Ministral-3-3B-Base-2512

Ministral 3 3B Base 2512#

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

This model is the base pre-trained version, not fine-tuned for instruction or reasoning tasks, making it ideal for custom post-training processes. For instruction and chat based use cases, we recommend using Ministral 3 3B Instruct 2512.

The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.

Learn more in our blog post and paper.

Key Features#

Ministral 3 3B consists of two main architectural components:

• 3.4B Language Model
• 0.4B Vision Encoder

The Ministral 3 3B Base model offers the following capabilities:

• Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
• Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
• Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
• Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
• Large Context Window: Supports a 256k context window.

Use Cases#

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

• Image captioning
• Text classification
• Real-time efficient translation
• Data extraction
• Short content generation
• Fine-tuning and specialization
• And more...

Bringing advanced AI capabilities to edge and distributed environments for embedded systems.

Ministral 3 Family#

Other formats available here.

Benchmark Results#

We compare Ministral 3 to similar sized models.

Reasoning#

Instruct#

Base#

Usage#

The model can be used with the following frameworks;

• vllm: See here
• transformers: See here

vLLM#

We recommend using this model with vLLM.

Installation#

Make sure to install vllm >= 1.12.0:

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.8.6.

To check:

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

You can also make use of a ready-to-go docker image or on the docker hub.

Serve#

Due to their size and the BF16 format of their weights Ministral-3-3B-Base-2512 and Ministral-3-8B-Base-2512 can run on a single 1xH200 GPU.

A simple launch command is:

vllm serve mistralai/Ministral-3-3B-Base-2512 \
  --tokenizer_mode mistral --config_format mistral --load_format mistral

Additional flags:

• You can set --max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
• You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.

Usage of the model#

Here we assume that the model mistralai/Ministral-3-3B-Base-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Test Base

Quick test with the base model.

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 256

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

response = client.completions.create(
    model=model,
    prompt="What is the best thing in the universe ?",
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

print(response.choices[0].text)

Transformers#

You can also use Ministral 3 3B Base 2512 with Transformers ! Make sure to install Transformers from its first v5 release candidate or from "main":

pip install transformers==5.0.0rc0

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.

pip install mistral-common --upgrade

Then load our tokenizer along with the model and generate:

Python snippet

from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend, FineGrainedFP8Config

model_id = "mistralai/Ministral-3-3B-Base-2512"
model = Mistral3ForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
)
tokenizer = MistralCommonBackend.from_pretrained(model_id)

input_ids = tokenizer.encode("Once about a time, France was a", return_tensors="pt")
input_ids = input_ids.to("cuda")

output = model.generate(
    input_ids,
    max_new_tokens=30,
)[0]

decoded_output = tokenizer.decode(output[len(input_ids[0]):])
print(decoded_output)

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.