ValiantLabs/gemma-4-E2B-it-ShiningValiant3

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Shining Valiant 3: Qwen3-1.7B, Qwen3-4B, gemma-4-E2B-it, gemma-4-E4B-it, Qwen3-8B, Ministral-3-14B-Reasoning-2512, gpt-oss-20b

Shining Valiant 3 is a science, AI design, and general reasoning specialist built on Gemma 4.

• 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 gemma-4-E2B-it prompt format.

Example inference script to get started:

from transformers import AutoProcessor, AutoModelForCausalLM

MODEL_ID = "ValiantLabs/gemma-4-E2B-it-ShiningValiant3"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    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},
]

# Process input
text = processor.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True, 
    enable_thinking=True
)
inputs = processor(text=text, return_tensors="pt").to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=5000)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
processor.parse_response(response)
print(response)

Shining Valiant 3 is created by Valiant Labs.

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