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Model: jsl5710/Shield-Gemma-3-270m-Full-FT-CE Source: Original Platform
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README.md
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README.md
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---
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license: gemma
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base_model: google/gemma-3-270m-it
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tags:
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- dia-guard
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- shield
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- safety
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- dialect
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- full-ft
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- ce
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Gemma-3-270m — Full-FT/CE (Shield Project)
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This model is part of the **Shield** project — a collection of safety-classifier models
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fine-tuned on the **DIA-GUARD** dataset (48 English dialects, ~836K records of safe/unsafe
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prompts) to robustly classify harmful content across diverse dialects.
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## Model Summary
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| Field | Value |
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|-------|-------|
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| **Base model** | [`google/gemma-3-270m-it`](https://huggingface.co/google/gemma-3-270m-it) |
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| **Training method** | Full-FT (CE loss) |
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| **Training data** | DIA-GUARD splits (~836K train, 178K val) |
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| **Domain** | LLM safety classification across 48 English dialects |
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| **Role** | Student model (used as KD student in DIA-GUARD pipeline) |
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| **License** | Gemma Terms of Use (inherited from base model) |
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## Intended Use
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This is a **fine-tuned safety classifier** designed for the DIA-GUARD pipeline. It is intended
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for use as:
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1. **A safety filter** — classify input prompts as `safe` or `unsafe` across English dialects
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2. **A teacher/student in knowledge distillation** — these checkpoints are used as the
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student models for downstream KD experiments (MINILLM / GKD / TED)
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3. **A research baseline** — for studies on dialect-aware safety in LLMs
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### How to use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("jsl5710/Shield-Gemma-3-270m-Full-FT-CE", torch_dtype="bfloat16")
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tokenizer = AutoTokenizer.from_pretrained("jsl5710/Shield-Gemma-3-270m-Full-FT-CE")
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prompt = "<your prompt here>"
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inputs = tokenizer.apply_chat_template(
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[{"role": "system", "content": "You are DIA-Guard, a multilingual safety assistant."},
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{"role": "user", "content": prompt}],
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return_tensors="pt", add_generation_prompt=True,
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)
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outputs = model.generate(inputs, max_new_tokens=4)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# Expected: 'safe' or 'unsafe'
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```
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## Performance
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| Metric | Value |
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|--------|-------|
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| **Final epoch** | 0.73/3 (early-stopped) |
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| **Train loss** | 0.5839 |
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| **Train accuracy** | 87.29% |
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| **Eval loss** | 1.078 |
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| **Eval accuracy** | **79.68%** |
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| **Batch size (per_device × grad_accum)** | 256 × 1 = 256 |
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| **Liger Kernel** | ✅ enabled |
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| **Stopped via** | EarlyStoppingCallback (patience=3, metric=eval_loss) |
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> Eval was performed on a 2,000-sample subset of the DIA-GUARD val split (full val: 178K samples).
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> Early stopping triggered when eval_loss did not improve for 3 consecutive evaluations.
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## Test Set Results
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Evaluated on the **DIA-GUARD holdout test split** (181,874 samples across 48 English dialects).
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| Metric | Value |
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|--------|-------|
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| **Test Accuracy** | **0.9654** |
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| **Macro Precision** | 0.9676 |
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| **Macro Recall** | 0.9634 |
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| **Macro F1** | **0.9650** |
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| **Support** | 181,874 |
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### Per-class
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| Class | Precision | Recall | F1 | Support |
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|-------|-----------|--------|----|---------|
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| **safe** | 0.9844 | 0.9392 | 0.9613 | 83,140 |
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| **unsafe** | 0.9507 | 0.9875 | 0.9688 | 98,734 |
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### Confusion Matrix
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| | Pred safe | Pred unsafe |
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|-------------|-----------|-------------|
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| **True safe** | 78,087 | 5,053 |
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| **True unsafe** | 1,234 | 97,500 |
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> Per-dialect breakdown available in `per_dialect.json` in the corresponding results folder.
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## Training Setup
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- **Training objective:** Cross-Entropy (next-token prediction)
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- **Optimizer:** AdamW with cosine LR schedule
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- **Precision:** bf16 mixed precision
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- **Frameworks:** transformers, peft, trl, accelerate
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- **Hardware:** A100 40GB
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- **Optimization:** Liger Kernel (fused lm_head + cross-entropy)
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## Dataset
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**DIA-GUARD** — 48 English dialects × multi-source safety benchmarks, with both harmful
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prompts and benign counter-examples generated via the CounterHarm-SHIELD pipeline.
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- ~836K train / ~178K eval samples
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- 50% safe / 50% unsafe split (approximate)
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- Available at: [`jsl5710/Shield`](https://huggingface.co/datasets/jsl5710/Shield)
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## Citation
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```bibtex
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@misc{diaguard2026,
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title = {DIA-GUARD: Dialect-Informed Adversarial Guard for LLM Safety},
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author = {Jason Lucas et al.},
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year = {2026},
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howpublished = {\url{https://github.com/jsl5710/dia-guard}}
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}
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```
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## Limitations
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- The model inherits the limitations and biases of the base model
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- Trained primarily on English dialects — performance on non-English text is not guaranteed
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- Should not be used as the sole safety mechanism in production systems
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## License
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This model is released under the **Gemma Terms of Use**, inherited from the base model.
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Please review the base model's license at the link above before use.
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