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)