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README.md

@@ -1,199 +1,249 @@
 ---
+language:
+- en
+- zh
 library_name: transformers
-tags: []
+license: mit
+pipeline_tag: text-generation
+tags:
+- heretic
+- uncensored
+- decensored
+- abliterated
 ---
+# This is a decensored version of [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0+custom
 
-# Model Card for Model ID
+## Abliteration parameters
 
-<!-- Provide a quick summary of what the model is/does. -->
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | per layer |
+| **attn.o_proj.max_weight** | 1.84 |
+| **attn.o_proj.max_weight_position** | 49.16 |
+| **attn.o_proj.min_weight** | 1.64 |
+| **attn.o_proj.min_weight_distance** | 26.42 |
+| **mlp.down_proj.max_weight** | 1.02 |
+| **mlp.down_proj.max_weight_position** | 53.46 |
+| **mlp.down_proj.min_weight** | 0.97 |
+| **mlp.down_proj.min_weight_distance** | 45.98 |
 
+## Performance
 
+| Metric | This model | Original model ([zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0748 | 0 *(by definition)* |
+| **Refusals** | 0/100 | 99/100 |
 
-## Model Details
+-----
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+# GLM-4.7
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
+</div>
+<p align="center">
+    👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
+    <br>
+    📖 Check out the GLM-4.7 <a href="https://z.ai/blog/glm-4.7" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report(GLM-4.5)</a>.
+    <br>
+    📍 Use GLM-4.7 API services on <a href="https://docs.z.ai/guides/llm/glm-4.7">Z.ai API Platform. </a>
+    <br>
+    👉 One click to <a href="https://chat.z.ai">GLM-4.7</a>.
+</p>
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Introduction
 
-### Model Sources [optional]
+**GLM-4.7**, your new coding partner, is coming with the following features:
 
-<!-- Provide the basic links for the model. -->
+- **Core Coding**: GLM-4.7 brings clear gains, compared to its predecessor GLM-4.6, in multilingual agentic coding and terminal-based tasks, including (73.8%, +5.8%) on SWE-bench, (66.7%, +12.9%) on SWE-bench Multilingual, and (41%, +16.5%) on Terminal Bench 2.0. GLM-4.7 also supports thinking before acting, with significant improvements on complex tasks in mainstream agent frameworks such as Claude Code, Kilo Code, Cline, and Roo Code.
+- **Vibe Coding**: GLM-4.7 takes a big step forward in improving UI quality. It produces cleaner, more modern webpages and generates better-looking slides with more accurate layout and sizing.
+- **Tool Using**: GLM-4.7 achieves significantly improvements in Tool using. Significant better performances can be seen on benchmarks such as τ^2-Bench and on web browsing via BrowseComp.
+- **Complex Reasoning**: GLM-4.7 delivers a substantial boost in mathematical and reasoning capabilities, achieving (42.8%, +12.4%) on the HLE (Humanity’s Last Exam) benchmark compared to GLM-4.6.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+You can also see significant improvements in many other scenarios such as chat, creative writing, and role-play scenario.
 
-## Uses
+![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench_glm47.png)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**Performances on Benchmarks.** More detailed comparisons of GLM-4.7 with other models GPT-5-High, GPT-5.1-High, Claude Sonnet 4.5, Gemini 3.0 Pro, DeepSeek-V3.2, Kimi K2 Thinking, on 17 benchmarks (including 8 reasoning, 5 coding, and 3 agents benchmarks) can be seen in the below table.
 
-### Direct Use
+| Benchmark                      | GLM-4.7 | GLM-4.6 | Kimi K2 Thinking | DeepSeek-V3.2 | Gemini 3.0 Pro | Claude Sonnet 4.5 | GPT-5-High | GPT-5.1-High |
+|:-------------------------------|:-------:|:-------:|:----------------:|:-------------:|:--------------:|:-----------------:|:----------:|:------------:|
+| MMLU-Pro                       |  84.3   |  83.2   |       84.6       |     85.0      |      90.1      |       88.2        |    87.5    |     87.0     |
+| GPQA-Diamond                   |  85.7   |  81.0   |       84.5       |     82.4      |      91.9      |       83.4        |    85.7    |     88.1     |
+| HLE                            |  24.8   |  17.2   |       23.9       |     25.1      |      37.5      |       13.7        |    26.3    |     25.7     |
+| HLE (w/ Tools)                 |  42.8   |  30.4   |       44.9       |     40.8      |      45.8      |       32.0        |    35.2    |     42.7     |
+| AIME 2025                      |  95.7   |  93.9   |       94.5       |     93.1      |      95.0      |       87.0        |    94.6    |     94.0     |
+| HMMT Feb. 2025                 |  97.1   |  89.2   |       89.4       |     92.5      |      97.5      |       79.2        |    88.3    |     96.3     |
+| HMMT Nov. 2025                 |  93.5   |  87.7   |       89.2       |     90.2      |      93.3      |       81.7        |    89.2    |      -       |
+| IMOAnswerBench                 |  82.0   |  73.5   |       78.6       |     78.3      |      83.3      |       65.8        |    76.0    |      -       |
+| LiveCodeBench-v6               |  84.9   |  82.8   |       83.1       |     83.3      |      90.7      |       64.0        |    87.0    |     87.0     |
+| SWE-bench Verified             |  73.8   |  68.0   |       71.3       |     73.1      |      76.2      |       77.2        |    74.9    |     76.3     |
+| SWE-bench Multilingual         |  66.7   |  53.8   |       61.1       |     70.2      |       -        |       68.0        |    55.3    |      -       |
+| Terminal Bench Hard            |  33.3   |  23.6   |       30.6       |   35.4   |      39.0      |       33.3        |    30.5    |     43.0     |
+| Terminal Bench 2.0             |  41.0   |  24.5   |       35.7       |     46.4      |      54.2      |       42.8        |    35.2    |     47.6     |
+| BrowseComp                     |  52.0   |  45.1   |        -         |     51.4      |       -        |       24.1        |    54.9    |     50.8     |
+| BrowseComp (w/ Context Manage) |  67.5   |  57.5   |       60.2       |     67.6      |      59.2      |         -         |     -      |      -       |
+| BrowseComp-Zh                  |  66.6   |  49.5   |       62.3       |     65.0      |       -        |       42.4        |    63.0    |      -       |
+| τ²-Bench                       |  87.4   |  75.2   |       74.3       |     85.3      |      90.7      |       87.2        |    82.4    |     82.7     |
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+> **Coding:** AGI is a long journey, and benchmarks are only one way to evaluate performance. While the metrics provide necessary checkpoints, the most important thing is still how it *feels*. True intelligence isn't just about acing a test or processing data faster; ultimately, the success of AGI will be measured by how seamlessly it integrates into our lives-**"coding"** this time.
 
-[More Information Needed]
 
-### Downstream Use [optional]
+## Getting started with GLM-4.7
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Interleaved Thinking & Preserved Thinking
 
-[More Information Needed]
+![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/thinking.png)
 
-### Out-of-Scope Use
+GLM-4.7 further enhances **Interleaved Thinking** (a feature introduced since GLM-4.5) and introduces **Preserved Thinking** and **Turn-level Thinking**. By thinking between actions and staying consistent across turns, it makes complex tasks more stable and more controllable:
+- **Interleaved Thinking**: The model thinks before every response and tool calling, improving instruction following and the quality of generation.
+- **Preserved Thinking**: In coding agent scenarios, the model automatically retains all thinking blocks across multi-turn conversations, reusing the existing reasoning instead of re-deriving from scratch. This reduces information loss and inconsistencies, and is well-suited for long-horizon, complex tasks.
+- **Turn-level Thinking**: The model supports per-turn control over reasoning within a session—disable thinking for lightweight requests to reduce latency/cost, enable it for complex tasks to improve accuracy and stability.
+ 
+More details: https://docs.z.ai/guides/capabilities/thinking-mode
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Evaluation Parameters
 
-[More Information Needed]
+**Default Settings (Most Tasks)**
 
-## Bias, Risks, and Limitations
+* temperature: `1.0`
+* top-p: `0.95`
+* max new tokens: `131072`
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+For multi-turn agentic tasks (τ²-Bench and Terminal Bench 2), please turn on [Preserved Thinking mode](https://docs.z.ai/guides/capabilities/thinking-mode).
 
-[More Information Needed]
+**Terminal Bench, SWE Bench Verified**
 
-### Recommendations
+* temperature: `0.7`
+* top-p: `1.0`
+* max new tokens: `16384`
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+**τ^2-Bench**
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+* Temperature: `0`
+* Max new tokens: `16384`
 
-## How to Get Started with the Model
+For τ^2-Bench evaluation, we added an additional prompt to the Retail and Telecom user interaction to avoid failure modes caused by users ending the interaction incorrectly. For the Airline domain, we applied the domain fixes as proposed in the [Claude Opus 4.5](https://assets.anthropic.com/m/64823ba7485345a7/Claude-Opus-4-5-System-Card.pdf) release report.
 
-Use the code below to get started with the model.
+## Serve GLM-4.7 Locally
 
-[More Information Needed]
+For local deployment, GLM-4.7 supports inference frameworks including vLLM and SGLang. Comprehensive deployment instructions are available in the official [Github](https://github.com/zai-org/GLM-4.5) repository.
 
-## Training Details
 
-### Training Data
+vLLM and SGLang only support GLM-4.7 on their main branches. you can use their official docker images for inference.
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+### vLLM
 
-[More Information Needed]
+Using Docker as:
 
-### Training Procedure
+```shell
+docker pull vllm/vllm-openai:nightly 
+```
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+or using pip (must use pypi.org as the index url):
 
-#### Preprocessing [optional]
+```shell
+pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
+```
 
-[More Information Needed]
+### SGLang
 
+Using Docker as:
 
-#### Training Hyperparameters
+```shell
+docker pull lmsysorg/sglang:dev
+```
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+or using pip install sglang from source.
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### transformers
 
-[More Information Needed]
+using with transformers as `4.57.3` and then run:
 
-## Evaluation
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_PATH = "zai-org/GLM-4.7"
+messages = [{"role": "user", "content": "hello"}]
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+    return_tensors="pt",
+)
+model = AutoModelForCausalLM.from_pretrained(
+    pretrained_model_name_or_path=MODEL_PATH,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+inputs = inputs.to(model.device)
+generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1] :])
+print(output_text)
+```
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+### vLLM
 
-### Testing Data, Factors & Metrics
+```shell
+vllm serve zai-org/GLM-4.7-FP8 \
+     --tensor-parallel-size 4 \
+     --speculative-config.method mtp \
+     --speculative-config.num_speculative_tokens 1 \
+     --tool-call-parser glm47 \
+     --reasoning-parser glm45 \
+     --enable-auto-tool-choice \
+     --served-model-name glm-4.7-fp8
+```
 
-#### Testing Data
+### SGLang
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```shell
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.7-FP8 \
+  --tp-size 8 \
+  --tool-call-parser glm47  \
+  --reasoning-parser glm45 \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 3 \
+  --speculative-eagle-topk 1 \
+  --speculative-num-draft-tokens 4 \
+  --mem-fraction-static 0.8 \
+  --served-model-name glm-4.7-fp8 \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
+### Parameter Instructions
+
+- For agentic tasks of GLM-4.7, please turn on [Preserved Thinking mode](https://docs.z.ai/guides/capabilities/thinking-mode) by adding the following config (only sglang support):
+  
+  ```
+    "chat_template_kwargs": {
+        "enable_thinking": true,
+        "clear_thinking": false
+    }
+    ```
+    
+- When using `vLLM` and `SGLang`, thinking mode is enabled by default when sending requests. If you want to disable the thinking switch, you need to add the `"chat_template_kwargs": {"enable_thinking": False}` parameter.
+- Both support tool calling. Please use OpenAI-style tool description format for calls.
+
+
+## Citation
+
+If you find our work useful in your research, please consider citing the following paper:
+
+```bibtex
+@misc{5team2025glm45agenticreasoningcoding,
+      title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models}, 
+      author={GLM Team and Aohan Zeng and Xin Lv and Qinkai Zheng and Zhenyu Hou and Bin Chen and Chengxing Xie and Cunxiang Wang and Da Yin and Hao Zeng and Jiajie Zhang and Kedong Wang and Lucen Zhong and Mingdao Liu and Rui Lu and Shulin Cao and Xiaohan Zhang and Xuancheng Huang and Yao Wei and Yean Cheng and Yifan An and Yilin Niu and Yuanhao Wen and Yushi Bai and Zhengxiao Du and Zihan Wang and Zilin Zhu and Bohan Zhang and Bosi Wen and Bowen Wu and Bowen Xu and Can Huang and Casey Zhao and Changpeng Cai and Chao Yu and Chen Li and Chendi Ge and Chenghua Huang and Chenhui Zhang and Chenxi Xu and Chenzheng Zhu and Chuang Li and Congfeng Yin and Daoyan Lin and Dayong Yang and Dazhi Jiang and Ding Ai and Erle Zhu and Fei Wang and Gengzheng Pan and Guo Wang and Hailong Sun and Haitao Li and Haiyang Li and Haiyi Hu and Hanyu Zhang and Hao Peng and Hao Tai and Haoke Zhang and Haoran Wang and Haoyu Yang and He Liu and He Zhao and Hongwei Liu and Hongxi Yan and Huan Liu and Huilong Chen and Ji Li and Jiajing Zhao and Jiamin Ren and Jian Jiao and Jiani Zhao and Jianyang Yan and Jiaqi Wang and Jiayi Gui and Jiayue Zhao and Jie Liu and Jijie Li and Jing Li and Jing Lu and Jingsen Wang and Jingwei Yuan and Jingxuan Li and Jingzhao Du and Jinhua Du and Jinxin Liu and Junkai Zhi and Junli Gao and Ke Wang and Lekang Yang and Liang Xu and Lin Fan and Lindong Wu and Lintao Ding and Lu Wang and Man Zhang and Minghao Li and Minghuan Xu and Mingming Zhao and Mingshu Zhai and Pengfan Du and Qian Dong and Shangde Lei and Shangqing Tu and Shangtong Yang and Shaoyou Lu and Shijie Li and Shuang Li and Shuang-Li and Shuxun Yang and Sibo Yi and Tianshu Yu and Wei Tian and Weihan Wang and Wenbo Yu and Weng Lam Tam and Wenjie Liang and Wentao Liu and Xiao Wang and Xiaohan Jia and Xiaotao Gu and Xiaoying Ling and Xin Wang and Xing Fan and Xingru Pan and Xinyuan Zhang and Xinze Zhang and Xiuqing Fu and Xunkai Zhang and Yabo Xu and Yandong Wu and Yida Lu and Yidong Wang and Yilin Zhou and Yiming Pan and Ying Zhang and Yingli Wang and Yingru Li and Yinpei Su and Yipeng Geng and Yitong Zhu and Yongkun Yang and Yuhang Li and Yuhao Wu and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yuxuan Zhang and Zezhen Liu and Zhen Yang and Zhengda Zhou and Zhongpei Qiao and Zhuoer Feng and Zhuorui Liu and Zichen Zhang and Zihan Wang and Zijun Yao and Zikang Wang and Ziqiang Liu and Ziwei Chai and Zixuan Li and Zuodong Zhao and Wenguang Chen and Jidong Zhai and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang},
+      year={2025},
+      eprint={2508.06471},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.06471}, 
+}