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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GAIR/LIMI")
model = AutoModelForCausalLM.from_pretrained("GAIR/LIMI")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Update license to Apache-2.0 and add library_name: transformers

by nielsr HF Staff - opened Sep 24, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-6

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -1,13 +1,14 @@
 ---
 tags:
 - text-generation
 - agent
 - tool-use
 - long-context
-license: other
-language:
-- en
-pipeline_tag: text-generation
 ---
 <div style="display: flex; justify-content: center; align-items: center; gap: 20px;">
@@ -169,5 +170,4 @@ print(out[0].outputs[0].text)
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2509.17567},
 }
-```

 ---
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - text-generation
 - agent
 - tool-use
 - long-context
+library_name: transformers
 ---
 <div style="display: flex; justify-content: center; align-items: center; gap: 20px;">
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2509.17567},
 }
+```