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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)
Shining Valiant 3 is created by Valiant Labs.
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