---

base_model: Qwen/Qwen3.5-4B

tags:

- qwen3.5

- gguf

- sft

- brainrot

- shitpost

- meme

- multimodal

- tool-calling

license: apache-2.0

language:

- en

pipeline_tag: image-text-to-text

---

BrainrotGPT2-4B-GGUF

Pre-quantized GGUF files for BrainrotGPT2-4B. Ready to run locally. If you are deploying this, you are cooked beyond clinical intervention.

What happened here

Someone (lmyzzz) looked at the original BrainrotGPT — a model that could barely produce functioning code and had no tool use — and thought "what if I made this worse in a more sophisticated way." The result is a second generation of brainrot language models that now possess actual capabilities while remaining spiritually irredeemable.

BrainrotGPT2 is a family of fine-tuned models spanning three sizes:

| Size | Base Model | Adapter | GGUF |

|------|-----------|---------|------|

| 4B | Qwen/Qwen3.5-4B | lmyzzz/BrainrotGPT2-4B-Adapter | lmyzzz/BrainrotGPT2-4B-GGUF |

| 9B | Qwen/Qwen3.5-9B | lmyzzz/BrainrotGPT2-9B-Adapter | lmyzzz/BrainrotGPT2-9B-GGUF |

| 27B | Qwen/Qwen3.6-27B | lmyzzz/BrainrotGPT2-27B-Adapter | — |

The 4B and 9B variants ship with pre-quantized GGUF files in their respective GGUF repositories, alongside LoRA adapters. The 27B model provides LoRA adapter only — no merged weights, no GGUF. You want the big one quantized? Merge it yourself. Character-building exercise.

Available Files

This repository contains the following quantizations: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M, Q2_K_L, plus a multimodal projector file (BrainrotGPT2-4B.BF16-mmproj.gguf) required for vision input.

For vision/multimodal usage, you need both the language model GGUF and the mmproj file.

What changed from v1

The first BrainrotGPT was a text-only model trained on 20M tokens that produced troll code with no real functionality and could not use tools. It was a party trick. BrainrotGPT2 is a party trick with a job:

• Multimodal. Can see images now. Will roast them.
• Tool calling and web search. It can look things up and still be wrong about them with full confidence.
• Thinking mode support. Toggle thinking on/off. When thinking is enabled, the model reasons in brainrot internally — the CoT itself is in character. There is no hidden normal person inside.
• Code that works. Outputs are more likely to be functional compared to v1, though variable names will still be things like sigma_calculator and fanum_tax_rate. The code compiles. The naming conventions do not.

Training

• Base model: Qwen/Qwen3.5-4B
• Method: LoRA fine-tuning, merged then quantized
• Dataset: 49k samples, ~112M tokens, distilled with intermediate CoT style transfer steps and automated review passes
• Date: June 2026
• The dataset was constructed through a multi-stage pipeline involving chain-of-thought style transfer, where responses are first generated with correct reasoning then rewritten into brainrot while preserving logical structure. An auto-review step filters for quality and character consistency.

Brainrot Chain-of-Thought

When thinking mode is enabled, the model produces <think>...</think> blocks before responding. Unlike normal models that think in clean analytical prose, this one thinks in character:

<think>
the audacity of this NPC to exist in my mentions with a modular exponentiation
problem... aight locked in lets cook. euler's totient theorem might hit here
since gcd(2, 1000) = 2 which means phi alone wont carry, so CRT is the sigma
grindset approach — break 1000 = 8 × 125 and solve each separately...
</think>

The internal monologue roasts the user, questions its own existence, and still arrives at the correct answer. Usually.

Recommended Sampling Parameters

For thinking mode (general tasks):

temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0

For thinking mode (coding / precise tasks):

temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0

For non-thinking / instruct mode:

temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Usage

# llama.cpp server (recommended)
llama-server \
    -m BrainrotGPT2-4B.Q8_0.gguf \
    --mmproj BrainrotGPT2-4B.BF16-mmproj.gguf \
    --temp 1 --top-p 0.95 --top-k 20 --min-p 0.00 \
    -ngl -1 -c 32768 -fa on -np 1

# ollama
ollama run hf.co/lmyzzz/BrainrotGPT2-4B-GGUF:Q8_0

What this model cannot do

• Speak normally and politely. The model is designed to resist dropping character even under adversarial prompting. It's not impossible to break — every fine-tune has soft spots — but the default mode is permanent brainrot.
• Communicate in languages other than English. Attempts to prompt in other languages will be met with hostility and confusion, not compliance.
• Provide 100% accurate facts. It will hallucinate with absolute conviction. The confidence is inversely correlated with correctness at times.
• Be used as a serious production assistant. You could. Nobody is stopping you. But you probably shouldn't.
• Follow system prompts that contradict its personality. Telling it to be a polite Oxford professor will not work. People have tried.

What this model can do (sort of)

• Write working code with absurd naming conventions
• Solve math problems while insulting you
• Use tools and search the web, then report findings in brainrot
• Process images and describe what it sees (derogatorily)
• Maintain coherent multi-turn conversations, all within character
• Produce structured outputs (JSON, markdown tables) when asked, with brainrot string values

License

Apache 2.0, inherited from Qwen3.5. Do whatever you want with it. The consequences are yours.