This model was base on unsloth/GLM-4.7-Flash and trained on a small reasoning dataset of Claude Opus 4.5, with reasoning effort set to High.

tools thinking

ollama run SimonPu/GLM-4.7-Flash:Q8

curl http://localhost:11434/api/chat \
  -d '{
    "model": "SimonPu/GLM-4.7-Flash:Q8",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='SimonPu/GLM-4.7-Flash:Q8',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'SimonPu/GLM-4.7-Flash:Q8',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated yesterday

yesterday

212fbbe9b83f · 32GB ·

model

archdeepseek2

parameters29.9B

quantizationQ8_0

32GB

template

[gMASK]<sop>{{ if .System }}<|system|> {{ .System }}{{ end }}{{ if .Prompt }}<|user|> {{ .Prompt }}{

140B

params

{ "min_p": 0.01, "repeat_penalty": 1, "seed": 3407, "stop": [ "<|user|>"

105B

Readme

GLM-4.7-Flash

👋 Join our Discord community.
📖 Check out the GLM-4.7 technical blog, technical report(GLM-4.5).
📍 Use GLM-4.7-Flash API services on Z.ai API Platform.
👉 One click to GLM-4.7.

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.

Default Settings (Most Tasks) from Run GLM-4.7-Flash Guide!

temperature: 1.0
top-p: 0.95
min-p: 0.01
repeat-penalty: 1.0

Benchmarks

Citation

If you find our work useful in your research, please consider citing the following paper:


@misc{5team2025glm45agenticreasoningcoding,
      title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models}, 
      author={GLM Team and Aohan Zeng and Xin Lv and others},
      year={2025},
      eprint={2508.06471},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.06471}, 
}

# 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.

**Default Settings (Most Tasks)** from  [Run GLM-4.7-Flash Guide!](https://unsloth.ai/docs/models/glm-4.7-flash)

* temperature: `1.0`
* top-p: `0.95`
* min-p: `0.01`
* repeat-penalty: `1.0`

## Benchmarks
![alt="Results Bar Chart"](https://cas-bridge.xethub.hf.co/xet-bridge-us/69726b9b70c3d2153f7df834/181fddb0ab577a70fc6db36d9a3c4f18bae67615025109a2bb7a283c8eb0292d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20260209%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20260209T045012Z&X-Amz-Expires=3600&X-Amz-Signature=ebb5248d9d73c328e5ddc3459c3ed8c720de2633a0035fb333a0074359d1547e&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=public&response-content-disposition=inline%3B+filename*%3DUTF-8%27%27results_bar_chart.png%3B+filename%3D%22results_bar_chart.png%22%3B&response-content-type=image%2Fpng&x-id=GetObject&Expires=1770616212&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc3MDYxNjIxMn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2FzLWJyaWRnZS54ZXRodWIuaGYuY28veGV0LWJyaWRnZS11cy82OTcyNmI5YjcwYzNkMjE1M2Y3ZGY4MzQvMTgxZmRkYjBhYjU3N2E3MGZjNmRiMzZkOWEzYzRmMThiYWU2NzYxNTAyNTEwOWEyYmI3YTI4M2M4ZWIwMjkyZCoifV19&Signature=mKu4QG43baW5uNTUUVuD3Ptz%7E7-OOsqsnXgtLWikVmZOyMwLKRsW%7E3mo5cs0YCA2b-O0ZLzb1cYl0Wu6RSWHRnCMsJ5Q1btOW0SU80C0jQ7CFbZqVI6Ew7NVdzQNEwHqO0hMkV9UAPxkQjp2F43nLRuXW39pR8FzckMxRRfuCEOb3ARcTCXTbsthugK6Uarsk3vkt4CW4633rz801BG%7EUtMZmg3-CU9gw8oUaMfFwUwB16m%7EGNQ3eeINJugtXTNU4VroxcHpksUCbinLPQQKIFcB-lOUqU%7EE83xb-sc%7EFX2ncVEPthoDGJ3DEFx-T3DtuHfpg9D8kV7FRJyof3gDOg__&Key-Pair-Id=K2L8F4GPSG1IFC)

## 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 others},
      year={2025},
      eprint={2508.06471},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.06471}, 
}```

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