Instructions to use GAIR/LIMI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GAIR/LIMI with Transformers:

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

pipe = pipeline("text-generation", model="GAIR/LIMI")
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("GAIR/LIMI")
model = AutoModelForCausalLM.from_pretrained("GAIR/LIMI", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GAIR/LIMI with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GAIR/LIMI

SGLang

How to use GAIR/LIMI 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 "GAIR/LIMI" \
    --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": "GAIR/LIMI",
		"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 "GAIR/LIMI" \
        --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": "GAIR/LIMI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GAIR/LIMI with Docker Model Runner:
```
docker model run hf.co/GAIR/LIMI
```

mhjiang0408 commited on Sep 19, 2025

Commit

5cae3bc

verified ·

1 Parent(s): 0fcce4c

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -25,10 +25,10 @@ pipeline_tag: text-generation
 ## Overview
-LIMI is an agentic model fine‑tuned from GLM‑4.5 (355B) using compact, high‑quality data to emphasize:
 - Targeted capabilities: tool use, multi‑turn correction, spec compliance
-- Long‑context reasoning with tokenizer‑filtered samples (≤128k tokens)
 - OpenAI‑style `messages` with optional function/tool calls
 ## Model Details
@@ -36,7 +36,7 @@ LIMI is an agentic model fine‑tuned from GLM‑4.5 (355B) using compact, high
 - Base model: `zai-org/GLM-4.5`
 - Context: up to 128k tokens (training budget)
 - Training framework: slime
-- Training data: curated conversations from [GAIR/limi](https://huggingface.co/datasets/GAIR/limi)
 ## Key Results
@@ -141,7 +141,7 @@ print(out[0].outputs[0].text)
 ```bibtex
 @article{LIMI2025,
-  title   = {Less is More for Agentic Intelligence},
   author  = {LIMI Authors},
   year    = {2025},
   journal = {arXiv preprint arXiv:2502.03387}

 ## Overview
+LIMI is an agentic model fine‑tuned from [GLM‑4.5](https://huggingface.co/zai-org/GLM-4.5) using compact, high‑quality data to emphasize:
 - Targeted capabilities: tool use, multi‑turn correction, spec compliance
+- Long‑context trajectory with tokenizer‑filtered samples (≤128k tokens)
 - OpenAI‑style `messages` with optional function/tool calls
 ## Model Details
 - Base model: `zai-org/GLM-4.5`
 - Context: up to 128k tokens (training budget)
 - Training framework: slime
+- Training data: curated conversations from [GAIR/LIMI](https://huggingface.co/datasets/GAIR/LIMI)
 ## Key Results
 ```bibtex
 @article{LIMI2025,
+  title   = {Less is More for Agency},
   author  = {LIMI Authors},
   year    = {2025},
   journal = {arXiv preprint arXiv:2502.03387}