Instructions to use zai-org/GLM-4.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4.7 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zai-org/GLM-4.7")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.7", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-4.7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-4.7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-4.7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-4.7

SGLang

How to use zai-org/GLM-4.7 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zai-org/GLM-4.7" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-4.7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zai-org/GLM-4.7" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-4.7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-4.7 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4.7
```

ZHANGYUXUAN-zR commited on Dec 22, 2025

Commit

4f271a7

verified ·

1 Parent(s): 646847c

Update README.md

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@@ -1,7 +1,7 @@
 ---
 language:
-- en
-- zh
 library_name: transformers
 license: mit
 pipeline_tag: text-generation
@@ -15,29 +15,81 @@ pipeline_tag: text-generation
 <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>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</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>
-**GLM-4.7, GLM-4.6 and GLM-4.5 use the same inference method.**
-you can check our [github](https://github.com/zai-org/GLM-4.5) for more detail.
-## Recommended Evaluation Parameters
-For general evaluations, we recommend using a **sampling temperature of 1.0**.
-For **code-related evaluation tasks** (such as LCB), it is further recommended to set:
-- `top_p = 0.95`
-- `top_k = 40`
-## Evaluation
-- For tool-integrated reasoning, please refer to [this doc](https://github.com/zai-org/GLM-4.5/blob/main/resources/glm_4.6_tir_guide.md).
-- For search benchmark, we design a specific format for searching toolcall in thinking mode to support search agent, please refer to [this](https://github.com/zai-org/GLM-4.5/blob/main/resources/trajectory_search.json). for the detailed template.

 ---
 language:
+  - en
+  - zh
 library_name: transformers
 license: mit
 pipeline_tag: text-generation
 <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>
+## Introduction
+GLM-4.7, your new coding partner, is coming with the following features:
+- 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%,
+  +10.0%) on Terminal Bench. 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 major step forward in UI quality. It produces cleaner, more modern webpages and generates
+  better-looking slides with more accurate layout and sizing.
+- Tool Using: Tool using is significantly improved. GLM-4.7 achieves open-source SOTA results on multi-step tool using
+  benchmarks such as τ^2-Bench and on web browsing via BrowserComp.
+- 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.
+More general, one would also witness significant improvements in many other scenarios such as chat, creative writing,
+and role-play scenario.
+![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench_glm47.png)
+## Benchmark
+| Benchmark                      | GLM-4.7 | GLM-4.6 | MiMo-V2-Flash | Kimi-K2-Thinking | DeepSeek-V3.2 | Gemini-3.0-Pro | Claude-Sonnet-4.5 | GPT-5-High | GPT-5.1-High | GPT-5.2-High |
+|:-------------------------------|:-------:|:-------:|:-------------:|:----------------:|:-------------:|:--------------:|:-----------------:|:----------:|:------------:|:------------:|
+| MMLU-Pro                       |  84.3   |  83.2   |     84.9      |       84.6       |     85.0      |      90.1      |       88.2        |    87.5    |     87.0     |     87.0     |
+| GPQA-Diamond                   |  85.7   |  81.0   |     83.7      |       84.5       |     82.4      |      91.9      |       83.4        |    85.7    |     88.1     |     92.4     |
+| HLE                            |  24.8   |  17.2   |     22.1      |       23.9       |     25.1      |      37.5      |       13.7        |    26.3    |     25.7     |     34.5     |
+| HLE (w/ Tools)                 |  42.8   |  30.4   |       -       |       44.9       |     40.8      |      45.8      |       32.0        |    35.2    |     42.7     |     45.5     |
+| AIME 2025                      |  95.7   |  93.9   |     94.1      |       94.5       |     93.1      |      95.0      |       87.0        |    94.6    |     94.0     |    100.0     |
+| HMMT Feb. 2025                 |  97.1   |  89.2   |     84.4      |       89.4       |     92.5      |      97.5      |       79.2        |    88.3    |     96.3     |     99.4     |
+| 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   |     80.6      |       83.1       |     83.3      |      90.7      |       64.0        |    87.0    |     87.0     |      -       |
+| SWE-Bench Verified             |  73.8   |  68.0   |     73.4      |       71.3       |     73.1      |      76.2      |       77.2        |    74.9    |     76.3     |     80.0     |
+| SWE-Bench Multilingual         |  66.7   |  53.8   |     71.7      |       61.1       |     70.2      |       -        |       68.0        |    55.3    |      -       |      -       |
+| Terminal Bench Hard            |  33.3   |  23.6   |     30.5      |       30.6       |   35.4 / 33   |      39.0      |       33.3        |    30.5    |     43.0     |      -       |
+| Terminal Bench 2.0             |  41.0   |  24.5   |     38.5      |       35.7       |     46.4      |      54.2      |       42.8        |    35.2    |     47.6     |     54.0     |
+| BrowseComp                     |  52.0   |  45.1   |     45.4      |        -         |     51.4      |       -        |       24.1        |    54.9    |     50.8     |     65.8     |
+| BrowseComp (w/ Context Manage) |  67.5   |  57.5   |     58.3      |       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   |     80.3      |       74.3       |     85.3      |      90.7      |       87.2        |    82.4    |     82.7     |      -       |
+## Evaluation Parameters
+**Default Settings (Most Tasks)**
+* temperature: `1.0`
+* top-p: `0.95`
+* max new tokens: `131072`
+For agentic tasks, please turn on [Preserved Thinking mode](https://docs.z.ai/guides/capabilities/thinking-mode).
+**Terminal Bench, SWE Bench Verified**
+* temperature: `0.7`
+* top-p: `1.0`
+* max new tokens: `16384`
+**τ^2-Bench**
+* Temperature: `0`
+* Max new tokens: `16384`
+> 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.
+## Inference
+Check our [Github](https://github.com/zai-org/GLM-4.5) For More Detail.