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Model: 360zhinao/Light-IF-14B Source: Original Platform
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen3-14B
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pipeline_tag: text-generation
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library_name: transformers
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---
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<!-- markdownlint-disable first-line-h1 -->
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<!-- markdownlint-disable html -->
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<!-- markdownlint-disable no-duplicate-header -->
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# Light-IF-14B
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64eeb81ad0ceda46832e0160/b2_eQV04B8xSdYJZnB2FD.png" width="95%" alt="Light-IF-32B" />
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</div>
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<hr>
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<div align="center" style="line-height: 1;">
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🤗 <a href="https://huggingface.co/qihoo360/Light-IF-32B">Hugging Face</a>   |    📑 <a href="https://arxiv.org/abs/2508.03178">Paper Link</a>    |    📑 <a href="https://zhuanlan.zhihu.com/p/1936535948360918628">Blog</a>    |    📑 <a href="https://github.com/Qihoo360/Light-IF">Github</a>    |    📑 <a href="https://mp.weixin.qq.com/s/pcBrykyK99dfWA5WSqj1jw">SuperCLUE-CPIF</a>   
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<br>
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</a>
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</div>
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## Evaluation
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|Model|SuperClue|IFEval|CFBench|IFBench|
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| ---- | ---- | ---- | ---- | ---- |
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|Qwen3-14B|0.227|0.898|0.827|0.422|
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|Qwen3-32B|0.234|0.877|0.823|0.384|
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|Qwen3-235B-A22B|0.244|0.882|0.834|0.423|
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|Qwen3-235B-A22B-Thinking-2507|0.434|0.916|0.843|0.475|
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|DeepSeek-R1-0528|0.436|0.863|0.827|0.415|
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|Doubao-seed-1-6-thinking-250615|0.362|0.832|0.82|0.477|
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|Doubao-seed-1-6-thinking-250715|0.345|0.856|0.84|0.366|
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|ChatGPT-4o-latest|0.260|0.836|0.807|0.365|
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|Deepseek-v3-250324|0.306|0.859|0.833|0.405|
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|Doubao-1.5-pro-32k-250115|0.285|0.889|0.797|0.375|
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|Kimi-K2|0.227|0.921|0.820|0.395|
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|GLM-4.5|0.395|0.893|0.833|0.466|
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| [**Light-IF-14B (ours)** 🤗](https://huggingface.co/qihoo360/Light-IF-14B) |**0.589**|**0.962**|**0.833**|**0.697**|
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## SuperCLUE-CPIF
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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**.
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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.
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<p align="left"></p>
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64eeb81ad0ceda46832e0160/JUB9i0wvj-eROi6bJpbEI.png" alt="" width="600"/>
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</p>
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## Introduction
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**Light-IF-14B** is the most powerful 14B instruction-following model we have open-sourced, even outperforming Light-IF-32B.
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This remarkable performance is largely attributed to our carefully designed curriculum learning strategy.
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During the SFT stage, we increased instruction difficulty; in the two-stage reinforcement learning phase, we introduced even more complex instructions.
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These improvements played a critical role in further boosting the model's capabilities.
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<p align="left"></p>
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64eeb81ad0ceda46832e0160/CPa2Eq6a3o4O9ItzcQgqx.png" alt="" width="600"/>
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</p>
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## Quickstart
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The following contains a code snippet illustrating how to use the model generate content based on given inputs.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "qihoo360/Light-IF-14B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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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。"
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=32768
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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# parsing thinking content
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try:
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# rindex finding 151668 (</think>)
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index = len(output_ids) - output_ids[::-1].index(151668)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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print("thinking content:", thinking_content)
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print("content:", content)
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```
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**thinking content:**
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<div style="max-height:300px; overflow-y:auto; border:1px solid #ccc; padding:10px;">
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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。
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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？
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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。
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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？
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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？
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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。
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接下来，确定主题：
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可能的主题：
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1. 【热门景点】
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2. 【特色美食】
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3. 【交通推荐】
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4. 【住宿建议】
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5. 【亲子活动】
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6. 【摄影打卡】
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7. 【购物优惠】
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8. 【安全提示】
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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。
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例如：
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主题1：热门景点。可能的内容行：
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- 长城、故宫等经典景点人流量大，建议提前预约。
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- 选择早班或错峰出行更省时。
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这样是2行。
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主题2：特色美食。可能：
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- 北京烤鸭、老北京炸酱面是必尝美食。
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- 居庸关长城脚下的农家菜也别有风味。
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这样2行？
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但可能需要调整行数。比如主题2用3行？
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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：
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假设：
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主题1：3行（这样总行数开始是3）
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主题2：3行 → 6
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主题3：3 →9
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主题4：3→12
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主题5：3→15
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主题6：2→17
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主题7：2→19
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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：
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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。
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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：
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需要总行数20行，8个主题，每个主题2-5行。可能的组合：
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比如：
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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。
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另一种方式，每个主题的行数：
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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？
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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。
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这样安排：4个主题3行，4个主题2行。
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现在需要确定主题内容：
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比如：
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主题1：热门景点（3行）
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行1：长城、故宫等经典景点人流量大，建议提前预约。
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行2：选择早班或错峰出行更省时。
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行3：周边小众景点如慕田峪长城人少景美。
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这样3行。
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主题2：特色美食（3行）
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行1：北京烤鸭、老北京炸酱面是必尝美食。
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行2：护城河边的胡同小吃摊位提供地道滋味。
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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},
|
||||||
|
}
|
||||||
|
```
|
||||||
28
added_tokens.json
Normal file
28
added_tokens.json
Normal file
@@ -0,0 +1,28 @@
|
|||||||
|
{
|
||||||
|
"</think>": 151668,
|
||||||
|
"</tool_call>": 151658,
|
||||||
|
"</tool_response>": 151666,
|
||||||
|
"<think>": 151667,
|
||||||
|
"<tool_call>": 151657,
|
||||||
|
"<tool_response>": 151665,
|
||||||
|
"<|box_end|>": 151649,
|
||||||
|
"<|box_start|>": 151648,
|
||||||
|
"<|endoftext|>": 151643,
|
||||||
|
"<|file_sep|>": 151664,
|
||||||
|
"<|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,
|
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||||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
|
||||||
|
size 11422654
|
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241
tokenizer_config.json
Normal file
241
tokenizer_config.json
Normal file
@@ -0,0 +1,241 @@
|
|||||||
|
{
|
||||||
|
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|
||||||
|
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|
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||||||
|
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|
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|
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"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
|
||||||
|
}
|
||||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user