gemma-3-1b-tatar-lora is a LoRA adapter built on top of Google's Gemma 3 (1B) instruction-tuned model, specialized for profanity filtering in the Tatar language. Given a Tatar-language input, the model rewrites the text to neutralize obscene language while preserving the original meaning as closely as possible. It is designed as a lightweight, on-device-friendly moderation component for chat apps, comment sections, and other user-generated-content pipelines that need Tatar-language coverage — a language largely unserved by mainstream moderation tools.
The adapter was trained with Unsloth and Hugging Face's TRL library, offering 2x faster fine-tuning than a standard training loop, on top of the 4-bit quantized base model unsloth/gemma-3-1b-it-bnb-4bit.
The adapter was evaluated on a held-out test set of Tatar sentences containing profanity, alongside a control set of clean sentences to measure over-filtering.
Metric
Value
Description
Toxicity Reduction Rate
78.67%
Share of profane input successfully neutralized
False Positive Rate
0.00%
Share of clean input incorrectly altered
BERTScore (F1)
0.6656
Semantic similarity between input and output at the embedding level
Avg Cosine Similarity
0.6633
Average embedding-level similarity before vs. after filtering
Peak VRAM Delta
0.74 GB
Additional GPU memory used during inference
Avg Tokens/sec
2.88
Average decoding throughput
Reading the results: the model combines a strong toxicity reduction rate with a 0.00% false positive rate, meaning clean text is left untouched — an important property for a moderation filter that shouldn't "cry wolf" on innocuous language. The moderate BERTScore/cosine similarity (~0.66) reflects the fact that neutralizing profanity necessarily changes the text; it is not expected to reach near-1.0 similarity the way a pure paraphrase task would. Throughput (2.88 tok/s) reflects an unoptimized single-request setup and can likely be improved with batching or a dedicated serving engine (vLLM/SGLang).
Core Capabilities
Tatar-language profanity detection & neutralization — identifies and rewrites obscene words/phrases in Tatar text.
Meaning preservation — aims to keep the surrounding sentence intact rather than simply blanking out words.
Low false-positive behavior — avoids altering clean text, as shown by the 0.00% FPR on the evaluation set.
Lightweight footprint — built on a 1B-parameter base with a small VRAM delta (~0.74 GB), suitable for consumer GPUs or CPU inference with quantization.
Chat-template compatible — works out of the box with the Gemma 3 chat template via Transformers.
Single-turn filtering — the model performs best on single sentences or short passages passed as a single user turn; very long inputs may see degraded meaning preservation.
Post-filter validation — for user-facing moderation pipelines, treat this model as one signal alongside rule-based filters and/or human review, rather than a standalone decision-maker.
Batching for throughput — since baseline throughput is ~2.88 tok/s, batch multiple requests or use a serving engine (vLLM/SGLang) in production to improve latency.
Domain match — the model is tuned specifically for Tatar profanity; performance on other Turkic languages or heavy Tatar-Russian code-mixing has not been separately validated.
Model Data
Base model pretraining data: inherited from google/gemma-3-1b-it, Google's general-purpose multilingual pretraining corpus (see the Gemma 3 model card for details).
Fine-tuning data: a task-specific dataset of Tatar sentences containing profanity paired with neutralized rewrites, plus clean Tatar sentences used to measure false positives.
Fine-tuning method: LoRA adapters trained with Unsloth + TRL on the 4-bit quantized base model.
Ethics and Safety
This adapter inherits the safety properties and limitations of its Gemma 3 base model. It has not undergone the same scale of red-teaming and safety evaluation as Google's official Gemma releases, and it is a community fine-tune, not an official Google product. It should not be treated as a certified content-safety system.
Usage and Limitations
Intended Usage
Pre-processing/filtering layer for Tatar-language user-generated content (chat, comments, forums).
Research on low-resource-language moderation and detoxification.
A starting point for further fine-tuning on related Tatar NLP moderation tasks.
Limitations
Evaluated on a single held-out test set; results may not generalize to other data distributions (slang, dialectal variation, Tatar-Russian code-mixing).
Moderate semantic-similarity scores (~0.66) mean meaning drift is possible on longer or more complex inputs.
Not evaluated as a standalone content-moderation system; should be paired with additional safeguards in production.
Inference throughput (~2.88 tok/s) is modest without further optimization.
Ethical Considerations and Risks
Over-reliance risk: automated filtering can create false confidence; sensitive deployments (e.g., moderating content involving minors) should include human review.
Bias: the fine-tuning dataset's coverage of Tatar dialects/slang is not exhaustively documented; filtering quality may vary across regional variants.
Dual use: understanding what counts as "profane" in a language can, in principle, be misused to build language-specific harassment tools; this model is released for defensive/moderation use cases only.
Citation
If you use this adapter, please consider citing the repository:
bibtex
1@misc{ladiesman69_2026_gemma3tatar,
2 title={gemma-3-1b-tatar-lora: A LoRA Adapter for Tatar Profanity Filtering},
3 author={LadiesMan69},
4 year={2026},
5 url={https://huggingface.co/LadiesMan69/gemma-3-1b-tatar-lora}
6}
