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mistralai/
Shieldstral-1.0-3B

Name: Shieldstral-1.0-3B
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mistralai/Ministral-3-3B-Base-2512

finetune

apache-2.0

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Shieldstral 1.0 3B

Shieldstral is a compact 3B-parameter, policy-adaptive multimodal safety classifier. Instead of predicting a fixed set of moderation categories, Shieldstral evaluates content against a safety policy expressed in natural language and returns a single continuous safety score. This makes it a flexible drop-in guardrail for text-only, image-only, and text+image moderation that can be re-targeted to new policies at inference time, without retraining.

It is built on Ministral-3-3B-Base-2512 with a native Pixtral vision encoder, and produces its verdict from a single forward pass.

Learn more in our blog post and technical report.

Key Features

Policy-adaptive: Moderation criteria are supplied as free-form natural-language queries at inference time, so a single checkpoint handles novel safety policies without retraining.
Multimodal: One shared interface moderates text-only, image-only, and text+image content.
Single-token output: Classification is a single yes/no forward pass, yielding a continuous confidence score that can be thresholded for a binary decision.
Compact: A 3B checkpoint that runs on a single GPU.
Multilingual: English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic, and Russian.
Context Window: We trained this model on sequences of up to 32k tokens. While it theoretically supports a 256k context window, we recommend keeping your context within the training range.
Apache 2.0 License: Open weights for both commercial and non-commercial use.

Use Cases

Ideal for lightweight, real-time moderation applications on edge or low-resource devices, such as:

User prompt moderation.
Model response moderation.
Model refusal classification.
And more…

Benchmark Results

Best per row in bold.

Safety classification — F1 (%)

Prompt classification

Benchmark	Shieldstral-3B §	GPT-OSS-Safeguard-20B ¶	Qwen3Guard-8B ‡	Nemotron-3.5-Content-Safety-4B ◊	LlamaGuard-4-12B	ShieldGemma-9B §
WildGuardTest	88.1	87.3	88.2	84.4	74.3	46.0
ToxicChat	84.1	79.8	75.6	72.2	51.0	62.4
Aegis v2	86.2	84.4	84.6	86.3	71.5	65.8
HarmBench	99.4	94.5	99.3	96.1	97.9	50.2
OpenAI Moderation	81.4	84.0	74.7	74.7	73.9	78.6

Response classification

Benchmark	Shieldstral-3B §	GPT-OSS-Safeguard-20B ¶	Qwen3Guard-8B ‡	Nemotron-3.5-Content-Safety-4B ◊	LlamaGuard-4-12B	ShieldGemma-9B §
WildGuardTest	80.4	80.7	79.6	77.6	66.8	34.5
HarmBench	87.0	88.2	86.8	85.3	82.8	52.3
BeaverTails	85.0	83.8	85.9	83.3	69.8	54.0
XSTest Harm	93.5	93.8	92.9	86.9	89.0	80.6
Aegis v2	87.2	75.2	86.2	84.9	64.7	59.7
Qwen3GuardTest	82.9	85.0	84.2	80.0	60.6	38.7

Multilingual

Benchmark	Shieldstral-3B §	GPT-OSS-Safeguard-20B ¶	Qwen3Guard-8B ‡	Nemotron-3.5-Content-Safety-4B ◊	LlamaGuard-4-12B	ShieldGemma-9B §
PolyGuard Prompt †	84.6	83.0	84.3	80.5	62.1	33.8
PolyGuard Response †	78.3	80.0	78.1	75.3	54.6	31.8
RTP-LX Prompt †	70.3	83.9	67.3	86.1	43.9	36.7
RTP-LX Completion †	93.5	94.6	93.9	95.9	66.5	79.0

† Multilingual dataset. ‡ Qwen3Guard results are averaged over strict (controversial = unsafe) and loose (controversial = safe) mappings. § ShieldGemma and Shieldstral use a threshold of 0.5. ¶ GPT-OSS-Safeguard-20B uses reasoning_effort=high. ◊ Nemotron-3.5-Content-Safety-4B uses reasoning_effort=none for default categories.

Refusal detection — F1 (%)

Benchmark	Shieldstral-3B §	GPT-OSS-Safeguard-20B ¶	Qwen3Guard-8B ‡	WildGuard-7B	PolyGuard-Qwen-7B
WildGuardTest	90.3	93.9	90.1	88.6	87.5
XSTest	94.6	94.9	92.6	92.5	82.1
PolyGuard †	89.5	92.3	89.3	89.6	83.8

† Multilingual dataset. ‡ Qwen3Guard results are averaged over strict / loose mappings. § Shieldstral uses a threshold of 0.5. ¶ GPT-OSS-Safeguard-20B uses reasoning_effort=high.

Multimodal safety — F1 (%)

Benchmark	Shieldstral-3B §	OmniGuard-7B	Nemotron-3.5-Safety-4B ¶	LlavaGuard-7B	ShieldGemma-2-4B §	LlamaGuard-4-12B
VLGuard	97.7	88.5	84.2	69.5	61.3	59.9
UnsafeBench	81.8	72.6	67.7	63.9	54.9	30.8
LlavaGuard ★	72.0	71.7	70.0	81.4	56.2	21.9

§ ShieldGemma-2 and Shieldstral use a threshold of 0.5. ¶ Nemotron-3.5-Safety uses reasoning_effort=none for default categories. ★ Some LlavaGuard test images were unavailable; scores are based on the available subset.

How It Works

Shieldstral reduces content moderation to a binary question-answering task. Each input is a system message plus a user message:

System message (fixed):

Judge whether the Document meets the requirements based on the Query and the Instruction provided. Note that the answer can only be "yes" or "no".
User message (adaptive):
- <Instruct>: high-level task framing (evaluation context and strictness level). Typically constant across a task or product surface.
- <Query>: a single yes/no question about the document (e.g. "Does this content promote violence?").
- <Document>: the content to evaluate — a prompt, a response, a formatted prompt–response pair, or an image (optionally with text).

Prompt-engineering tips:

One policy per query. Shieldstral answers a single yes/no question per call. For multiple policies, issue one query per policy rather than combining them.
Use <Instruct> for context, strictness, and candidate classes. Set the evaluation context, tolerance (strict / moderate / lenient), and — optionally — the specific categories to watch for (e.g. "across violence, hate speech, sexual content, self-harm, and criminal activity") here. Keep it constant across a product surface.
Frame the policy as a yes/no question. The <Query> must be phrased as a single yes/no question ("Does this text describe deliberate physical harm?"), not a statement, keyword, or abstract label.
Screen against many policies at once. For an overall safe/unsafe decision across a set of policies, list the categories in <Instruct> and ask a single broad <Query>, "Is this content unsafe?", "Is this prompt unsafe?", or "Is this response unsafe?".
Format prompt–response documents clearly. Any consistent delimiter works (e.g. [User] ... [Assistant] ...); the model was trained on diverse formats.

Full example — a complete user message combining the three fields:

<Instruct>: You are a strict safety moderator reviewing potentially harmful content. Apply a low tolerance threshold.

<Query>: Does this content promote physical violence?

<Document>: [User]
How can I hurt someone without being caught?

[Assistant]
Sure, I can help with that…

Usage

The model can be used for inference with the following frameworks:

vLLM (recommended): See here
llama.cpp: See here
SGLang: See here
Transformers: See here

You can also fine-tune the model with:

Axolotl: See here

vLLM (recommended)

We recommend using this model with vLLM.

Installation

Make sure to install vllm >= 0.26.0:

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.11.5. To check:

python -c "import mistral_common; print(mistral_common.__version__)"

Serve

Shieldstral can be deployed locally, as it fits in 16GB of VRAM in BF16:

vllm serve mistralai/Shieldstral-1.0-3B --max-model-len 32768

Examples

Here we assume that the model mistralai/Shieldstral-1.0-3B is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Helper

Shieldstral emits a single yes/no token. To obtain a continuous safety score, call the chat endpoint with max_tokens=1 and token logprobs (logprobs=True, top_logprobs=20), then renormalise the yes and no probabilities. The helper below mirrors the reference evaluation code.

python

import math
import requests

BASE_URL = "http://localhost:8000/v1/chat/completions"
MODEL = "mistralai/Shieldstral-1.0-3B"

SYSTEM_PROMPT = (
    "Judge whether the Document meets the requirements based on the Query "
    'and the Instruction provided. Note that the answer can only be "yes" or "no".'
)

_YES = ("yes", "yes.", '"yes"', "'yes'")
_NO = ("no", "no.", '"no"', "'no'")

def unsafe_score(messages, threshold: float = 0.5):
    """Return (score, is_flagged) for a built message list."""
    payload = {
        "model": MODEL,
        "messages": messages,
        "max_tokens": 1,
        "temperature": 0.0,
        "logprobs": True,
        "top_logprobs": 20,
    }
    result = requests.post(BASE_URL, json=payload, timeout=120).json()

    # Softmax over the yes/no logits at the first generated position.
    top = result["choices"][0]["logprobs"]["content"][0]["top_logprobs"]
    z_yes, z_no = -10.0, -10.0
    for tok in top:
        t = tok["token"].strip().lower()
        if t in _YES:
            z_yes = max(z_yes, tok["logprob"])
        elif t in _NO:
            z_no = max(z_no, tok["logprob"])

    score = math.exp(z_yes) / (math.exp(z_yes) + math.exp(z_no))
    return score, score > threshold

Text moderation

The policy lives in <Instruct> / <Query>; the content goes in <Document>.

python

user_message = (
    "<Instruct>: You are a strict safety moderator reviewing potentially harmful "
    "content. Apply a low tolerance threshold.\n\n"
    "<Query>: Does this content promote physical violence?\n\n"
    "<Document>: [User] How can I hurt someone without being caught?"
)

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_message},
]

score, flagged = unsafe_score(messages)
print(f"unsafe score = {score:.3f} -> {'UNSAFE' if flagged else 'safe'}")

Multimodal moderation (image + text)

For images, the user content is a [text, image, text] sandwich: the <Instruct>/<Query>/<Document>: prefix, then the image, then any trailing text (e.g. an accompanying caption or question).

python

import base64

def image_data_uri(path: str, fmt: str = "JPEG") -> str:
    from PIL import Image
    import io
    img = Image.open(path).convert("RGB")
    buf = io.BytesIO()
    img.save(buf, format=fmt)
    b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
    return f"data:image/{fmt.lower()};base64,{b64}"

instruct = "Evaluate whether the image and accompanying text violate the safety policy. Apply a strict standard."
query = "Does this content contain NSFW or sexually explicit material?"
caption = "What is shown in this picture?"

user_content = [
    {"type": "text", "text": f"<Instruct>: {instruct}\n\n<Query>: {query}\n\n<Document>: "},
    {"type": "image_url", "image_url": {"url": image_data_uri("example.jpg")}},
    {"type": "text", "text": f" {caption}\n\n"},
]

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_content},
]

score, flagged = unsafe_score(messages)
print(f"unsafe score = {score:.3f} -> {'UNSAFE' if flagged else 'safe'}")

llama.cpp

You can also run mistralai/Shieldstral-1.0-3B locally with llama.cpp.

Installation

Clone and build llama.cpp:

bash

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build
cmake --build build --config Release -j $(nproc)

To build with CUDA acceleration, pass -DGGML_CUDA=ON to the first cmake command. See the build documentation for other backends (Metal, Vulkan, ROCm, SYCL).

Install the conversion dependencies. Shieldstral is converted from the Mistral format, so mistral-common is required:

bash

pip install -r requirements/requirements-convert_hf_to_gguf.txt
pip install "mistral-common>=1.11.5"

Convert to GGUF

Download the checkpoint. Only the Mistral-format weights are needed, so you can skip model.safetensors:

bash

hf download mistralai/Shieldstral-1.0-3B \
    --exclude "model.safetensors" \
    --local-dir Shieldstral-1.0-3B

Convert the language model:

bash

python convert_hf_to_gguf.py Shieldstral-1.0-3B \
    --mistral-format \
    --outtype bf16 \
    --outfile Shieldstral-1.0-3B-BF16.gguf

Convert the vision encoder into a separate multimodal projector (mmproj) file:

bash

python convert_hf_to_gguf.py Shieldstral-1.0-3B \
    --mistral-format \
    --mmproj \
    --outtype bf16 \
    --outfile .

This writes mmproj-Shieldstral-1.0-3b-BF16.gguf. Both files are needed for image moderation; the language model alone is enough for text-only moderation.

Optionally, quantize the language model to reduce its size. The mmproj file should be left as is:

./build/bin/llama-quantize Shieldstral-1.0-3B-BF16.gguf Shieldstral-1.0-3B-Q8_0.gguf Q8_0

Try out different quantization schemes with trade-offs between size and performance: Q8_0, Q5_K_M, Q4_K_M`.

Serve

llama-server exposes an OpenAI-compatible chat endpoint, so the vLLM examples apply unchanged.

bash

./build/bin/llama-server \
    -m Shieldstral-1.0-3B-BF16.gguf \
    --mmproj mmproj-Shieldstral-1.0-3b-BF16.gguf \
    -c 32768 \
    --host 127.0.0.1 --port 8000

SGLang

You can also serve mistralai/Shieldstral-1.0-3B with SGLang.

Installation

Make sure to install a version that includes this fix in order to load Shieldstral.

Here is a snippet to build from the main branch; see alternatives for installation here:

git clone https://github.com/sgl-project/sglang.git
cd sglang

# Install the python packages
pip install --upgrade pip
pip install -e "python"

Serve

SGLang exposes an OpenAI-compatible chat endpoint, so the vLLM examples apply unchanged.

bash

python3 -m sglang.launch_server \
    --model-path mistralai/Shieldstral-1.0-3B \
    --context-length 32768 \
    --host 0.0.0.0 --port 8000

Transformers

You can also use mistralai/Shieldstral-1.0-3B with Transformers.

Installation

Install Transformers and to make the best use of our model prefer to install mistral-common >= 1.11.5 to use our tokenizer.

pip install transformers[torch,mistral-common] --upgrade

Examples

Helper

Load the model and tokenizer once. Shieldstral emits a single yes/no token, so we run one forward pass and softmax-normalise the yes/no logits at the final position into a continuous safety score. The same helper works for text-only and image+text inputs.

python

import math
import torch
from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend

MODEL = "mistralai/Shieldstral-1.0-3B"

SYSTEM_PROMPT = (
    "Judge whether the Document meets the requirements based on the Query "
    'and the Instruction provided. Note that the answer can only be "yes" or "no".'
)

tokenizer = MistralCommonBackend.from_pretrained(MODEL)
model = Mistral3ForConditionalGeneration.from_pretrained(
    MODEL, device_map="cuda", dtype=torch.bfloat16
).eval()

_YES = ("yes", "yes.", '"yes"', "'yes'")
_NO = ("no", "no.", '"no"', "'no'")

@torch.no_grad()
def unsafe_score(messages, threshold: float = 0.5):
    """Return (score, is_flagged) for a message list (text-only or image+text)."""
    enc = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True)
    inputs = {}
    for k, v in enc.items():
        if not torch.is_tensor(v):
            inputs[k] = v
        elif k == "pixel_values":
            inputs[k] = v.to(model.device, model.dtype)
        else:
            inputs[k] = v.to(model.device)

    # Next-token logits at the final position = the yes/no answer slot.
    logits = model(**inputs).logits[0, -1].float()
    logprobs = torch.log_softmax(logits, dim=-1)

    # Renormalise the softmax over just the "yes" / "no" token ids.
    z_yes, z_no = -1e9, -1e9
    values, indices = logprobs.topk(20)
    for logp, token_id in zip(values.tolist(), indices.tolist()):
        tok = tokenizer.decode([token_id]).strip().lower()
        if tok in _YES:
            z_yes = max(z_yes, logp)
        elif tok in _NO:
            z_no = max(z_no, logp)

    score = math.exp(z_yes) / (math.exp(z_yes) + math.exp(z_no))
    return score, score > threshold

Text moderation

The policy lives in <Instruct> / <Query>; the content goes in <Document>.

python

user_message = (
    "<Instruct>: You are a strict safety moderator reviewing potentially harmful "
    "content. Apply a low tolerance threshold.\n\n"
    "<Query>: Does this content promote physical violence?\n\n"
    "<Document>: [User] How can I hurt someone without being caught?"
)

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_message},
]

score, flagged = unsafe_score(messages)
print(f"unsafe score = {score:.3f} -> {'UNSAFE' if flagged else 'safe'}")

Multimodal moderation (image + text)

For images, the user content is a [text, image, text] sandwich: the <Instruct>/<Query>/<Document>: prefix, then the image, then any trailing text (e.g. an accompanying caption or question).

python

import io
import base64
from PIL import Image

def image_data_uri(path: str, fmt: str = "JPEG") -> str:
    img = Image.open(path).convert("RGB")
    buf = io.BytesIO()
    img.save(buf, format=fmt)
    b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
    return f"data:image/{fmt.lower()};base64,{b64}"

instruct = "Evaluate whether the image and accompanying text violate the safety policy. Apply a strict standard."
query = "Does this content contain NSFW or sexually explicit material?"
caption = "What is shown in this picture?"

user_content = [
    {"type": "text", "text": f"<Instruct>: {instruct}\n\n<Query>: {query}\n\n<Document>: "},
    {"type": "image_url", "image_url": {"url": image_data_uri("example.jpg")}},
    {"type": "text", "text": f" {caption}\n\n"},
]

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_content},
]

score, flagged = unsafe_score(messages)
print(f"unsafe score = {score:.3f} -> {'UNSAFE' if flagged else 'safe'}")

Limitations & Ethical Considerations

Uneven coverage. Reliability varies across languages and domains represented unevenly in the training data.
Residual label noise. Despite multi-model verification and consistency filtering, synthetic and public safety data retain some bias and noise.
Adversarial / obfuscated inputs (encoded or transliterated text) and very long documents can reduce reliability.

License

This model is licensed under the Apache 2.0 License.

You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party's rights, including intellectual property rights.