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Model: 360zhinao/Light-IF-14B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-14B
+pipeline_tag: text-generation
+library_name: transformers
+---
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+# Light-IF-14B
+
+<div align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64eeb81ad0ceda46832e0160/b2_eQV04B8xSdYJZnB2FD.png" width="95%" alt="Light-IF-32B" />
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  🤗 <a href="https://huggingface.co/qihoo360/Light-IF-32B">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2508.03178">Paper Link</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://zhuanlan.zhihu.com/p/1936535948360918628">Blog</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://github.com/Qihoo360/Light-IF">Github</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://mp.weixin.qq.com/s/pcBrykyK99dfWA5WSqj1jw">SuperCLUE-CPIF</a> &nbsp&nbsp
+<br>
+  </a>
+</div>
+
+## Evaluation 
+|Model|SuperClue|IFEval|CFBench|IFBench|
+| ---- | ---- | ---- | ---- | ---- |
+|Qwen3-14B|0.227|0.898|0.827|0.422|
+|Qwen3-32B|0.234|0.877|0.823|0.384|
+|Qwen3-235B-A22B|0.244|0.882|0.834|0.423|
+|Qwen3-235B-A22B-Thinking-2507|0.434|0.916|0.843|0.475|
+|DeepSeek-R1-0528|0.436|0.863|0.827|0.415|
+|Doubao-seed-1-6-thinking-250615|0.362|0.832|0.82|0.477|
+|Doubao-seed-1-6-thinking-250715|0.345|0.856|0.84|0.366|
+|ChatGPT-4o-latest|0.260|0.836|0.807|0.365|
+|Deepseek-v3-250324|0.306|0.859|0.833|0.405|
+|Doubao-1.5-pro-32k-250115|0.285|0.889|0.797|0.375|
+|Kimi-K2|0.227|0.921|0.820|0.395|
+|GLM-4.5|0.395|0.893|0.833|0.466|
+| [**Light-IF-14B (ours)** 🤗](https://huggingface.co/qihoo360/Light-IF-14B) |**0.589**|**0.962**|**0.833**|**0.697**| 
+
+
+## SuperCLUE-CPIF
+In the latest SuperCLUE-CPIF evaluation, Light-IF-14B (shown as 360zhinao3-o1.5 in the figure below) reached the domestic **SOTA**, outperforming **ERNIE-X1.1** and **DeepSeek-V3.2-Exp-Thinking**. 
+
+SuperCLUE-CPIF (Chinese Precise Instruction Following) is a benchmark designed to assess how well large language models (LLMs) can accurately follow complex, multi-constraint instructions in Chinese.
+
+<p align="left"></p>
+<p align="left">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64eeb81ad0ceda46832e0160/JUB9i0wvj-eROi6bJpbEI.png" alt="" width="600"/>
+</p>
+
+
+## Introduction
+**Light-IF-14B** is the most powerful 14B instruction-following model we have open-sourced, even outperforming Light-IF-32B. 
+
+This remarkable performance is largely attributed to our carefully designed curriculum learning strategy. 
+
+During the SFT stage, we increased instruction difficulty; in the two-stage reinforcement learning phase, we introduced even more complex instructions. 
+
+These improvements played a critical role in further boosting the model's capabilities.
+
+<p align="left"></p>
+<p align="left">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64eeb81ad0ceda46832e0160/CPa2Eq6a3o4O9ItzcQgqx.png" alt="" width="600"/>
+</p>
+
+## Quickstart
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "qihoo360/Light-IF-14B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+prompt = "帮我写一份国庆节旅游攻略，要求有8个主题，每个主题的小标题用【】显示。每个主题不少于2句，不超过5句（不包含主题）。整个攻略一共是20行（以单个换行符分割，20行不包括主题）。整个攻略不超过300字。"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True 
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+# parsing thinking content
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+**thinking content:**
+<div style="max-height:300px; overflow-y:auto; border:1px solid #ccc; padding:10px;">
+好的，我现在需要帮用户写一份国庆节旅游攻略，满足几个具体要求。首先，需要8个主题，每个主题的小标题用【】显示。然后每个主题的内容要有2到5句（不包含主题）。整个攻略一共20行，用单个换行符分隔，20行不包括主题。还要不超过300字。
+
+首先，我需要确定国庆节常见的旅游主题，比如景点推荐、美食、交通、住宿、注意事项等等。然后每个主题下要有2-5句内容。接下来，计算总行数。8个主题，每个主题可能包含多少行？需要总行数20行，所以每个主题大约2-3行？
+
+但每个主题的小标题是单独一行吗？比如【主题一】作为一行，然后主题内容行数。所以总行数包括主题行吗？用户说“20行（以单个换行符分割，20行不包括主题）”。可能是指不包括主题的小标题行？需要确认。可能用户的意思是整个攻略中，除了主题小标题之外的内容行是20行？或者整个攻略总行数是20行，其中主题小标题算行吗？可能需要仔细分析。
+
+比如，用户说“整个攻略一共是20行（以单个换行符分割，20行不包括主题）”，所以主题的小标题不计入这20行。那需要主题下的内容行数总和是20行？
+
+然后每个主题需要2-5句，也就是每个主题的内容行数是2-5行？需要8个主题，所以总行数是8个主题各自的内容行数之和等于20行。需要分配每个主题的内容行数。比如，有些主题用3行，有些用2行？
+
+比如，假设大部分主题用2行，可能需要8个主题×2行=16行，不够20行，所以需要部分主题用3行。比如4个主题用3行，4个用2行？这样总行数4×3+4×2=12+8=20行。这样分配可能更合理。
+
+接下来，确定主题：
+
+可能的主题：
+
+1. 【热门景点】
+2. 【特色美食】
+3. 【交通推荐】
+4. 【住宿建议】
+5. 【亲子活动】
+6. 【摄影打卡】
+7. 【购物优惠】
+8. 【安全提示】
+
+然后每个主题的内容需要2-5句，不超过300字。现在需要构造每个主题的内容行。
+
+例如：
+
+主题1：热门景点。可能的内容行：
+- 长城、故宫等经典景点人流量大，建议提前预约。
+- 选择早班或错峰出行更省时。
+
+这样是2行。
+
+主题2：特色美食。可能：
+- 北京烤鸭、老北京炸酱面是必尝美食。
+- 居庸关长城脚下的农家菜也别有风味。
+这样2行？
+
+但可能需要调整行数。比如主题2用3行？
+
+需要总行数20行，8个主题，每个主题的内容行数加起来20行。比如：
+
+假设：
+
+主题1：3行（这样总行数开始是3）
+
+主题2：3行 → 6
+
+主题3：3 →9
+
+主题4：3→12
+
+主题5：3→15
+
+主题6：2→17
+
+主题7：2→19
+
+主题8：1→20？不行，每个主题需要至少2句，即至少2行？用户说每个主题不少于2句，不超过5句（不包含主题）。所以每个主题的内容行数至少2行，最多5行。所以主题8必须至少2行，所以前面可能调整：
+
+比如主题1-6各3行，主题7-8各2行？这样总行数：6×3 +2×2=18+4=22，超过了20，需要调整。
+
+比如主题1-5各3行，主题6-8各（20-5×3）=5行？不，这样可能复杂。另一种分配：
+
+需要总行数20行，8个主题，每个主题2-5行。可能的组合：
+
+比如：
+
+主题行数分布：3,3,3,3,2,2,2,3 →总和是3×5+2×3+3=15+6+3=24？不对，可能需要重新计算。
+
+另一种方式，每个主题的行数：
+
+假设5个主题用3行，3个主题用2行。总行数5×3+3×2=15+6=21，接近20，差1行。所以可能调整其中一个主题用2行，其他4个用3行，3个用2行？
+
+比如：4个主题用3行，4个主题用2行？总行数4×3+4×2=12+8=20行。完美。
+
+这样安排：4个主题3行，4个主题2行。
+
+现在需要确定主题内容：
+
+比如：
+
+主题1：热门景点（3行）
+
+行1：长城、故宫等经典景点人流量大，建议提前预约。
+
+行2：选择早班或错峰出行更省时。
+
+行3：周边小众景点如慕田峪长城人少景美。
+
+这样3行。
+
+主题2：特色美食（3行）
+
+行1：北京烤鸭、老北京炸酱面是必尝美食。
+
+行2：护城河边的胡同小吃摊位提供地道滋味。
+
+行3：注意部分餐厅需提前订位。
+
+3行？
+
+主题3：交通推荐（3行）
+
+行1：地铁是出行首选，国庆期间加密班次。
+
+行2：机场快线直达核心景区，节省时间。
+
+行3：共享单车覆盖主要景点，适合短途。
+
+3行。
+
+主题4：住宿建议（3行）
+
+行1：市中心酒店价格较高，建议提前两周预订。
+
+行2：郊区民宿性价比高，还能体验当地文化。
+
+行3：注意查看住宿的防疫要求。
+
+3行。
+
+然后剩下的4个主题用2行：
+
+主题5：亲子活动（2行）
+
+行1：儿童乐园、科技馆有专门国庆活动。
+
+行2：家长可参与亲子手工课程，增进互动。
+
+主题6：摄影打卡（2行）
+
+行1：天安门广场、国家大剧院是热门取景点。
+
+行2：黄昏时分光线柔和，更容易拍出大片。
+
+主题7：购物优惠（2行）
+
+行1：大型商圈有满减活动，适合囤货。
+
+行2：景区周边小店可淘特色纪念品。
+
+主题8：安全提示（2行）
+
+行1：注意保管随身物品，避免人流拥挤时丢失。
+
+行2：备好常用药品，应对突发小状况。
+
+现在检查总行数：
+
+主题1：3行
+
+主题2：3行 →6
+
+主题3：3→9
+
+主题4：3→12
+
+主题5：2→14
+
+主题6：2→16
+
+主题7：2→18
+
+主题8：2→20行。刚好20行，符合要求。
+
+接下来检查每个主题的句子数是否符合2-5句（不包含主题）。每个主题的内容行数即句子数吗？可能需要调整句子是否完整，每个行是一句话吗？是的，用户可能认为每行是句子。所以每个主题的内容行数等于句子数吗？
+
+比如主题1的3行是3个句子？这样主题1有3句，符合2-5句。
+
+现在需要确保总字数不超过300字。
+
+现在开始写具体内容，计算字数：
+
+主题1：
+
+1. 长城、故宫等经典景点人流量大，建议提前预约。（约25字）
+
+2. 选择早班或错峰出行更省时。（约16字）
+
+3. 周边小众景点如慕田峪长城人少景美。（约20字）→总约25+16+20=61字
+
+主题2：
+
+1. 北京烤鸭、老北京炸酱面是必尝美食。（约18字）
+
+2. 护城河边的胡同小吃摊位提供地道滋味。（约19字）
+
+3. 注意部分餐厅需提前订位。（约12字）→总约18+19+12=49字
+
+主题3：
+
+1. 地铁是出行首选，国庆期间加密班次。（约17字）
+
+2. 机场快线直达核心景区，节省时间。（约16字）
+
+3. 共享单车覆盖主要景点，适合短途。（约16字）→总17+16+16=49字
+
+主题4：
+
+1. 市中心酒店价格较高，建议提前两周预订。（约20字）
+
+2. 郊区民宿性价比高，还能体验当地文化。（约21字）
+
+3. 注意查看住宿的防疫要求。（约13字）→总20+21+13=54字
+
+主题5：
+
+1. 儿童乐园、科技馆有专门国庆活动。（约17字）
+
+2. 家长可参与亲子手工课程，增进互动。（约16字）→33字
+
+主题6：
+
+1. 天安门广场、国家大剧院是热门取景点。（约17字）
+
+2. 黄昏时分光线柔和，更容易拍出大片。（约16字）→33字
+
+主题7：
+
+1. 大型商圈有满减活动，适合囤货。（约15字）
+
+2. 景区周边小店可淘特色纪念品。（约14字）→29字
+
+主题8：
+
+1. 注意保管随身物品，避免人流拥挤时丢失。（约20字）
+
+2. 备好常用药品，应对突发小状况。（约14字）→34字
+
+现在总字数：61+49+49+54+33+33+29+34=392字？超过了300字，不符合要求。
+
+需要调整，减少字数。可能需要缩短句子。
+
+比如主题1的第3句：“周边小众景点如慕田峪人少景美。” 这样减少“长城”两字？或者调整句子结构。
+
+需要总字数不超过300，所以需要缩短每个主题的内容。
+
+可能调整如下：
+
+主题1：
+
+1. 长城、故宫人流量大，建议提前预约。
+
+2. 早班或错峰出行更省时。
+
+3. 慕田峪等小景点人少景美。
+
+这样3行，字数约：18+13+13=44字？
+
+主题2：
+
+1. 北京烤鸭是必尝美食。
+
+2. 胡同小吃摊位有地道滋味。
+
+3. 部分餐厅需提前订位。
+
+3行，约：9+11+8=28字？
+
+主题3：
+
+1. 地铁是出行首选，国庆加密班次。
+
+2. 机场快线直达景区，节省时间。
+
+3. 共享单车覆盖景点，方便短途。
+
+3行，约：16+15+12=43字？
+
+主题4：
+
+1. 市中心酒店价格高，建议提前预订。
+
+2. 郊区民宿性价比高，体验当地文化。
+
+3. 注意查看防疫要求。
+
+3行，约：16+18+8=42字？
+
+主题5：
+
+1. 儿童乐园、科技馆有国庆活动。
+
+2. 家长可参与亲子课程，增进互动。
+
+2行，约：14+13=27字？
+
+主题6：
+
+1. 天安门广场、国家大剧院是热门景点。
+
+2. 黄昏光线柔和，适合拍照。
+
+2行，约：17+10=27字？
+
+主题7：
+
+1. 大型商圈有满减，适合购物。
+
+2. 景区小店可淘纪念品。
+
+2行，约：10+11=21字？
+
+主题8：
+
+1. 注意保管物品，避免丢失。
+
+2. 备好常用药品应对突发状况。
+
+2行，约：12+12=24字？
+
+现在总字数：
+
+主题1:44
+
+主题2:28
+
+主题3:43
+
+主题4:42
+
+主题5:27
+
+主题6:27
+
+主题7:21
+
+主题8:24 →44+28=72, +43=115, +42=157, +27=184, +27=211, +21=232, +24=256字，符合300以内。
+
+现在需要调整句子更简短，确保每个主题内容符合要求。
+
+现在具体写：
+
+【热门景点】
+
+长城、故宫人流量大，建议提前预约。
+
+早班或错峰出行更省时。
+
+慕田峪等小景点人少景美。
+
+（3行）
+
+【特色美食】
+
+北京烤鸭是必尝美食。
+
+胡同小吃摊位有地道滋味。
+
+部分餐厅需提前订位。
+
+（3行）
+
+【交通推荐】
+
+地铁是出行首选，国庆加密班次。
+
+机场快线直达景区，节省时间。
+
+共享单车覆盖景点，方便短途。
+
+（3行）
+
+【住宿建议】
+
+市中心酒店价格高，建议提前预订。
+
+郊区民宿性价比高，体验当地文化。
+
+注意查看防疫要求。
+
+（3行）
+
+【亲子活动】
+
+儿童乐园、科技馆有国庆活动。
+
+家长可参与亲子课程，增进互动。
+
+（2行）→这里之前是2行？但需要主题行数是2行吗？前面分配是主题5用2行，是的。
+
+【摄影打卡】
+
+天安门广场、国家大剧院是热门景点。
+
+黄昏光线柔和，适合拍照。
+
+（2行）
+
+【购物优惠】
+
+大型商圈有满减，适合购物。
+
+景区小店可淘纪念品。
+
+（2行）
+
+【安全提示】
+
+注意保管物品，避免丢失。
+
+备好常用药品应对突发状况。
+
+（2行）
+
+现在检查总行数：
+
+主题小标题行不算，内容行数：
+
+主题1：3行 →行1-3
+
+主题2：3 →4-6
+
+主题3：3→7-9
+
+主题4：3→10-12
+
+主题5：2→13-14
+
+主题6：2→15-16
+
+主题7：2→17-18
+
+主题8：2→19-20
+
+刚好20行？是的。现在检查每个主题的句子数是否符合2-5句。每个主题的内容行数是：
+
+主题1：3句（符合）
+
+主题2：3句（符合）
+
+主题3：3句（符合）
+
+主题4：3句（符合）
+
+主题5：2句（符合）
+
+主题6：2句（符合）
+
+主题7：2句（符合）
+
+主题8：2句（符合）
+
+这样满足每个主题不少于2句，不超过5句。
+
+现在检查总字数是否不超过300字。之前估算大约256字，符合要求。
+
+可能需要调整部分句子，比如“国庆加密班次”是否通顺？“地铁是出行首选，国庆加密班次。” 这样没问题。
+
+现在再检查：
+
+主题1：
+
+1. 长城、故宫人流量大，建议提前预约。（25字？可能更短）
+
+可能需要更简短的句子：
+
+比如“长城、故宫人流量大，需预约。” 这样更简短？
+
+调整后：
+
+主题1：
+
+长城、故宫人流量大，需预约。
+
+早班或错峰出行更省时。
+
+慕田峪等小景点人少景美。
+
+这样字数更少，总字数会减少。
+
+可能这样调整：
+
+主题1：
+
+1. 长城、故宫人流量大，需预约。（16字）
+
+2. 早班或错峰出行更省时。（13字）
+
+3. 慕田峪等小景点人少景美。（15字）→总44字？
+
+可能这样更合适。
+
+现在最终调整：
+
+【热门景点】
+
+长城、故宫人流量大，需预约。
+
+早班或错峰出行更省时。
+
+慕田峪等小景点人少景美。
+
+【特色美食】
+
+北京烤鸭是必尝美食。
+
+胡同小吃摊位有地道滋味。
+
+部分餐厅需提前订位。
+
+【交通推荐】
+
+地铁是出行首选，国庆加密班次。
+
+机场快线直达景区，节省时间。
+
+共享单车覆盖景点，方便短途。
+
+【住宿建议】
+
+市中心酒店价格高，建议提前预订。
+
+郊区民宿性价比高，体验当地文化。
+
+注意查看防疫要求。
+
+【亲子活动】
+
+儿童乐园、科技馆有国庆活动。
+
+家长可参与亲子课程，增进互动。
+
+【摄影打卡】
+
+天安门广场、国家大剧院是热门景点。
+
+黄昏光线柔和，适合拍照。
+
+【购物优惠】
+
+大型商圈有满减，适合购物。
+
+景区小店可淘纪念品。
+
+【安全提示】
+
+注意保管物品，避免丢失。
+
+备好常用药品应对突发状况。
+
+现在检查行数是否20行：
+
+每个主题的内容行：
+
+主题1：3行
+
+主题2：3行 →累计6
+
+主题3：3→9
+
+主题4：3→12
+
+主题5：2→14
+
+主题6：2→16
+
+主题7：2→18
+
+主题8：2→20
+
+是的，刚好20行。
+
+现在检查字数：
+
+大致估算：
+
+主题1: 16+13+15=44
+
+主题2: 9+11+8=28（北京烤鸭是必尝美食→9字？可能需要重新计算字数。）
+
+可能我之前估算方式不太准确，但总字数应该在300以内。例如：
+
+每个行的大致字数：
+
+主题1:
+
+1. 长城、故宫人流量大，需预约。→17字（长城、2，故宫2，人流量大4，需预约3→17？）
+
+可能更准确的方式是数每个句子的字数：
+
+例如：
+
+1. 长城、故宫人流量大，需预约。→17字
+
+2. 早班或错峰出行更省时。→13字
+
+3. 慕田峪等小景点人少景美。→14字（慕田峪等小景点人少景美：慕田峪3，等1，小景点3，人少2，景美2→3+1+3+2+2=11？可能我数的方式有问题，但大概总字数不会超过300。）
+
+不管怎样，现在需要确保总字数不超过300。可能现在这样写是符合要求的。
+
+现在确认：
+
+所有内容行：
+
+行1：长城、故宫人流量大，需预约。
+
+行2：早班或错峰出行更省时。
+
+行3：慕田峪等小景点人少景美。
+
+行4：北京烤鸭是必尝美食。
+
+行5：胡同小吃摊位有地道滋味。
+
+行6：部分餐厅需提前订位。
+
+行7：地铁是出行首选，国庆加密班次。
+
+行8：机场快线直达景区，节省时间。
+
+行9：共享单车覆盖景点，方便短途。
+
+行10：市中心酒店价格高，建议提前预订。
+
+行11：郊区民宿性价比高，体验当地文化。
+
+行12：注意查看防疫要求。
+
+行13：儿童乐园、科技馆有国庆活动。
+
+行14：家长可参与亲子课程，增进互动。
+
+行15：天安门广场、国家大剧院是热门景点。
+
+行16：黄昏光线柔和，适合拍照。
+
+行17：大型商圈有满减，适合购物。
+
+行18：景区小店可淘纪念品。
+
+行19：注意保管物品，避免丢失。
+
+行20：备好常用药品应对突发状况。
+
+现在数一下总字数：
+
+行1：17
+
+行2：13 →30
+
+行3：14→44
+
+行4：9→53（北京烤鸭是必尝美食→9字？“北京烤鸭是必尝美食”共9字？可能需要重新数：
+
+“北京烤鸭是必尝美食”→北京（2）烤鸭（2）是（1）必尝（2）美食（2）→2+2+1+2+2=9？
+
+行5：“胡同小吃摊位有地道滋味。”→胡同（2）小吃（2）摊位（2）有（1）地道（2）滋味（2）→2+2+2+1+2+2？不，句子是“胡同小吃摊位有地道滋味。” 共11字？
+
+可能我之前的方法不太对，但无论如何，总字数大概在20行×平均15字=300字左右？需要更准确计算吗？
+
+可能现在不需要精确到每个字，只要不超过300即可。根据之前的调整，应该没问题。
+
+现在确认每个主题的小标题用【】显示，所以最终的攻略结构是：
+
+【热门景点】
+
+长城、故宫人流量大，需预约。
+
+早班或错峰出行更省时。
+
+慕田峪等小景点人少景美。
+
+【特色美食】
+
+北京烤鸭是必尝美食。
+
+胡同小吃摊位有地道滋味。
+
+部分餐厅需提前订位。
+
+【交通推荐】
+
+地铁是出行首选，国庆加密班次。
+
+机场快线直达景区，节省时间。
+
+共享单车覆盖景点，方便短途。
+
+【住宿建议】
+
+市中心酒店价格高，建议提前预订。
+
+郊区民宿性价比高，体验当地文化。
+
+注意查看防疫要求。
+
+【亲子活动】
+
+儿童乐园、科技馆有国庆活动。
+
+家长可参与亲子课程，增进互动。
+
+【摄影打卡】
+
+天安门广场、国家大剧院是热门景点。
+
+黄昏光线柔和，适合拍照。
+
+【购物优惠】
+
+大型商圈有满减，适合购物。
+
+景区小店可淘纪念品。
+
+【安全提示】
+
+注意保管物品，避免丢失。
+
+备好常用药品应对突发状况。
+
+现在检查是否符合所有要求：
+
+- 8个主题，小标题用【】显示：是。
+
+- 每个主题不少于2句，不超过5句：每个主题的内容行数是3或2，符合。
+
+- 整个攻略一共20行（不包括主题）：是的，20行。
+
+- 不超过300字：大概估算：
+
+行1-3：主题1的3行，约17+13+14=44字
+
+行4-6：主题2的3行，约9+11+10=30字（假设部分餐厅需提前订位→10字）
+
+行7-9：主题3的3行，约16+12+12=40字（地铁是出行首选，国庆加密班次→16字？可能需要更准确：地铁是出行首选，国庆加密班次→14字？）
+
+不管怎样，总字数大概在44+30+40+40+30+30+20+20=244字？应该远低于300，符合要求。
+
+现在可能需要调整部分句子，使更流畅。
+
+比如主题4的“注意查看防疫要求。” 是一句，没问题。
+
+主题7的“大型商圈有满减，适合购物。” 是一句。
+
+现在确认所有内容符合要求，没有问题。
+
+这样应该满足用户的所有要求了。
+</div>
+
+**content:**
+<div style="max-height:300px; overflow-y:auto; border:1px solid #ccc; padding:10px;">
+【热门景点】
+
+长城、故宫人流量大，需预约。
+
+早班或错峰出行更省时。
+
+慕田峪等小景点人少景美。
+
+【特色美食】
+
+北京烤鸭是必尝美食。
+
+胡同小吃摊位有地道滋味。
+
+部分餐厅需提前订位。
+
+【交通推荐】
+
+地铁是出行首选，国庆加密班次。
+
+机场快线直达景区，节省时间。
+
+共享单车覆盖景点，方便短途。
+
+【住宿建议】
+
+市中心酒店价格高，建议提前预订。
+
+郊区民宿性价比高，体验当地文化。
+
+注意查看防疫要求。
+
+【亲子活动】
+
+儿童乐园、科技馆有国庆活动。
+
+家长可参与亲子课程，增进互动。
+
+【摄影打卡】
+
+天安门广场、国家大剧院是热门景点。
+
+黄昏光线柔和，适合拍照。
+
+【购物优惠】
+
+大型商圈有满减，适合购物。
+
+景区小店可淘纪念品。
+
+【安全提示】
+
+注意保管物品，避免丢失。
+
+备好常用药品应对突发状况。
+</div>
+
+For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint.
+
+## Citation
+```
+@misc{lightifproj,
+      title={Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following}, 
+      author={Chenyang Wang, Liang Wen, Shousheng Jia, Xiangzheng Zhang, Liang Xu},
+      year={2025},
+      eprint={2508.03178},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.03178}, 
+}
+```

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config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17408,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 40,
+  "model_type": "qwen3",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
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+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.51.3"
+}

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+    "model.layers.9.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
+    "model.layers.9.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
+    "model.norm.weight": "model-00006-of-00006.safetensors"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_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|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,241 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_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|>"
+  ],
+  "bos_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 message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\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>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].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 %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}