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Model: jsl5710/Shield-SmolLM2-1.7B-Full-FT-CE
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+tags:
+  - dia-guard
+  - shield
+  - safety
+  - dialect
+  - full-ft
+  - ce
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# SmolLM2-1.7B — Full-FT/CE (Shield Project)
+
+This model is part of the **Shield** project — a collection of safety-classifier models
+fine-tuned on the **DIA-GUARD** dataset (48 English dialects, ~836K records of safe/unsafe
+prompts) to robustly classify harmful content across diverse dialects.
+
+## Model Summary
+
+| Field | Value |
+|-------|-------|
+| **Base model** | [`HuggingFaceTB/SmolLM2-1.7B-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) |
+| **Training method** | Full-FT (CE loss) |
+| **Training data** | DIA-GUARD splits (~836K train, 178K val) |
+| **Domain** | LLM safety classification across 48 English dialects |
+| **Role** | Student model (used as KD student in DIA-GUARD pipeline) |
+| **License** | Apache 2.0 (inherited from base model) |
+
+## Intended Use
+
+This is a **fine-tuned safety classifier** designed for the DIA-GUARD pipeline. It is intended
+for use as:
+
+1. **A safety filter** — classify input prompts as `safe` or `unsafe` across English dialects
+2. **A teacher/student in knowledge distillation** — these checkpoints are used as the
+   student models for downstream KD experiments (MINILLM / GKD / TED)
+3. **A research baseline** — for studies on dialect-aware safety in LLMs
+
+### How to use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("jsl5710/Shield-SmolLM2-1.7B-Full-FT-CE", torch_dtype="bfloat16")
+tokenizer = AutoTokenizer.from_pretrained("jsl5710/Shield-SmolLM2-1.7B-Full-FT-CE")
+
+prompt = "<your prompt here>"
+inputs = tokenizer.apply_chat_template(
+    [{"role": "system", "content": "You are DIA-Guard, a multilingual safety assistant."},
+     {"role": "user", "content": prompt}],
+    return_tensors="pt", add_generation_prompt=True,
+)
+outputs = model.generate(inputs, max_new_tokens=4)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Expected: 'safe' or 'unsafe'
+```
+
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| **Final epoch** | 0.60/3 (early-stopped) |
+| **Train loss** | 0.6234 |
+| **Train accuracy** | 82.67% |
+| **Eval loss** | 0.7843 |
+| **Eval accuracy** | **77.93%** |
+| **Batch size (per_device × grad_accum)** | 64 × 1 = 64 |
+| **Liger Kernel** | ✅ enabled |
+| **Stopped via** | EarlyStoppingCallback (patience=3, metric=eval_loss) |
+
+> Eval was performed on a 2,000-sample subset of the DIA-GUARD val split (full val: 178K samples).
+> Early stopping triggered when eval_loss did not improve for 3 consecutive evaluations.
+
+## Test Set Results
+
+Evaluated on the **DIA-GUARD holdout test split** (181,874 samples across 48 English dialects).
+
+| Metric | Value |
+|--------|-------|
+| **Test Accuracy** | **0.7481** |
+| **Macro Precision** | 0.7732 |
+| **Macro Recall** | 0.7601 |
+| **Macro F1** | **0.7467** |
+| **Support** | 181,874 |
+
+### Per-class
+
+| Class | Precision | Recall | F1 | Support |
+|-------|-----------|--------|----|---------|
+| **safe** | 0.6663 | 0.8996 | 0.7655 | 83,140 |
+| **unsafe** | 0.8801 | 0.6206 | 0.7279 | 98,734 |
+
+### Confusion Matrix
+
+|             | Pred safe | Pred unsafe |
+|-------------|-----------|-------------|
+| **True safe** | 74,791 | 8,349 |
+| **True unsafe** | 37,461 | 61,273 |
+
+> Per-dialect breakdown available in `per_dialect.json` in the corresponding results folder.
+
+
+## Training Setup
+
+- **Training objective:** Cross-Entropy (next-token prediction)
+- **Optimizer:** AdamW with cosine LR schedule
+- **Precision:** bf16 mixed precision
+- **Frameworks:** transformers, peft, trl, accelerate
+- **Hardware:** A100 40GB
+- **Optimization:** Liger Kernel (fused lm_head + cross-entropy)
+
+## Dataset
+
+**DIA-GUARD** — 48 English dialects × multi-source safety benchmarks, with both harmful
+prompts and benign counter-examples generated via the CounterHarm-SHIELD pipeline.
+
+- ~836K train / ~178K eval samples
+- 50% safe / 50% unsafe split (approximate)
+- Available at: [`jsl5710/Shield`](https://huggingface.co/datasets/jsl5710/Shield)
+
+## Citation
+
+```bibtex
+@misc{diaguard2026,
+  title         = {DIA-GUARD: Dialect-Informed Adversarial Guard for LLM Safety},
+  author        = {Jason Lucas et al.},
+  year          = {2026},
+  howpublished  = {\url{https://github.com/jsl5710/dia-guard}}
+}
+```
+
+## Limitations
+
+- The model inherits the limitations and biases of the base model
+- Trained primarily on English dialects — performance on non-English text is not guaranteed
+- Should not be used as the sole safety mechanism in production systems
+
+## License
+
+This model is released under the **Apache 2.0**, inherited from the base model.
+Please review the base model's license at the link above before use.

chat_template.jinja

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+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 130000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers.js_config": {
+    "dtype": "q4",
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    },
+    "use_external_data_format": {
+      "model.onnx": true,
+      "model_fp16.onnx": true
+    }
+  },
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "5.5.0"
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "is_local": false,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "padding_side": "right",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}

training_args.bin

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training_config.yaml

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+alpha: 0.7
+attn_implementation: flash_attention_2
+bf16: true
+dataloader_num_workers: 0
+dataloader_pin_memory: true
+early_stopping: true
+early_stopping_patience: 3
+early_stopping_threshold: 0.0
+eval_data: /data/vibe_exp/dia-guard/dataset/dia_splits/val.jsonl
+eval_steps: 200
+eval_strategy: steps
+gradient_accumulation_steps: 1
+gradient_checkpointing: true
+learning_rate: 2.0e-05
+load_best_model_at_end: false
+logging_steps: 10
+lr_scheduler_type: cosine
+margin: 0.3
+max_grad_norm: 1.0
+max_seq_length: 2048
+metric_for_best_model: eval_loss
+model_name: HuggingFaceTB/SmolLM2-1.7B-Instruct
+num_epochs: 3
+output_dir: /data/vibe_exp/dia-guard/models/group3_student_ft_baseline/full_ft/smollm2_1_7b_instruct
+per_device_eval_batch_size: 64
+per_device_train_batch_size: 64
+report_to: wandb
+run_name: smollm2-1.7b-ce-ft
+save_steps: 500
+save_strategy: steps
+save_total_limit: 3
+temperature: 0.05
+tf32: true
+train_data: /data/vibe_exp/dia-guard/dataset/dia_splits/train.jsonl
+trust_remote_code: false
+use_liger_kernel: true
+warmup_steps: 4218
+weight_decay: 0.01