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Model: cycloevan/gdpr_gemma-2-2b
Source: Original Platform
---
 .gitattributes                   |   36 +
 README.md                        |  212 ++++
 config.json                      |   36 +
 generation_config.json           |   11 +
 model-00001-of-00002.safetensors |    3 +
 model-00002-of-00002.safetensors |    3 +
 model.safetensors.index.json     |  295 +++++
 special_tokens_map.json          |   34 +
 tokenizer.json                   |    3 +
 tokenizer.model                  |    3 +
 tokenizer_config.json            | 2013 ++++++++++++++++++++++++++++++
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 create mode 100644 tokenizer_config.json

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diff --git a/README.md b/README.md
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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+base_model: google/gemma-2-2b-it
+tags:
+- gemma
+- gemma-2
+- gdpr
+- compliance
+- legal
+- dpo
+- qlora
+- sft
+datasets:
+- sims2k/GDPR_QA_instruct_dataset
+model-index:
+- name: gdpr_gemma-2-2b
+  results:
+  - task:
+      type: text-generation
+      name: GDPR Q&A
+    dataset:
+      type: sims2k/GDPR_QA_instruct_dataset
+      name: GDPR_QA_instruct_dataset
+      split: train[:100]
+    metrics:
+    - type: rouge
+      name: ROUGE-L
+      value: 0.2252
+    - type: bleu
+      name: BLEU
+      value: 0.1034
+    - type: bertscore
+      name: BertScore F1
+      value: 0.8527
+---
+
+# GDPR-Gemma-2-2B — GDPR Compliance Assistant
+
+A specialized fine-tune of **`google/gemma-2-2b-it`** for English GDPR
+(General Data Protection Regulation) Q&A. The model is aligned with expert
+GDPR answers via a **3-stage pipeline** — Supervised Fine-Tuning, Dynamic
+Rejection sampling, and Direct Preference Optimization (DPO) — using QLoRA
+for resource-friendly training.
+
+> **Disclaimer**: This model provides informational guidance only and **does
+> not constitute legal advice**. Always consult a qualified legal
+> professional for binding GDPR compliance decisions.
+
+- 🔗 GitHub: <https://github.com/seok-hee97/gdpr-gemma2>
+- 🧑‍💻 Author: **seok-hee97** (HF: `cycloevan`)
+- 🏷️ Base: `google/gemma-2-2b-it`
+- 🌐 Language: English
+
+---
+
+## Training Pipeline (3-Stage)
+
+```
+                ┌──────────────┐     ┌────────────────────┐     ┌──────────────┐
+ Base Gemma-2 ─►│ Stage 1: SFT │ ──► │ Stage 2: Dynamic   │ ──► │ Stage 3: DPO │
+                │  (knowledge) │     │ Rejection Sampling │     │ (alignment)  │
+                └──────────────┘     └────────────────────┘     └──────────────┘
+```
+
+| Stage | Goal | Method |
+|---|---|---|
+| 1. SFT | Inject GDPR domain knowledge | QLoRA SFT on expert Q&A |
+| 2. Dynamic Rejection | Build *realistic* preference pairs | Sample SFT outputs (T=0.9) as `rejected`; expert answer = `chosen` |
+| 3. DPO | Align preferences toward expert answers | DPO on top of SFT adapter (β=0.1) |
+
+This pipeline is more faithful than naive DPO because Stage 2 produces
+rejection candidates that match the model's *actual* failure modes, rather
+than synthetic or generic wrong answers.
+
+---
+
+## Training Configuration
+
+| Component | Value |
+|---|---|
+| Base model | `google/gemma-2-2b-it` |
+| Quantization | 4-bit NF4 (QLoRA), bf16 compute |
+| LoRA `r` / `alpha` / `dropout` | 16 / 32 / 0.05 |
+| LoRA target modules | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
+| SFT epochs / LR | 3 / 2e-5 |
+| DPO epochs / LR / β | 3 / 5e-6 / 0.1 |
+| Batch size / Grad accum | 1 / 4 |
+| Max prompt / total length | 1024 / 2048 |
+| Optimizer | `paged_adamw_8bit` |
+| Hardware | NVIDIA DGX Spark (CUDA, bf16) |
+
+---
+
+## Evaluation
+
+Quantitative on 100 samples from `sims2k/GDPR_QA_instruct_dataset`;
+qualitative via GPT-4o LLM-as-a-Judge on 10 samples (1–5 scale).
+
+### Quantitative (ROUGE / BLEU / BertScore)
+
+| Metric        | Base   | SFT        | **DPO (this model)** |
+|---------------|--------|------------|----------------------|
+| ROUGE-L       | 0.2072 | **0.2331** | 0.2252               |
+| BLEU          | 0.0838 | **0.1146** | 0.1034               |
+| BertScore F1  | 0.8432 | **0.8541** | 0.8527               |
+
+### Qualitative (GPT-4o Judge, 1–5)
+
+| Criterion             | Base | SFT  | **DPO (this model)** |
+|-----------------------|------|------|----------------------|
+| Legal Correctness     | 3.10 | 3.00 | **3.40**             |
+| Article Accuracy      | 2.20 | 2.30 | **2.60**             |
+| Compliance Alignment  | 3.70 | 3.40 | **3.80**             |
+| Clarity               | **4.10** | **4.10** | 3.80         |
+
+DPO improves legal correctness, GDPR-article citation accuracy, and
+compliance alignment over both Base and SFT. It trades a small amount of
+surface-level lexical overlap (ROUGE/BLEU) and clarity in exchange for
+substantively more accurate legal content — a typical alignment trade-off.
+
+---
+
+## Quickstart
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "cycloevan/gdpr_gemma-2-2b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="eager",  # recommended for Gemma-2
+)
+
+SYSTEM = (
+    "You are a professional GDPR compliance assistant. "
+    "Provide accurate, legal, and clear guidance based on the General Data "
+    "Protection Regulation."
+)
+
+def ask_gdpr(question: str, max_new_tokens: int = 512) -> str:
+    messages = [{"role": "user", "content": f"{SYSTEM}\n\nQuestion: {question}"}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=0.1,
+        top_p=0.2,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return text.split("model")[-1].strip() if "model" in text else text
+
+print(ask_gdpr("What are the main principles of GDPR?"))
+```
+
+---
+
+## Intended Use
+
+- **In-scope**: Educational explanations of GDPR articles and principles,
+  drafting first-pass compliance summaries, internal training material,
+  GDPR-aware chatbot prototypes.
+- **Out-of-scope**: Binding legal opinions, jurisdiction-specific advice
+  outside the EU/EEA, regulated decisions affecting individuals' rights,
+  enforcement/litigation strategy.
+
+## Limitations & Risks
+
+- **Snapshot of the regulation**: Trained on a static GDPR Q&A dataset;
+  does not reflect post-training case law (CJEU rulings, EDPB guidelines)
+  or national supervisory authority decisions.
+- **English only**: No multilingual coverage; legal language outside English
+  may degrade significantly.
+- **Article-citation accuracy**: Average ~2.6/5 — the model occasionally
+  cites incorrect or non-existent article numbers. Always verify citations
+  against the official GDPR text.
+- **Alignment trade-off**: DPO improves substantive legal accuracy at a
+  small cost to surface fluency vs the SFT-only variant.
+- **Hallucination**: As with any LLM, it can fabricate plausible-looking
+  legal references. Treat outputs as drafts, not authoritative sources.
+
+## Ethical Considerations
+
+GDPR compliance affects individuals' fundamental rights to privacy and data
+protection. Errors in legal interpretation may cause organisations to
+mishandle personal data or mislead data subjects. Use only as a
+decision-support tool, never as the sole basis for compliance actions.
+
+## Citation
+
+```bibtex
+@misc{gdpr_gemma_2_2b_2024,
+  title  = {GDPR-Gemma-2-2B: A 3-Stage Aligned GDPR Compliance Assistant},
+  author = {seok-hee97},
+  year   = {2024},
+  howpublished = {Hugging Face Model Hub},
+  url    = {https://huggingface.co/cycloevan/gdpr_gemma-2-2b}
+}
+```
diff --git a/config.json b/config.json
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+{
+  "_name_or_path": "/Users/jangseokhee/workspace/P/ML/gdpr-gemma2/models/gemma-2-2b-it",
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.0",
+  "use_cache": true,
+  "vocab_size": 256000
+}
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+  "pad_token_id": 0,
+  "transformers_version": "4.43.0"
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+oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
+size 4241003
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..36ba128
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,2013 @@
+{
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+      "normalized": false,
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+    },
+    "196": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    "197": {
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+      "lstrip": false,
+      "normalized": false,
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+    },
+    "198": {
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+      "lstrip": false,
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+    "199": {
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+    "203": {
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+      "single_word": false,
+      "special": false
+    },
+    "204": {
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+      "rstrip": false,
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+    },
+    "205": {
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+      "rstrip": false,
+      "single_word": false,
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+    },
+    "213": {
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+    "216": {
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+    },
+    "255968": {
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+    },
+    "255969": {
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+    },
+    "255971": {
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+    },
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+    },
+    "255984": {
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+      "normalized": false,
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+    },
+    "255985": {
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+    "255986": {
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+    },
+    "255993": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255994": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255995": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255996": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255997": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255998": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255999": {
+      "content": "<unused99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": "<bos>",
+  "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in 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 + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}