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README.md

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+---
+language:
+  - ko
+license: apache-2.0
+tags:
+  - task-specific
+  - structured-prediction
+  - korean
+  - public-sector
+  - lora
+  - qwen3
+  - domain-specific
+base_model: Qwen/Qwen3-4B
+datasets: []
+pipeline_tag: text-generation
+model-index:
+  - name: DLM-NL2JSON-4B
+    results:
+      - task:
+          type: structured-prediction
+          name: Korean NL-to-JSON Schema Extraction
+        dataset:
+          type: custom
+          name: Busan Public Data Query Test Set
+          args:
+            num_samples: 2041
+        metrics:
+          - type: exact_match
+            value: 94.4
+            name: Exact Match Accuracy (raw)
+          - type: exact_match
+            value: 96.8
+            name: Exact Match Accuracy (adjusted)
+---
+
+# DLM-NL2JSON-4B
+
+**A 4B-parameter service-specific LLM that outperforms GPT-4o (+14%p) and Qwen3.5-35B (+22%p) on structured JSON extraction from Korean natural language queries.**
+
+DLM (Domain-specific Language Model) is a series of task-specialized models by [Data Science Lab., Ltd.](https://huggingface.co/dataslab). This model is a LoRA-merged Qwen3-4B fine-tuned for structured JSON extraction in the Busan Metropolitan City public data analytics service.
+
+## Key Results
+
+Evaluated on 2,041 test samples across 10 task categories (field-level exact match, summary excluded):
+
+| Model | Params | Accuracy | Accuracy (adj*) | Avg Latency |
+|-------|--------|----------|-----------------|-------------|
+| **DLM-NL2JSON-4B** | **4B** | **94.4%** | **96.8%** | 2.59s |
+| GPT-4o | ~200B+ | 80.5% | 82.5% | 1.58s |
+| Qwen3.5-35B-A3B | 35B | 72.2% | 73.9% | 0.85s |
+
+*\*adj: 64 CSM samples with known gold label noise excluded (see Evaluation section)*
+
+### Per-Category Breakdown
+
+| Category | N | DLM-NL2JSON-4B | GPT-4o | Qwen3.5-35B |
+|----------|---|-------------|--------|-------------|
+| ALP-A (population pattern) | 250 | **99.6%** | 56.0% | 47.6% |
+| ALP-B (population flow) | 250 | **98.4%** | 50.4% | 46.8% |
+| CSM (consumer spending) | 700 | **90.6%** | 90.1% | 86.1% |
+| CREDIT-Income | 58 | **94.8%** | 53.4% | 34.5% |
+| CREDIT-Spending | 77 | **97.4%** | 92.2% | 51.9% |
+| CREDIT-Loan/Default | 73 | **98.6%** | 94.5% | 72.6% |
+| CPI (business status) | 219 | 86.3% | **87.2%** | 54.8% |
+| GIS-Inflow | 72 | **97.2%** | 79.2% | 93.1% |
+| GIS-Outflow | 62 | **98.4%** | 77.4% | 98.4% |
+| GIS-Consumption | 280 | 98.2% | **99.6%** | 97.5% |
+
+DLM-NL2JSON-4B wins **8 out of 10 categories**, with the largest gains on ALP (+43%p vs GPT-4o) and CREDIT-Income (+41%p).
+
+## Important: This is a Service-Specific Model
+
+> **This model is NOT a general-purpose NL-to-JSON converter.** It is trained exclusively for a fixed set of predefined schemas used in a specific production service. It will not generalize to arbitrary JSON schemas or different prompt formats.
+
+To use this model correctly, you **must**:
+1. Use the **exact system prompts** it was trained on (one per task category — see Usage section)
+2. Include the corresponding **special token** (`<TASK_CSM>`, `<TASK_CREDIT>`, `<TASK_GIS>`, `<TASK_ALP>`, `<TASK_CPI>`) in the input
+3. Expect output conforming only to the **predefined schemas** listed below
+
+**Why publish a service-specific model?** This model serves as a reference implementation demonstrating that **task-specific LoRA fine-tuning on a 4B model can dramatically outperform GPT-4o and larger open-source models** on constrained structured output tasks. We believe the DLM (Domain-specific Language Model) approach — training small, cheap-to-serve models for specific service endpoints — is an underexplored but highly practical paradigm.
+
+## Intended Use
+
+This model converts **Korean natural language queries about public/economic data** into **structured JSON** conforming to its predefined schemas. It is designed for and deployed in the **Busan Metropolitan City Big Data Wave** analytics dashboard.
+
+**Input**: Free-form Korean query + task-specific system prompt
+
+**Output**: Single-line JSON with exact schema compliance:
+```json
+{"summary":"##2025년 5월 부산광역시 해운대구 유통/의료 소비분석##","base_ym":202505,"region_nm":"부산광역시 해운대구","industry_select":{"3":[],"8":[]},"sex_cd":[1],"age_cd":[30],"category":2}
+```
+
+### Task Categories
+
+| ID | Name | Schema Type |
+|----|------|-------------|
+| 0 | ALP-A | Population pattern (ptrn: residence/work/visit) |
+| 1 | ALP-B | Population flow (flow_cd: inflow/outflow) |
+| 2 | CSM | Consumer spending by industry |
+| 3 | CREDIT-Income | Income statistics |
+| 4 | CREDIT-Spending | Spending statistics |
+| 5 | CREDIT-Loan | Loan/default statistics |
+| 6 | CPI | Business/enterprise status |
+| 9 | GIS-Inflow | Geographic inflow analysis |
+| 10 | GIS-Outflow | Geographic outflow analysis |
+| 11 | GIS-Consumption | Geographic consumption analysis |
+
+## Training Details
+
+| Item | Value |
+|------|-------|
+| Base model | [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
+| Method | LoRA SFT → merged full model |
+| Training samples | 16,292 (Korean) |
+| Validation samples | 2,034 |
+| Special tokens | `<TASK_CSM>`, `<TASK_CREDIT>`, `<TASK_GIS>`, `<TASK_ALP>`, `<TASK_CPI>` |
+| Max sequence length | 6,144 |
+| Architecture | Qwen3ForCausalLM (36 layers, 2560 hidden, 32 heads) |
+
+Training data consists of synthetically generated Korean natural language queries paired with structured JSON outputs, covering the Busan public data analytics domain.
+
+## Evaluation Methodology
+
+- **Metric**: Field-level exact match — each JSON key's value is compared against the gold label. The `summary` field is excluded from comparison.
+- **Test set**: 2,041 samples, stratified by category
+- **Gold label noise**: 64/700 CSM samples have `age_cd` capped at `[10..60]` instead of `[10..70]` for "all ages" queries, conflicting with the prompt specification. These affect all models equally and are excluded in the adjusted metric.
+- **Train/Test overlap**: 16/2,041 input strings (0.78%) appear in both sets — retained for consistency.
+- **All models** received identical system prompts per category.
+
+### Hardware
+
+| Model | Serving | GPU |
+|-------|---------|-----|
+| DLM-NL2JSON-4B | TensorRT-LLM | NVIDIA L4 24GB |
+| GPT-4o | OpenAI API | N/A |
+| Qwen3.5-35B-A3B | vLLM | NVIDIA A6000 48GB |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "dataslab/DLM-NL2JSON-4B"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+# System prompt (example: CREDIT schema)
+system_prompt = """너는 반드시 **JSON 한 줄**만 출력한다. 설명/텍스트/코멘트/마크다운/코드블록/이모지/공백 줄 금지.
+
+[스키마: 개인신용 통합]
+{"summary":string,"base_ym":int,"region_nm":string,"job_cd":[int],"perc_cd":[int],"sex_cd":[int],"age_cd":[int],"category":int}
+
+[기본값]
+- base_ym: 0, region_nm: "부산광역시"
+- job_cd: [0,1,2], perc_cd: [0,1,2,3,4,5,6,7,8,9]
+- sex_cd: [0,1], age_cd: [10,20,30,40,50,60,70]
+- category: 소득=3, 소비=4, 대출/연체=5"""
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": "부산 해운대구 소득 남성 30대 3분위"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.0, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
+# {"summary":"##부산광역시 해운대구 소득통계##","base_ym":0,"region_nm":"부산광역시 해운대구","job_cd":[0,1,2],"perc_cd":[2],"sex_cd":[0],"age_cd":[30],"category":3}
+```
+
+### vLLM / OpenAI-compatible serving
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://your-server:8006/v1", api_key="token")
+resp = client.chat.completions.create(
+    model="DLM-NL2JSON-4B",
+    messages=[
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": "부산 해운대구 소득 남성 30대 3분위"}
+    ],
+    max_tokens=512,
+    temperature=0.0,
+    extra_body={"chat_template_kwargs": {"enable_thinking": False}}  # disable thinking mode
+)
+print(resp.choices[0].message.content)
+```
+
+> **Important**: When serving with vLLM/TensorRT-LLM, pass `chat_template_kwargs: {"enable_thinking": false}` to disable the Qwen3 thinking mode. Otherwise, reasoning tokens will consume the output budget and truncate the JSON.
+
+## Known Limitations
+
+1. **CPI category** (86.3%) is the weakest — complex industry classification codes (A~U with sub-codes) are harder to extract.
+2. **CSM training data noise**: ~8% of CSM training samples have `age_cd` capped at 60 instead of 70 for "all ages" queries, introducing inconsistency.
+3. **Domain-specific only**: This model is trained exclusively for the Busan public data schema extraction task. It has no general-purpose capabilities and should not be used as a general chatbot.
+4. **Korean only**: All training data and prompts are in Korean.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{dsl-dlm-nl2json-4b,
+  title={DLM-NL2JSON-4B: A Domain-Specific Language Model for Korean Public Data Schema Extraction},
+  author={Data Science Lab., Ltd.},
+  year={2026},
+  url={https://huggingface.co/dataslab/DLM-NL2JSON-4B}
+}
+```
+
+## Contact
+
+- **Organization**: Data Science Lab., Ltd.
+- **Project**: Busan Metropolitan City Big Data Wave

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<TASK_ALP>": 151672,
+  "<TASK_CPI>": 151673,
+  "<TASK_CREDIT>": 151670,
+  "<TASK_CSM>": 151669,
+  "<TASK_GIS>": 151671,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
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+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
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+  ],
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+  "dtype": "bfloat16",
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+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151674
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.2"
+}

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special_tokens_map.json

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