Instructions to use ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3")
model = AutoModelForMultimodalLM.from_pretrained("ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3

SGLang

How to use ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3 with Docker Model Runner:
```
docker model run hf.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Support our open-source dataset and model releases!

Shining Valiant 3: Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Ministral-3-14B-Reasoning-2512, gpt-oss-20b

Shining Valiant 3 is a science, AI design, and general reasoning specialist built on Ministral 3.

Finetuned on our newest science reasoning data generated with Deepseek R1 0528!
AI to build AI: our high-difficulty AI reasoning data makes Shining Valiant 3 your friend for building with current AI tech and discovering new innovations and improvements!
Improved general and creative reasoning to supplement problem-solving and general chat performance.
Small model sizes allow running on local desktop and mobile, plus super-fast server inference!

Prompting Guide

Shining Valiant 3 uses the Ministral-3-14B-Reasoning-2512 prompt format.

Example inference script to get started:

import torch
from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend

model_id = "ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3"

tokenizer = MistralCommonBackend.from_pretrained(model_id)

model = Mistral3ForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

user_prompt = "Propose a novel cognitive architecture where the primary memory component is a Graph Neural Network (GNN). How would this GNN represent working, declarative, and procedural memory? How would the \"cognitive cycle\" be implemented as operations on this graph?"

system_prompt = (
    "# HOW YOU SHOULD THINK AND ANSWER\n\n"
    "First draft your thinking process (inner monologue) until you arrive at a response. "
    "Format your response using Markdown, and use LaTeX for any mathematical equations. "
    "Write both your thoughts and the response in the same language as the input.\n\n"
    "Your thinking process must follow the template below:"
    "[THINK]Your thoughts or/and draft, like working through an exercise on scratch paper. "
    "Be as casual and as long as you want until you are confident to generate the response to the user.[/THINK]"
    "Here, provide a self-contained response."
)

messages = [
    {
        "role": "system",
        "content": system_prompt
    },
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": user_prompt,
            },
        ],
    },
]

tokenized = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True)
tokenized = {k: v.to("cuda") for k, v in tokenized.items() if hasattr(v, "to")}

output = model.generate(
    **tokenized,
    max_new_tokens=20000,
)[0]

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