Instructions to use LiquidAI/LFM2-8B-A1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-8B-A1B with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2-8B-A1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-8B-A1B")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-8B-A1B")
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 LiquidAI/LFM2-8B-A1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-8B-A1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2-8B-A1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-8B-A1B

SGLang

How to use LiquidAI/LFM2-8B-A1B 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 "LiquidAI/LFM2-8B-A1B" \
    --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": "LiquidAI/LFM2-8B-A1B",
		"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 "LiquidAI/LFM2-8B-A1B" \
        --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": "LiquidAI/LFM2-8B-A1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2-8B-A1B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-8B-A1B
```

No file named configuration_lfm2_moe.py

by Mohaddz - opened Oct 8, 2025

Discussion

Mohaddz

Oct 8, 2025

I am getting this error

OSError: LiquidAI/LFM2-8B-A1B does not appear to have a file named configuration_lfm2_moe.py. Checkout 'https://huggingface.co/LiquidAI/LFM2-8B-A1B/tree/main' for available files.

Transformers version:

pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6

paulpak58

Liquid AI org Oct 8, 2025

Can you verify thattrust_remote_code is set to False?

Mohaddz

Oct 8, 2025

@paulpak58 Oh thanks, now it works, I would suggest to add it to the model card (not sure if its common knowledge)
Also, congrats on the launch and well done!

stev236

Oct 17, 2025

@paulpak58 Unfortunately, this doesn't work with vllm serve. If trust_remote_code is not enabled, vllm complains that it needs to be enabled. If you enable it, then you get the missing configuration_lfm2_moe.py error.

ERROR:    (APIServer pid=4114) Please pass the argument `trust_remote_code=True` to allow custom code to be run. [type=value_error, input_value=ArgsKwargs(()

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