Improve model card with detailed quick start, system requirements, and paper link clarification

This pull request significantly enhances the model card by incorporating:
* **Detailed 'Quick Start' instructions**: Including specific commands and usage examples for `transformers`, `vLLM`, and `SGLang` frameworks, directly from the official GitHub repository.
* **Comprehensive 'System Requirements'**: Adding information on GPU configurations needed for both inference and fine-tuning, also sourced from the GitHub repository.
* **Corrected Paper Link Statement**: Updating the outdated phrase "The technical report will be released soon" to accurately reflect that the technical report is available on arXiv.
* **Model Downloads Section**: Adding a table with links to different model versions.

These additions make the Hugging Face model card more informative and user-friendly, aligning with the goal of thoroughly documenting AI artifacts.

README.md

@@ -1,45 +1,186 @@
----
-license: mit
-language:
-- en
-- zh
-pipeline_tag: text-generation
-library_name: transformers
----
-
-# GLM-4.5
-
-<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-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</a>.
-    <br>
-    📍 Use GLM-4.5 API services on <a href="https://docs.z.ai/guides/llm/glm-4.5">Z.ai API Platform (Global)</a> or <br> <a href="https://docs.bigmodel.cn/cn/guide/models/text/glm-4.5">Zhipu AI Open Platform (Mainland China)</a>.
-    <br>
-    👉 One click to <a href="https://chat.z.ai">GLM-4.5</a>.
-</p>
-  
-## Model Introduction
-
-The **GLM-4.5** series models are foundation models designed for intelligent agents. GLM-4.5 has **355** billion total parameters with **32** billion active parameters, while GLM-4.5-Air adopts a more compact design with **106** billion total parameters and **12** billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
-
-Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
-
-We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.
-
-As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of **63.2**, in the **3rd** place among all the proprietary and open-source  models. Notably, GLM-4.5-Air delivers competitive results at **59.8** while maintaining superior efficiency.
-
-![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench.png)
-
-For more eval results, show cases, and technical details, please visit
-our [technical blog](https://z.ai/blog/glm-4.5). The technical report will be released soon.
-
-
-The model code, tool parser and reasoning parser can be found in the implementation of [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4_moe), [vLLM](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/glm4_moe_mtp.py) and [SGLang](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/glm4_moe.py).
-
-## Quick Start
-
-Please refer our [github page](https://github.com/zai-org/GLM-4.5) for more detail.
+---
+language:
+- en
+- zh
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+---
+
+# GLM-4.5
+
+<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-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</a>.
+    <br>
+    📍 Use GLM-4.5 API services on <a href="https://docs.z.ai/guides/llm/glm-4.5">Z.ai API Platform (Global)</a> or <br> <a href="https://docs.bigmodel.cn/cn/guide/models/text/glm-4.5">Zhipu AI Open Platform (Mainland China)</a>.
+    <br>
+    👉 One click to <a href="https://chat.z.ai">GLM-4.5</a>.
+</p>
+  
+## Model Introduction
+
+The **GLM-4.5** series models are foundation models designed for intelligent agents. GLM-4.5 has **355** billion total parameters with **32** billion active parameters, while GLM-4.5-Air adopts a more compact design with **106** billion total parameters and **12** billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
+
+Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
+
+We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.
+
+As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of **63.2**, in the **3rd** place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at **59.8** while maintaining superior efficiency.
+
+![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench.png)
+
+For more eval results, show cases, and technical details, please visit
+our [technical blog](https://z.ai/blog/glm-4.5) or [technical report](https://arxiv.org/abs/2508.06471).
+
+The model code, tool parser and reasoning parser can be found in the implementation of [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4_moe), [vLLM](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/glm4_moe_mtp.py) and [SGLang](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/glm4_moe.py).
+
+## Model Downloads
+
+You can directly experience the model on [Hugging Face](https://huggingface.co/spaces/zai-org/GLM-4.5-Space)
+or [ModelScope](https://modelscope.cn/studios/ZhipuAI/GLM-4.5-Demo) or download the model by following the links below.
+
+| Model            | Download Links                                                                                                                                | Model Size | Precision |
+|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|------------|-----------|
+| GLM-4.5          | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5)                   | 355B-A32B  | BF16      |
+| GLM-4.5-Air      | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air)           | 106B-A12B  | BF16      |
+| GLM-4.5-FP8      | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-FP8)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-FP8)           | 355B-A32B  | FP8       |
+| GLM-4.5-Air-FP8  | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air-FP8)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air-FP8)   | 106B-A12B  | FP8       |
+| GLM-4.5-Base     | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Base)         | 355B-A32B  | BF16      |
+| GLM-4.5-Air-Base | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air-Base) | 106B-A12B  | BF16      |
+
+## System Requirements
+
+### Inference
+
+We provide minimum and recommended configurations for "full-featured" model inference. The data in the table below is
+based on the following conditions:
+
+1. All models use MTP layers and specify
+   `--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4` to ensure competitive
+   inference speed.
+2. The `cpu-offload` parameter is not used.
+3. Inference batch size does not exceed `8`.
+4. All are executed on devices that natively support FP8 inference, ensuring both weights and cache are in FP8 format.
+5. Server memory must exceed `1T` to ensure normal model loading and operation.
+
+The models can run under the configurations in the table below:
+
+| Model       | Precision | GPU Type and Count   | Test Framework |
+|-------------|-----------|----------------------|----------------|
+| GLM-4.5     | BF16      | H100 x 16 / H200 x 8 | sglang         |
+| GLM-4.5     | FP8       | H100 x 8 / H200 x 4  | sglang         |
+| GLM-4.5-Air | BF16      | H100 x 4 / H200 x 2  | sglang         |
+| GLM-4.5-Air | FP8       | H100 x 2 / H200 x 1  | sglang         |
+
+Under the configurations in the table below, the models can utilize their full 128K context length:
+
+| Model       | Precision | GPU Type and Count    | Test Framework |
+|-------------|-----------|-----------------------|----------------|
+| GLM-4.5     | BF16      | H100 x 32 / H200 x 16 | sglang         |
+| GLM-4.5     | FP8       | H100 x 16 / H200 x 8  | sglang         |
+| GLM-4.5-Air | BF16      | H100 x 8 / H200 x 4   | sglang         |
+| GLM-4.5-Air | FP8       | H100 x 4 / H200 x 2   | sglang         |
+
+### Fine-tuning
+
+The code can run under the configurations in the table below
+using [Llama Factory](https://github.com/hiyouga/LLaMA-Factory):
+
+| Model       | GPU Type and Count | Strategy | Batch Size (per GPU) |
+|-------------|--------------------|----------|----------------------|
+| GLM-4.5     | H100 x 16          | Lora     | 1                    |
+| GLM-4.5-Air | H100 x 4           | Lora     | 1                    |
+
+The code can run under the configurations in the table below using [Swift](https://github.com/modelscope/ms-swift):
+
+| Model       | GPU Type and Count | Strategy | Batch Size (per GPU) |
+|-------------|--------------------|----------|----------------------|
+| GLM-4.5     | H20 (96GiB) x 16   | Lora     | 1                    |
+| GLM-4.5-Air | H20 (96GiB) x 4    | Lora     | 1                    |
+| GLM-4.5     | H20 (96GiB) x 128  | SFT      | 1                    |
+| GLM-4.5-Air | H20 (96GiB) x 32   | SFT      | 1                    |
+| GLM-4.5     | H20 (96GiB) x 128  | RL       | 1                    |
+| GLM-4.5-Air | H20 (96GiB) x 32   | RL       | 1                    |
+
+## Quick Start
+
+Please install the required packages according to `requirements.txt`.
+
+```shell
+pip install -r requirements.txt
+```
+
+### transformers
+
+Please refer to the `trans_infer_cli.py` code in the `inference` folder.
+
+### vLLM
+
++ Both BF16 and FP8 can be started with the following code:
+
+```shell
+vllm serve zai-org/GLM-4.5-Air \
+    --tensor-parallel-size 8 \
+    --tool-call-parser glm45 \
+    --reasoning-parser glm45 \
+    --enable-auto-tool-choice \
+    --served-model-name glm-4.5-air
+```
+
+If you're using 8x H100 GPUs and encounter insufficient memory when running the GLM-4.5 model, you'll need
+`--cpu-offload-gb 16` (only applicable to vLLM).
+
+If you encounter `flash infer` issues, use `VLLM_ATTENTION_BACKEND=XFORMERS` as a temporary replacement. You can also
+specify `TORCH_CUDA_ARCH_LIST='9.0+PTX'` to use `flash infer` (different GPUs have different TORCH_CUDA_ARCH_LIST
+values, please check accordingly).
+
+### SGLang
+
++ BF16
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.5-Air \
+  --tp-size 8 \
+  --tool-call-parser glm45  \
+  --reasoning-parser glm45 \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 3 \
+  --speculative-eagle-topk 1 \
+  --speculative-num-draft-tokens 4 \
+  --mem-fraction-static 0.7 \
+  --served-model-name glm-4.5-air \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
++ FP8
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.5-Air-FP8 \
+  --tp-size 4 \
+  --tool-call-parser glm45  \
+  --reasoning-parser glm45  \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 3  \
+  --speculative-eagle-topk 1  \
+  --speculative-num-draft-tokens 4 \
+  --mem-fraction-static 0.7 \
+  --disable-shared-experts-fusion \
+  --served-model-name glm-4.5-air-fp8 \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
+### Request Parameter Instructions
+
++ 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 `extra_body={"chat_template_kwargs": {"enable_thinking": False}}` parameter.
++ Both support tool calling. Please use OpenAI-style tool description format for calls.
++ For specific code, please refer to `api_request.py` in the `inference` folder.