Create README.md

README.md

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+---
+language:
+  - en
+  - zh
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+---
+
+# GLM-4.7-Flash
+
+<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-Flash 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-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.
+
+
+### Performances on Benchmarks
+
+
+| Benchmark          | GLM-4.7-Flash | Qwen3-30B-A3B-Thinking-2507 | GPT-OSS-20B |
+|--------------------|---------------|-----------------------------|-------------|
+| AIME 25            | 91.6          | 85.0                        | 91.7        |
+| GPQA               | 75.2          | 73.4                        | 71.5        |
+| LCB v6             | 64.0          | 66.0                        | 61.0        |
+| HLE                | 14.4          | 9.8                         | 10.9        |
+| SWE-bench Verified | 59.2          | 22.0                        | 34.0        |
+| τ²-Bench           | 79.5          | 49.0                        | 47.7        |
+| BrowseComp         | 42.8          | 2.29                        | 28.3        |
+
+
+### Evaluation Parameters
+
+**Default Settings (Most Tasks)**
+
+* temperature: `1.0`
+* top-p: `0.95`
+* max new tokens: `131072`
+
+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).
+
+**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.
+
+## Serve GLM-4.7-Flash Locally
+
+For local deployment, GLM-4.7-Flash 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.
+
+vLLM and SGLang only support GLM-4.7-Flash on their main branches.
+
+### vLLM
+
++ using pip (must use pypi.org as the index url):
+
+```shell
+pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+### SGLang
+
++ Install the supported versions of SGLang and Transformers (using `uv` is recommended):
+
+```shell
+uv pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 --extra-index-url https://sgl-project.github.io/whl/pr/
+uv pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa
+```
+
+### transformers
+
+using with transformers as
+
+```shell
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+and then run:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_PATH = "zai-org/GLM-4.7-Flash"
+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)
+```
+
+### vLLM
+
+```shell
+vllm serve dtometzki/GLM-4.7-Flash-FP8-Dynamic \
+     --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-flash
+```