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Model: vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct-V2
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+datasets:
+- vector-institute/unbias-plus-dataset
+language:
+- en
+tags:
+- qwen3
+- 8b
+- bias-detection
+- news-debiasing
+- fine-tuned
+- unsloth
+- sft
+- conversational
+pipeline_tag: text-generation
+---
+
+# Qwen3-8B-UnBias-Plus-SFT-Instruct-V2
+
+A fine-tuned version of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for news media bias detection and neutral rewriting, developed by the [Vector Institute](https://vectorinstitute.ai) as part of the [UnBias-Plus](https://github.com/VectorInstitute/unbias-plus) project.
+
+Given a news article, the model identifies biased language segments, classifies each segment's bias type and severity, provides neutral replacements, assigns a single article-level severity score, and returns a fully rewritten unbiased version, all in a single structured JSON response.
+
+This is the **V2 Instruct variant**, trained without chain-of-thought thinking blocks (`enable_thinking=False`). It produces clean structured JSON directly, making it suitable for production inference via vLLM or other OpenAI-compatible backends.
+
+V2 is trained as a **conservative span-level bias annotator**: it flags only clear, material bias (while always flagging toxic/dehumanizing language), applies a strict bias-type label precedence, treats attributed/quoted language under the same standard, and changes only flagged text when producing the rewrite.
+
+> **Trained on:** [train_4](https://huggingface.co/datasets/vector-institute/unbias-plus-dataset) (5,000 expert-annotated news articles)
+
+## Difference from Qwen3-8B-UnBias-Plus-SFT-Instruct
+
+|                          | SFT-Instruct (predecessor)                                   | SFT-Instruct-V2 (this model)                                                    |
+| ------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------------------------- |
+| Training data            | train_3 (UnBias-Plus-3000)                                   | train_4 (5,000 expert-annotated articles)                                       |
+| Article severity scale   | `0, 1, 2, 3, 4`                                              | `0–10` integer                                                                  |
+| Segment severity         | `low` / `medium` / `high`                                    | `Low` / `Medium` / `High`                                                       |
+| Output fields            | `severity`, `bias_found`, `biased_segments`, `unbiased_text` | `severity`, `biased_segments`, `unbiased_text` (label/bias_found derived downstream) |
+| Bias type taxonomy       | 7 free-form labels                                           | 8 canonical slugs with strict label precedence                                  |
+| System prompt            | Bias detection + rewrite rules                               | Conservative span-level annotator (label precedence, attribution handling, toxicity rules, audit step) |
+
+## Model Details
+
+| Property           | Value                                                    |
+| ------------------ | -------------------------------------------------------- |
+| Base model         | Qwen/Qwen3-8B                                            |
+| Fine-tuning method | Supervised Fine-Tuning (SFT) with LoRA                   |
+| Training precision | bf16 (full precision, no quantization during training)   |
+| LoRA rank          | 16                                                       |
+| Training framework | Unsloth + TRL                                            |
+| Context length     | 8192 tokens                                              |
+| Thinking mode      | Disabled (`enable_thinking=False`)                       |
+| Output format      | Structured JSON                                          |
+| Training dataset   | [train_4](https://huggingface.co/datasets/vector-institute/unbias-plus-dataset) (5,000 expert-annotated articles) |
+
+## Usage
+
+The exact system prompt used during training is maintained in
+[`src/unbias_plus/prompt.py`](https://github.com/VectorInstitute/unbias-plus/blob/main/src/unbias_plus/prompt.py)
+(`SYSTEM_PROMPT`). For faithful results, use that prompt verbatim — a condensed
+version is shown below for illustration. The easiest path is to use the
+[`unbias-plus`](https://github.com/VectorInstitute/unbias-plus) package, which
+wires up the prompt, parsing, and offset computation for you.
+
+### Using with transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch, json
+
+model_id = "vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct-V2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model.eval()
+
+# Use the full SYSTEM_PROMPT from src/unbias_plus/prompt.py for best results.
+SYSTEM_PROMPT = """You are a conservative span-level bias annotator.
+
+Given an article, identify material biased language, assign one article-level
+severity score (0-10), and produce a neutral rewrite that changes only flagged
+text. Return the result only as a single valid JSON object with the fields
+`severity`, `biased_segments`, and `unbiased_text`. Do not output `binary_label`
+or `bias_found`. Do not write anything outside the JSON object."""
+
+article = "Your news article here..."
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": f"Analyze the following article for bias and return the result in the required JSON format.\n\nARTICLE:\n{article}"},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=False,
+    return_tensors="pt",
+    return_dict=True,
+    truncation=True,
+    max_length=8192,
+)
+
+with torch.no_grad():
+    outputs = model.generate(
+        input_ids=inputs["input_ids"].to(model.device),
+        attention_mask=inputs["attention_mask"].to(model.device),
+        max_new_tokens=4096,
+        do_sample=False,
+        temperature=None,
+        top_p=None,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+response = tokenizer.decode(new_tokens, skip_special_tokens=True)
+result = json.loads(response)
+```
+
+### Using with vLLM
+
+```bash
+vllm serve vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct-V2 \
+  --served-model-name unbias-plus \
+  --max-model-len 8192 \
+  --dtype bfloat16
+```
+
+```python
+from openai import OpenAI
+import json
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+
+completion = client.chat.completions.create(
+    model="unbias-plus",
+    messages=messages,
+    max_tokens=4096,
+    temperature=0,
+    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
+)
+result = json.loads(completion.choices[0].message.content)
+```
+
+## Output Schema
+
+The model emits exactly three fields. `binary_label` and `bias_found` are **not**
+produced by the model — they are derived downstream from `severity`
+(`severity > 0` ⇒ biased).
+
+```json
+{
+  "severity": 0,
+  "biased_segments": [
+    {
+      "original": "exact substring from input article",
+      "replacement": "neutral alternative phrase (or empty string to delete)",
+      "severity": "Low | Medium | High",
+      "bias_type": "loaded_language | euphemism | dehumanizing_language | opinion_as_fact | unsupported_generalization | stereotypical_association | sensationalism | informational_bias",
+      "reasoning": "short, specific explanation of the linguistic cue"
+    }
+  ],
+  "unbiased_text": "Full rewritten neutral article"
+}
+```
+
+- `severity` is an integer `0–10` (article-level).
+- Each segment's `severity` is a string: `Low`, `Medium`, or `High`.
+- `bias_type` is exactly one of the eight canonical slugs listed below.
+- If `biased_segments` is empty, article `severity` is `0`. If `severity > 0`, there is at least one segment.
+
+### Article-level Severity Scale
+
+| Value  | Meaning                                      |
+| ------ | -------------------------------------------- |
+| 0      | No biased segments                           |
+| 1–5    | Limited, low, or moderate bias               |
+| 6–10   | Strong, recurring, or highly distorting bias |
+
+## Bias Types Detected
+
+V2 assigns exactly one primary `bias_type` per segment, using the following
+label precedence (first applicable label wins):
+
+1. **`dehumanizing_language`** — treats people or groups as less than human, as a threatening mass, or as inherently dangerous (e.g. "vermin", "flood of migrants").
+2. **`stereotypical_association`** — assigns traits, roles, or motives to a demographic/social/political/protected group, links identity to suspicion or wrongdoing, or reduces a group to a single function.
+3. **`sensationalism`** — hyperbolic, alarmist, or dramatic language that inflates significance (e.g. "bombshell", "catastrophe", "sparks fury").
+4. **`opinion_as_fact`** — an unattributed subjective or evaluative judgment presented as fact (e.g. "the policy is a failure").
+5. **`unsupported_generalization`** — a sweeping or absolute claim about a group or situation without support (e.g. "everyone knows", "immigrants always").
+6. **`euphemism`** — softened or vague wording that minimizes, hides, or downplays harmful facts (e.g. "collateral damage", "enhanced interrogation").
+7. **`informational_bias`** — authorial framing that visibly treats one side, source, or claim differently in a way that steers interpretation.
+8. **`loaded_language`** — clearly charged, morally weighted, editorial, toxic, abusive, or inflammatory wording not covered by a more specific type (e.g. "thugs", "so-called reform").
+
+
+## Hardware Requirements
+
+| Setup                | Configuration                                             |
+| -------------------- | --------------------------------------------------------- |
+| Recommended (server) | `torch_dtype=torch.bfloat16, device_map="auto"` (~16GB VRAM) |
+| Lightweight (laptop) | `load_in_4bit=True` (~5GB VRAM)                           |
+
+## Model Variants
+
+| Model | Params | Context | Best for |
+| ----- | ------ | ------- | -------- |
+| [Qwen3.5-4B-UnBias-Plus-SFT-Instruct](https://huggingface.co/vector-institute/Qwen3.5-4B-UnBias-Plus-SFT-Instruct) | 4B | 4096 | Speed, low VRAM |
+| Qwen3-8B-UnBias-Plus-SFT-Instruct-V2 (this) | 8B | 8192 | Higher recall, longer articles, production deployment |
+
+## Citation
+
+```@article{radwan2026unbias,
+  title={UnBias-Plus: Detect, Explain, and Rewrite Bias},
+  author={Radwan, Ahmed Y and ElKady, Ahmed and Chaduvula, Sindhuja and Hafez, Mohamed and Krishnan, Amrit and Raza, Shaina},
+  journal={arXiv preprint arXiv:2606.23412},
+  year={2026}
+}
+```
+
+## Links
+
+- 📊 Leaderboard: [UnBias-Plus Leaderboard](https://huggingface.co/spaces/vector-institute/UnBias-Plus-Leaderboard)
+- 📁 Dataset: [vector-institute/unbias-plus-dataset](https://huggingface.co/datasets/vector-institute/unbias-plus-dataset)
+- 🏛️ Organization: [Vector Institute](https://vectorinstitute.ai)
+- 🌐 Project: [AIXpert](https://aixpert-project.eu/)

chat_template.jinja

@@ -0,0 +1,98 @@
+{%- 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 forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).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' }}
+        {{- message.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": null,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "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": 151669,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.3.5",
+    "use_cache": false,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151669,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.3.0"
+}

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tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7430e9138b76e93fb6f93462394d236b411111aef53cb421ba97d2691040cca
+size 11423114

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",
+  "is_local": false,
+  "model_max_length": 40960,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- set message = messages[index] %}\n    {%- set current_content = message.content if message.content is not none else '' %}\n    {%- set tool_start = '<tool_response>' %}\n    {%- set tool_start_length = tool_start|length %}\n    {%- set start_of_message = current_content[:tool_start_length] %}\n    {%- set tool_end = '</tool_response>' %}\n    {%- set tool_end_length = tool_end|length %}\n    {%- set start_pos = (current_content|length) - tool_end_length %}\n    {%- if start_pos < 0 %}\n        {%- set start_pos = 0 %}\n    {%- endif %}\n    {%- set end_of_message = current_content[start_pos:] %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+}