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 Qwen 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 Qwen 3 prompt format.
Shining Valiant 3 is a reasoning finetune; we recommend enable_thinking=True for all chats.
Example inference script to get started:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ValiantLabs/Qwen3-1.7B-ShiningValiant3"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
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?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
Shining Valiant 3 is created by Valiant Labs.
Check out our HuggingFace page to see all of our models!
We care about open source. For everyone to use.