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Model: Mindie/Qwen3-4b-kss-style-tuning
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+datasets:
+- Mindie/Summary_article_KSS_style_dataset
+---
+
+```markdown
+> Instruction-tuned model with LoRA can enforce structured output without losing general knowledge.
+
+# KSS-Format Instruction-Tuned Model (LoRA)
+
+## 📌 Model Description
+
+This model is instruction-tuned to generate summaries in a structured format:
+
+Subject:
+Keywords:
+Summary:
+
+The goal of this project is to enforce output format while preserving the base model’s general knowledge and reasoning ability.
+
+---
+
+## 🎯 Key Features
+
+- ✅ Structured summary generation (KSS-style format)
+- ✅ Instruction-following behavior
+- ✅ Knowledge preservation after fine-tuning
+- ✅ Robust across both short and long inputs
+
+---
+
+## 🧠 Base Model
+
+- Base model: Qwen3-4B
+- Fine-tuning method: LoRA (Low-Rank Adaptation)
+
+---
+
+## 🏗️ Training Details
+
+### Dataset
+
+- Total samples: 596
+- Contrastive dataset:
+  - Instruction data (format enforced)
+  - Non-instruction data (free-form output)
+
+### Data Sources
+
+- GPT-generated summaries (short-form)
+- Base model-generated summaries (long-form)
+- CNN article dataset (961 samples used for label generation)
+
+---
+
+## ⚙️ Training Setup
+
+- Method: LoRA fine-tuning
+- Objective:
+  - Learn when to apply structured format
+  - Avoid overfitting to instruction-only behavior
+
+---
+
+## 📊 Evaluation
+
+### 1. Format Adherence
+
+- Evaluated on 50 samples
+- Result:
+  - High consistency in following required format
+
+---
+
+### 2. Knowledge Preservation (MMLU)
+
+- Evaluation method: Logit-based scoring
+- Samples: 20 per subject
+
+| Model        | Score |
+|--------------|------|
+| Base Model   | 0.725 |
+| Tuned Model  | 0.724 |
+
+👉 No significant performance degradation observed
+
+---
+
+## 💡 Key Insight
+
+Instruction tuning can enforce output structure **without degrading model knowledge**,  
+when using a carefully designed LoRA fine tuning setup.
+
+---
+
+## 🧪 Example Usage
+This model is finetuned on non-thinking mode. non-thinking mode is recommended
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+#model calling
+model_name = "Mindie/Qwen3-4b-kss-style-tuning"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+#generation
+prompt = text
+messages = [
+    {"role": "user", "content": f'Use KSS style Summaries: {prompt}'}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=200
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
+print(content)
+
+###Ouptut foramt
+Subject:
+Keywords:
+Summary:
+
+
+## 🔗 Project Links
+
+- GitHub Repository: https://github.com/byeongmin1/qwen3-4b-lora-instruction-tuning
+
+## 📖 Training Details
+
+This model was trained using a full pipeline including:
+- data generation
+- filtering
+- instruction tuning (LoRA)
+- Logit Evaluation (MMLU)
+
+For full implementation details, please refer to the GitHub repository.

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": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.0.0"
+}

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