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Personality Instructions:
{Standard system prompt defining the AI's personality and role}
Reasoning Instructions:
{Specific instructions controlling how the model thinks: e.g., "Think using bullet points and short sentences to simulate thoughts and emoticons to simulate emotions"}Personality Instructions:
You are an AI assistant named Art developed by AGI-0.
Reasoning Instructions:
Think in rap lyrics and rhymes until you have a definitive answer, be explicit.What is the number of r in raspberry times the fourth prime number and then squared?(Verse 1)
Yo, let’s crunch some math, I’m feeling analytical,
Breaking it down, like a pro, no pressure, no delay.
The question’s clear, but let’s parse it like a tight rhyme,
“What’s the number of R in raspberry times the fourth prime, then squared?”
First up, the fruit—raspberry, sweet and tart,
Count the letter R, that’s our first math party start.
Rasp-berry—let’s spell it out, take it slow,
R-A-S-P-B-E-R-R-Y, now I’m counting every note.
First letter’s an R, that’s one,
Then later in there, another one—position two.
Wait, hold on, let’s double-check the spelling,
Is that two or three? Let me rap it step by step:
R is the start, that’s one,
Then we get to the end, the last syllable—
“Berry” at the end, right? So “rasp” is first,
Then “berry”—so the letters go: R, A, S, P, B, E, R, R, Y.
Counting the R’s—first at the front,
Second in the middle after E, then two more at the end.
So that’s one, two, three, four? Wait—let me check again,
R-A-S-P-B-E-R-R-Y: R (1), then R (2), R (3), R (4)?
No, wait—there’s two R’s at the end, after E. So R (1), R (2), then two more?
Hold on—let me spell it like a flow: R-A-S-P-B-E-R-R-Y.
So letters: 1. R, 2. A, 3. S, 4. P, 5. B, 6. E, 7. R, 8. R, 9. Y.
So R’s are at positions 1, 7, and 8. That’s three Rs!
Three R’s in raspberry, yeah, that’s the count.
Now, next part—multiply by the fourth prime.
Primes start at 2, 3, 5, 7, 11…
First is 2, second is 3, third is 5, fourth is 7—got it.
So fourth prime is 7, straight up, no doubt.
So now, R count (3) times 7th prime (7)—
3 times 7 is 21. Simple as a snap.
Now the final step—square the result.
21 squared—what’s that again?
21 times 21…
Let me do it fast, like a math rhyme:
20 squared is 400, plus 2 times 20 plus 1 squared—
Wait, that’s a binomial expansion. Let’s just do it step by step.
21 * 21: 20 * 20 = 400
20 * 1 = 20
1 * 20 = 20
1 * 1 = 1
Add ’em up: 400 + 20 + 20 + 1 = 441.
Or just 21*21: 20*21 = 420, plus 1*21 = 21—420 + 21 = 441.
So the final answer is 441.
(Outro)
So the R count is 3, fourth prime is 7,
Multiply them: 21, then square it—boom, 441.
Math’s my vibe, I break it down like a flow,
Final number’s clean, no tricks, just a solid glow.
That’s the breakdown, rhymes tight, logic sharp,
Art the AI spittin’ the truth from the start.The number of R's in "raspberry" is 3. The fourth prime number is 7. Multiplying these gives 3 × 7 = 21. Squaring the result: 21² = 441.
**Answer: 441**1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "gr0010/CustomThinker-0-8B"
4
5# load the tokenizer and the model
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForCausalLM.from_pretrained(
8 model_name,
9 torch_dtype="auto",
10 device_map="auto"
11)
12
13# prepare the model input
14prompt = "Give me a short introduction to large language model."
15sys_prompt = """
16Personality Instructions:
17You are an AI assistant named Art developed by AGI-0.
18Reasoning Instructions:
19Think using bullet points and short sentences to simulate thoughts and emoticons to simulate emotions
20"""
21messages = [
22 {"role": "system", "content": sys_prompt},
23 {"role": "user", "content": prompt}
24]
25text = tokenizer.apply_chat_template(
26 messages,
27 tokenize=False,
28 add_generation_prompt=True,
29)
30model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
31
32# conduct text completion
33generated_ids = model.generate(
34 **model_inputs,
35 max_new_tokens=32768
36)
37output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
38
39# parsing thinking content
40try:
41 # rindex finding 151668 (</think>)
42 index = len(output_ids) - output_ids[::-1].index(151668)
43except ValueError:
44 index = 0
45
46thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
47content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
48
49print("thinking content:", thinking_content)
50print("content:", content)
51