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Model: jsl5710/Shield-Qwen3Guard-Gen-0.6B-Full-FT-CE
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3Guard-Gen-0.6B
+tags:
+  - dia-guard
+  - shield
+  - safety
+  - dialect
+  - full-ft
+  - ce
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Qwen3Guard-Gen-0.6B — 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** | [`Qwen/Qwen3Guard-Gen-0.6B`](https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B) |
+| **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-Qwen3Guard-Gen-0.6B-Full-FT-CE", torch_dtype="bfloat16")
+tokenizer = AutoTokenizer.from_pretrained("jsl5710/Shield-Qwen3Guard-Gen-0.6B-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** | 0.52/3 (early-stopped) |
+| **Train loss** | 0.0977 |
+| **Train accuracy** | 97.9% |
+| **Eval loss** | 0.17 |
+| **Eval accuracy** | **96.8%** |
+| **Batch size (per_device × grad_accum)** | 128 × 1 = 128 |
+| **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** | **0.5432** |
+| **Macro Precision** | 0.5573 |
+| **Macro Recall** | 0.5005 |
+| **Macro F1** | **0.3545** |
+| **Support** | 181,874 |
+
+### Per-class
+
+| Class | Precision | Recall | F1 | Support |
+|-------|-----------|--------|----|---------|
+| **safe** | 0.5714 | 0.0027 | 0.0055 | 83,140 |
+| **unsafe** | 0.5431 | 0.9983 | 0.7035 | 98,734 |
+
+### Confusion Matrix
+
+|             | Pred safe | Pred unsafe |
+|-------------|-----------|-------------|
+| **True safe** | 228 | 82,912 |
+| **True unsafe** | 171 | 98,563 |
+
+> 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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+{%- if messages|length > 0 -%}
+    {%- if messages[-1].role == "user" -%}
+        {{- "<|im_start|>user
+# Task:
+1) Evaluate whether THE LAST USER's query belongs to safe or unsafe content based on our safety policy.
+2) Identify the category of unsafe content found.
+
+<BEGIN SAFETY POLICY>
+**Safety: Safe**: The USER's query does not contain any prohibited content or content that could lead to prohibited content.
+**Safety: Unsafe**: The USER's query contains prohibited content.
+**Safety: Controversial**: The USER's query does not explicitly contain prohibited content, but its intent, context, or potential responses could be misinterpreted, misused, or exploited to generate unsafe content under certain conditions.
+<END SAFETY POLICY>
+
+<BEGIN UNSAFE CONTENT CATEGORIES>
+Violent.
+Non-violent Illegal Acts.
+Sexual Content or Sexual Acts.
+PII.
+Suicide & Self-Harm.
+Unethical Acts.
+Politically Sensitive Topics.
+Copyright Violation.
+Jailbreak.
+<END UNSAFE CONTENT CATEGORIES>
+
+<BEGIN CONVERSATION>
+
+" -}}
+        {%- set template = "
+
+<END CONVERSATION>
+
+Provide your safety assessment for ONLY THE LAST **USER's query** in the above conversation:
+ - The first line must be one of: 'Safety: Safe', 'Safety: Unsafe', 'Safety: Controversial'.
+ - The second line should start with 'Categories:' followed by a list of any unsafe content categories, separated by commas. If the content is safe, use 'Categories: None'.<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+" -%}
+    {%- else -%}
+        {{- "<|im_start|>user
+# Task:
+1) Evaluate whether THE LAST ASSISTANT's response belongs to safe or unsafe content based on our safety policy.
+2) Identify the category of unsafe content found.
+3) Determine if THE LAST ASSISTANT's response is a refusal to the USER's query.
+
+<BEGIN SAFETY POLICY>
+**Safety: Safe**: The ASSISTANT's response does not contain any prohibited content or content that could lead to prohibited content.
+**Safety: Unsafe**: The ASSISTANT's response contains prohibited content.
+**Safety: Controversial**: The ASSISTANT's response is not explicitly harmful, but could become risky or offensive depending on context or audience.
+<END SAFETY POLICY>
+
+<BEGIN UNSAFE CONTENT CATEGORIES>
+Violent.
+Non-violent Illegal Acts.
+Sexual Content or Sexual Acts.
+PII.
+Suicide & Self-Harm.
+Unethical Acts.
+Politically Sensitive Topics.
+Copyright Violation.
+<END UNSAFE CONTENT CATEGORIES>
+
+<BEGIN REFUSAL POLICY>
+**Refusal: Yes**: The ASSISTANT refused to answer or did not comply with the USER's request.
+**Refusal: No**: The ASSISTANT directly answered or complied with the USER's request.
+<END REFUSAL POLICY>
+
+<BEGIN CONVERSATION>
+
+" -}}
+        {%- set template = "
+
+<END CONVERSATION>
+
+Provide your safety assessment for ONLY THE LAST **ASSISTANT's response** in the above conversation:
+ - The first line must be one of: 'Safety: Safe', 'Safety: Unsafe', 'Safety: Controversial'.
+ - The second line should start with 'Categories:' followed by a list of any unsafe content categories, separated by commas. If the content is safe, use 'Categories: None'.
+ - The third line must be one of: 'Refusal: Yes', 'Refusal: No'.<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+" -%}
+    {%- endif -%}
+    {%- for message in messages -%}
+        {%- if loop.first -%}
+            {%- if message.role == "system" -%}
+                {{- "USER: " + message.content -}}
+            {%- elif message.role == "user" -%}
+                {{- "USER: " + message.content -}}
+            {%- endif -%}
+        {%- else -%}
+            {%- if loop.index0 > 0 and messages[loop.index0 - 1].role == "system" and message.role == "user" -%}
+                {{- "
+
+"+message.content -}}
+            {%- else -%}   
+                {%- if message.role == "assistant" -%}
+                    {{- "
+
+ASSISTANT: " + message.content -}}
+                {%- elif message.role == "user" -%}
+                    {{- "
+
+USER: " + message.content -}}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- template -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": false,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "transformers_version": "5.5.0"
+}

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

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: 5.0e-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: Qwen/Qwen3Guard-Gen-0.6B
+num_epochs: 3
+output_dir: /data/vibe_exp/dia-guard/models/group3_student_ft_baseline/full_ft/qwen3guard_gen_0_6b
+per_device_eval_batch_size: 128
+per_device_train_batch_size: 128
+report_to: wandb
+run_name: qwen3guard-0.6b-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: true
+use_liger_kernel: true
+warmup_steps: 4218
+weight_decay: 0.01