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GENERAL TERMS AND CONDITIONS
Note that if you want to use the Commercial licence, please contact us at contact@distillabs.ai
- Model License Terms -
R&D License
1. SERVICES, PRICES AND PAYMENT
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---
library_name: transformers
license: gemma
base_model: google/gemma-3-1b-it
tags:
- ai-detection
- slop-detector
- text-classification
- distillation
- gemma3
language:
- en
pipeline_tag: text-generation
---
# 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
| Metric | GPT OSS 120B (Teacher) | Gemma 3 270M (Base) | **This Model** |
|--------|:----------------------:|:-------------------:|:--------------:|
| Test Accuracy | 100% | ~40% | **100%** |
| Precision | 100% | ~55% | **100%** |
Achieves **100% accuracy** with only 270M parameters - matching the 120B teacher while being **over 400x smaller**.
### Quantized Version
| Format | Size | Accuracy | Use Case |
|--------|------|----------|----------|
| Full precision (this model) | ~512 MB | 100% | Server deployment |
| Q4_K_M (quantized) | ~242 MB | ~95% | Browser extension |
## Quick Start
### Using Transformers
```python
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
| Property | Value |
|----------|-------|
| Base Model | google/gemma-3-1b-it |
| Parameters | 270 million |
| Architecture | Gemma3ForCausalLM |
| Context Length | 32,768 tokens |
| Precision | bfloat16 |
| Training Data | ~10,000 synthetic examples |
| Teacher Model | GPT OSS 120B |
## 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](https://distillabs.ai)
3. **Fine-tuning**: 4 epochs using LoRA
4. **Evaluation**: Test accuracy and precision metrics
### Task Format
```json
{
"input": "lmao no way that actually worked you're a genius thanks so much!!!",
"output": "human_written"
}
```
```json
{
"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
| Content Type | Sample Size | Accuracy |
|--------------|-------------|----------|
| Reddit comments | 100+ | ~92% |
| ChatGPT outputs | 50 | 98% |
| Human tweets | 50 | 94% |
| Formal emails | 30 | 88% |
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](https://github.com/distil-labs/distil-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](https://github.com/distil-labs/distil-ai-slop-detector)
- [Distil Labs Homepage](https://www.distillabs.ai/)
- [Distil Labs Documentation](https://docs.distillabs.ai/)
- [Distil Labs on HuggingFace](https://huggingface.co/distil-labs)
---
*Built with [Distil Labs](https://distillabs.ai) - turn a prompt and a few examples into production-ready small language models.*

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Gemma Terms of Use
Copyright 2025 Google
Last modified: March 24, 2025
By using, reproducing, modifying, distributing, performing or displaying any portion or element of Gemma, Model Derivatives including via any Hosted Service, (each as defined below) (collectively, the "Gemma Services") or otherwise accepting the terms of this Agreement, you agree to be bound by this Agreement.
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Copyright 2025 OpenAI
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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{{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
{%- if messages[0]['content'] is string -%}
{%- set first_user_prefix = messages[0]['content'] + '
' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
' -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'image' -%}
{{ '<start_of_image>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<start_of_turn>model
'}}
{%- endif -%}

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{
"_sliding_window_pattern": 6,
"architectures": [
"Gemma3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"bos_token_id": 2,
"eos_token_id": 1,
"final_logit_softcapping": null,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 640,
"initializer_range": 0.02,
"intermediate_size": 2048,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"model_type": "gemma3_text",
"num_attention_heads": 4,
"num_hidden_layers": 18,
"num_key_value_heads": 1,
"pad_token": "<pad>",
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_local_base_freq": 10000.0,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": 512,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.0",
"use_bidirectional_attention": false,
"use_cache": true,
"vocab_size": 262144
}

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"cache_implementation": "hybrid",
"do_sample": true,
"eos_token_id": [
1,
106
],
"top_k": 64,
"top_p": 0.95,
"transformers_version": "4.53.0"
}

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"rstrip": false,
"single_word": false
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"eos_token": {
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"image_token": "<image_soft_token>",
"pad_token": {
"content": "<pad>",
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"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
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