Update README.md

README.md

@@ -6,40 +6,125 @@ language:
 pipeline_tag: text-generation
 library_name: transformers
 ---
-
-# GLM-4.5-Air
-
 <div align="center">
-<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
+
+<img src="assets/sii.jpg" alt="SII" width="96" height="96">
+<img src="assets/asi.png" alt="ASI" width="96" height="96">
 </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>.
-    <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.
+---
+tags:
+- text-generation
+- agent
+- tool-use
+- long-context
+license: other
+language:
+- en
+pipeline_tag: text-generation
+---
+# LIMI‑Air: Less is More for Agency
+
+## 📌 Table of Contents
+- [Overview](#overview)
+- [Model Details](#model-details)
+- [Dataset](#dataset)
+- [Quick Start](#quick-start)
+- [Prompting](#prompting)
+- [Evaluation](#evaluation)
+- [Limitations](#limitations)
+- [License](#license)
+- [Citation](#citation)
+
+## Overview
+
+LIMI‑Air is a smaller, faster agentic variant built on [GLM‑4.5‑Air](https://huggingface.co/zai-org/GLM-4.5-Air) (~106B), fine‑tuned with the same compact, high‑quality agentic data as LIMI.
 
-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.
+## Model Details
 
-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.
+- Base model: `zai-org/GLM-4.5-Air`
+- Params: ~106B
+- Framework: slime; Data: [GAIR/LIMI](https://huggingface.co/datasets/GAIR/LIMI)
 
-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.
+## Model Zoo
 
-![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench.png)
+Our LIMO model is available on Hugging Face 🤗:
 
-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.
+| Model | Backbone | Size | Link |
+|---|---|---|---|
+| LIMI | [GLM‑4.5](https://huggingface.co/zai-org/GLM-4.5) | 355B | https://huggingface.co/GAIR/LIMI |
+| LIMI‑Air | [GLM‑4.5‑Air](https://huggingface.co/zai-org/GLM-4.5-Air) | 106B | https://huggingface.co/GAIR/LIMI-Air |
 
 
-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).
+## Datasets
+
+We release our datasets through Hugging Face 🤗:
+- Name: `GAIR/LIMI`
+- Summary: curated agentic SFT data (OpenAI `messages`, optional `tools`, normalized tool‑call arguments); current release contains 78 high‑quality samples.
+- Link: https://huggingface.co/datasets/GAIR/LIMI
 
 ## Quick Start
 
-Please refer our [github page](https://github.com/zai-org/GLM-4.5) for more detail.
+<details>
+<summary>Start with HF Transformers</summary>
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    "GAIR/LIMI-Air", torch_dtype="auto", device_map="auto", trust_remote_code=True
+)
+tok = AutoTokenizer.from_pretrained("GAIR/LIMI-Air", trust_remote_code=True)
+text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+out = model.generate(
+    **tok(text, return_tensors="pt").to(model.device),
+    max_new_tokens=4096,
+    temperature=0.6,
+    top_p=0.95,
+    do_sample=True,
+)
+```
+
+</details>
+
+<details>
+<summary>Start with VLLM</summary>
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+llm = LLM(model="GAIR/LIMI-Air", trust_remote_code=True)
+tok = AutoTokenizer.from_pretrained("GAIR/LIMI-Air", trust_remote_code=True)
+text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+out = llm.generate(text, SamplingParams(temperature=0.6, max_tokens=4096, top_p=0.95))
+```
+
+</details>
+
+## Prompting
+
+Same as LIMI; provide messages in OpenAI chat format, optionally with `tools`. Include a grounding system message when helpful.
+
+## Evaluation
+
+Uses the same metrics (FTFC, SR@R, RC@R at R=3) and protocol as LIMI; see the paper for comparative results.
+
+## Limitations
+
+- Inherits base model constraints; validated on curated agentic tasks only
+- Lower compute cost with potential performance trade‑offs on complex tasks
+
+## License
+
+- Inherits GLM‑4.5‑Air terms; verify upstream license before deployment
+
+## Citation
+
+```bibtex
+@article{LIMI2025,
+  title   = {Less is More for Agentic Intelligence},
+  author  = {LIMI Authors},
+  year    = {2025},
+  journal = {arXiv preprint arXiv:2502.03387}
+}
+```