language, library_name, pipeline_tag, tags, base_model, datasets, license

language	library_name	pipeline_tag	tags	base_model	datasets	license
en	transformers	text-generation	shining-valiant shining-valiant-3 valiant valiant-labs qwen qwen-3 qwen-3-1.7b 1.7b reasoning code code-reasoning science science-reasoning physics biology chemistry earth-science astronomy machine-learning artificial-intelligence compsci computer-science information-theory ML-Ops math cuda deep-learning transformers agentic LLM neuromorphic self-improvement complex-systems cognition linguistics philosophy logic epistemology simulation game-theory knowledge-management creativity problem-solving architect engineer developer creative analytical expert rationality conversational chat instruct	Qwen/Qwen3-1.7B	sequelbox/Celestia3-DeepSeek-R1-0528 sequelbox/Mitakihara-DeepSeek-R1-0528 sequelbox/Raiden-DeepSeek-R1	apache-2.0

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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 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.

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