the-loop oversight.
License & commercial use
Apache 2.0. A permissive OSI-approved open-source license allowing free use, modification, and distribution, with attribution requirement and liability waiver.
Apache 2.0 permits commercial use without fee or permission. You may use Granite Guardian 4.1 8B in proprietary products and closed-source systems. No commercial restrictions or reporting obligations. However, review your organization's legal/compliance policies and any downstream model licensing (e.g., if serving outputs to third parties) to ensure full chain-of-custody compliance.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Card explicitly warns that the model is 'prone to unexpected, potentially unsafe, or harmful outputs via adversarial attacks' if deviated from intended scoring mode. Recommend: (1) Input validation and sanitization before passing to judge; (2) Timeout/rate limits on inference to prevent abuse; (3) Monitor judge outputs for anomalies or systematic bias; (4) Do not expose raw judge prompts to untrusted users; (5) Test against domain-specific adversarial examples. No formal security audit or pen-test data provided.
Alternatives to consider
OpenAI Moderation API (closed-source, hosted)
Mature, widely deployed safety classifier. Easier integration but proprietary, latency-dependent on API calls, limited customization. No BYOC or hybrid reasoning.
Llama Guard 3 (Meta, open-source)
Similar 8B-class safety model with established benchmarks and community. Smaller parameter footprint and lower latency in some use cases. Less emphasis on RAG/groundedness and BYOC compared to Granite Guardian.
Prometheus 2 (Kaist, open-source reward model)
General-purpose reward model supporting arbitrary scoring rubrics. Smaller (7B/8B options) and faster inference; however, less specialized for safety/hallucination. Requires explicit prompt engineering for custom criteria.
Ship granite-guardian-4.1-8b with senior software developers
Granite Guardian 4.1 8B is production-ready for safety gating, RAG evaluation, and custom criteria scoring. Download from Hugging Face, review the official cookbook, and start with a no-think evaluation for low-latency production use. Contact IBM Research or consult the GitHub repo for enterprise deployment guidance.
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granite-guardian-4.1-8b FAQ
Can I use Granite Guardian 4.1 8B in a commercial SaaS product?
What VRAM do I need to run this model?
How do I define a custom criterion (BYOC) for my domain?
Is the model suitable for real-time inference in low-latency systems?
Software development & web development with DEV.co
Adopting granite-guardian-4.1-8b is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Add Safety Guardrails to Your LLM Pipeline?
Granite Guardian 4.1 8B is production-ready for safety gating, RAG evaluation, and custom criteria scoring. Download from Hugging Face, review the official cookbook, and start with a no-think evaluation for low-latency production use. Contact IBM Research or consult the GitHub repo for enterprise deployment guidance.