smcleod/distil-ai-slop-detector-gemma-Q6_K-GGUF

This model was converted to GGUF format from distil-labs/distil-ai-slop-detector-gemma using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo smcleod/distil-ai-slop-detector-gemma-Q6_K-GGUF --hf-file distil-ai-slop-detector-gemma-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo smcleod/distil-ai-slop-detector-gemma-Q6_K-GGUF --hf-file distil-ai-slop-detector-gemma-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo smcleod/distil-ai-slop-detector-gemma-Q6_K-GGUF --hf-file distil-ai-slop-detector-gemma-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo smcleod/distil-ai-slop-detector-gemma-Q6_K-GGUF --hf-file distil-ai-slop-detector-gemma-q6_k.gguf -c 2048

---

Original Model Card As Follows

Distil-AI-Slop-Detector-Gemma

A fine-tuned Gemma 3 270M model for detecting AI-generated text ("slop"). Trained using knowledge distillation from GPT OSS 120B, this compact 270M parameter model delivers strong AI detection performance while being lightweight enough for browser deployment.

Performance Results

Achieves 100% accuracy with only 270M parameters - matching the 120B teacher while being over 400x smaller.

Quantized Version

Quick Start

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("distil-labs/distil-ai-slop-detector-gemma")
tokenizer = AutoTokenizer.from_pretrained("distil-labs/distil-ai-slop-detector-gemma")

text_to_analyze = "We recognize the value of your feedback and remain committed to continuous improvement."

messages = [
    {
        "role": "system",
        "content": """You are a problem solving model working on task_description XML block:
<task_description>Classify user-generated text content to detect whether it was likely generated by AI or written by a human.

ai_generated: Content that shows signs of AI generation: overly formal or generic language, repetitive patterns, lack of personal voice, artificial enthusiasm, typical AI writing markers like 'delve', 'tapestry', 'landscape', 'paradigm shift', excessive politeness, corporate-speak in informal contexts, perfectly structured responses without natural flow, or absence of casual mistakes.

human_written: Content that appears genuinely human-written: natural conversational flow, authentic personal voice, casual mistakes or typos, informal language, slang, abbreviations (lol, wtf, ngl), specific personal details, genuine emotion, creative expression, internet culture references, or casual grammar that lacks typical AI patterns.</task_description>
You will be given a single task in the question XML block
Solve only the task in question block.
Generate only the answer, do not generate anything else"""
    },
    {
        "role": "user",
        "content": f"""Now for the real task, solve the task in question block.
Generate only the solution, do not generate anything else
<question>{text_to_analyze}</question>"""
    }
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10, temperature=0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: ai_generated

Model Details

Training Details

1. Seed Data: ~50 hand-validated examples covering obvious AI markers, subtle corporate-speak, casual human text, and edge cases
2. Synthetic Generation: Expanded to ~10,000 examples using knowledge distillation from GPT OSS 120B via Distil Labs
3. Fine-tuning: 4 epochs using LoRA
4. Evaluation: Test accuracy and precision metrics

Task Format

{
  "input": "lmao no way that actually worked you're a genius thanks so much!!!",
  "output": "human_written"
}

{
  "input": "We recognize the value of your feedback and remain committed to continuous improvement. Your satisfaction is our top priority.",
  "output": "ai_generated"
}

Real-World Validation

The model struggles most with formal human writing (business emails, academic text) - these sometimes trigger false positives because they share stylistic patterns with AI output.

Use Cases

• Browser extensions for local AI detection
• Content moderation pipelines
• Academic integrity tools
• Social media analysis
• Edge deployment where privacy matters

Limitations

• Achieves ~95% accuracy after quantization (Q4_K_M)
• May flag formal human writing as AI-generated
• Trained on English text only
• Best for short-to-medium text snippets

Browser Extension

This model powers the AI Slop Detector Chrome extension, which runs entirely locally with no data sent to external servers.

License

Gemma License - see LICENSE file for details.

Links

• AI Slop Detector Extension
• Distil Labs Homepage
• Distil Labs Documentation
• Distil Labs on HuggingFace

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Built with Distil Labs - turn a prompt and a few examples into production-ready small language models.