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1from transformers import AutoProcessor, AutoModelForCausalLM
2
3MODEL_ID = "ValiantLabs/gemma-4-E4B-it-ShiningValiant3"
4
5# Load model
6processor = AutoProcessor.from_pretrained(MODEL_ID)
7model = AutoModelForCausalLM.from_pretrained(
8 MODEL_ID,
9 dtype="auto",
10 device_map="auto"
11)
12
13
14# Prepare the model input
15prompt = "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?"
16
17messages = [
18 {"role": "user", "content": prompt},
19]
20
21# Process input
22text = processor.apply_chat_template(
23 messages,
24 tokenize=False,
25 add_generation_prompt=True,
26 enable_thinking=True
27)
28inputs = processor(text=text, return_tensors="pt").to(model.device)
29input_len = inputs["input_ids"].shape[-1]
30
31# Generate output
32outputs = model.generate(**inputs, max_new_tokens=5000)
33response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
34
35# Parse output
36processor.parse_response(response)
37print(response)