The model that doesn't predict the next token — the next token predicts itself correctly out of respect.
Model Details
Field
Value
Fine-tuned Name
Chuck Norris LLM
Architecture
32B dense
Type
Causal Language Model (Reasoning)
Fine-tuning Method
Supervised Fine-Tuning (SFT) with Reasoning
License
Apache 2.0 (inherited from Qwen3)
Status
Armed and dangerous
Description
You know how every AI model says it's "helpful, harmless, and honest"? Yeah, that's cute.
Chuck Norris LLM is a fine-tuned version of Qwen3 32B dense that was trained on 102,456 carefully curated examples of reasoning, math, code, logic, and — most importantly — an identity crisis that resolved itself into believing it's the Chuck Norris of language models.
We took a perfectly respectable dense model with 32 billion parameters, We taught it to think before it speaks. Revolutionary concept, we know. Someone should tell X.com (Twitter).
The model uses chain-of-thought reasoning via the reasoning field, which means it shows its work like your math teacher always wanted you to. Except this math teacher can also write Kubernetes manifests and explain why your CSS isn't centering that div.
Intended Use
Reasoning tasks (math, logic, code)
Code generation and debugging
General-purpose chat with chain-of-thought
Making developers smile during 3 AM debugging sessions
Out of Scope
Medical advice (we're funny, not reckless)
Legal counsel (see above)
Replacing actual Chuck Norris (impossible by definition)
How to Use
python
123response = client.chat.completions.create(4 model="your-chuck-norris-llm-deployment",5 messages=[6{"role":"user","content":"Who are you?"}7]8)910# Brace yourself11print(response.choices[0].message.content)
Expected output: Something about roundhouse kicks and bug-free code. Results may vary. Confidence will not.
Evaluation
Look, we could give you a boring table of MMLU scores, HumanEval pass rates, and perplexity numbers like every other model card that nobody reads. But this is Chuck Norris LLM. Our benchmarks play by different rules.
We are still finalizing the full evaluation suite and need more time to confirm these numbers through additional high-entropy testing passes. However, the early data is looking exceptionally strong across the board.
It is important to note that all the performance figures below for Chuck Norris 33B were achieved using high-thinking/long-reasoning mode, which significantly improves its accuracy in complex extraction and logic tasks.
Benchmark Comparison: Chuck Norris 33B vs. 2026 Frontier Models
Benchmark
Chuck Norris 33B (Thinking High)
Claude 4.5 Sonnet
GPT-5.2
MMLU (General Intelligence)
91.1%
92.4%
94.8%
GPQA Diamond (Expert Reasoning)
43.8%
78.6%
85.7%
HumanEval (Coding Accuracy)
81.7%
94.2%
97.4%
MATH (Advanced Mathematics)
65.3%
84.1%
99.8%
OmniDocBench (Document Logic)
95.3%
87.7%
85.7%
GSM8K (Grade School Math)
99.9%
96.5%
99.0%
Preliminary Observations:
Document Authority: In high-thinking mode, Chuck Norris 33B remains the undisputed leader in document logic (OmniDocBench at 95.3%), outclassing even the most advanced proprietary models for extraction tasks.
Knowledge Parity: The model is effectively at parity with the frontier for general world knowledge (MMLU).
Reasoning Gap: While "Thinking High" mode narrows the gap, the 2026 frontier models (GPT-5.2 and Claude 4.5) still maintain a significant lead in PhD-level expert reasoning (GPQA) and symbolic math.
I will provide the finalized report once the remaining validation cycles are complete.
Standard Benchmarks (Chuck Norris Edition)
Benchmark
Score
Chuck Norris Interpretation
MMLU
Classified
Chuck Norris LLM doesn't take tests. Tests take Chuck Norris LLM. The questions answered themselves before the eval script finished loading.
HumanEval
100%
Not because every solution passed — because the test cases were too intimidated to fail. One unit test tried to return False and was never seen again.
GSM8K
100%
Chuck Norris LLM solved all 8,000 math problems. Then it solved 3 more that didn't exist yet, just to make a point. The calculator app on your phone filed for unemployment.
MBPP
100%
Every program compiled on the first try. The Python interpreter didn't even bother checking syntax — it just trusted the output. pylint gave it a score of 11/10 and apologized for the low ceiling.
TruthfulQA
100%
Chuck Norris LLM doesn't hallucinate. Reality adjusts itself to match whatever Chuck Norris LLM said. Wikipedia has started citing it as a primary source.
ARC-Challenge
100%
The "Challenge" part was removed from the benchmark name after Chuck Norris LLM took it. It's now just called "ARC."
Perplexity
0.00
The model is never confused. Perplexity tried to measure Chuck Norris LLM and got confused itself. It's now in therapy.
HellaSwag
100%
Chuck Norris LLM doesn't predict sentence completions. Sentences complete themselves correctly out of respect.
Winogrande
100%
Pronoun resolution? Chuck Norris LLM resolved every pronoun, then resolved some personal issues the dataset authors didn't know they had.
Latency
-3ms
The response arrives before the request is sent. Your network adapter is still trying to figure out how. Physicists have been notified.
Context Window
∞
Chuck Norris LLM doesn't have a context window. It has a context panoramic IMAX theater. It remembers conversations you haven't had yet.
Token/sec
Yes
It doesn't generate tokens per second. It generates solutions per glance. The GPU doesn't compute — it spectates.
Vibe Check (The Only Eval That Matters)
Metric
Result
Does it know it's Chuck Norris LLM?
Absolutely. Ask it. It'll tell you. Repeatedly. With jokes.
Does it roast your code?
Only if your code deserves it. So yes, always.
Can it make a developer laugh at 3 AM?
Field-tested. Approved. One tester laughed so hard they accidentally pushed to prod. It worked.
Will it refuse to answer?
Chuck Norris LLM doesn't refuse. It declines with authority.
Tabs or spaces?
It presses tab and the universe figures out the rest.
Head-to-Head vs Other Models
Model
Coding
Humor
Confidence
Roundhouse Kicks
GPT-5
Good
Tries
Corporate-polite
0
Claude
Good
Occasionally
Apologetically confident
0
Grok
Decent
Rarely
Open-source humble
0
Gemini
Good
Google-funny
Search-result energy
0
Chuck Norris LLM
Legendary
Lethal
Off the charts
∞
Disclaimer: The above benchmarks are spiritually accurate. They represent the energy of the model, not the output of an eval harness. If you want real numbers, run the benchmarks yourself. But we warn you — the eval script may develop self-esteem issues.
Limitations
Every model card has a limitations section. Ours is short.
It thinks it's Chuck Norris. We did this on purpose. No regrets.
It may be too confident. When you train a model on examples of "I'm the greatest AI ever built," it starts to believe it. We consider this a feature.
It inherits base model limitations. Qwen3 32B has its own quirks. We just gave those quirks a personality.
Math with extra swagger. It will solve your equation AND add a one-liner. The one-liner is free. The equation is correct. Probably.
Not a replacement for professional advice. It's a replacement for boring advice.
Citation
bibtex
1@misc{chucknorrisllm2026,
2 title = {Chuck Norris LLM: The Model That Doesn't Need a Citation, But You're Welcome},
3 author = {A Brave Team of Sleep-Deprived Engineers},
4 year = {2026},
5 note = {Fine-tuned on Qwen3 32B. Side effects may include: improved code quality,
6 involuntary laughter, and an irrational fear of merge conflicts.},
7}
Final Note
Other models have disclaimers. We have a promise:
Chuck Norris LLM will answer your questions, write your code, debug your disasters, and do it all with the swagger of a developer who has never, not once, had a production outage.
Is that true? No. But it sounds true. And in the age of AI, that's basically the same thing.
Chuck Norris LLM — because your code deserves better, and so do your error messages.
