Instructions to use LiquidAI/LFM2.5-8B-A1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-8B-A1B with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-8B-A1B")
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("LiquidAI/LFM2.5-8B-A1B")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-8B-A1B", 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 LiquidAI/LFM2.5-8B-A1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-8B-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/LFM2.5-8B-A1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-8B-A1B

SGLang

How to use LiquidAI/LFM2.5-8B-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/LFM2.5-8B-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/LFM2.5-8B-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/LFM2.5-8B-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/LFM2.5-8B-A1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-8B-A1B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-8B-A1B
```

LFM3-8B-A1B?

#40

by nerun - opened 14 days ago

Discussion

nerun

14 days ago

Any plans for a LFM3 version of this 8B-A1B model? I'm curious to see improvements to my favorite LiquidAI model.

mlabonne

Liquid AI org 13 days ago

Hey thanks! What do you think about LFM3-1.7B as a drop-in replacement for the 8B-A1B?

bpool

13 days ago

Hey thanks! What do you think about LFM3-1.7B as a drop-in replacement for the 8B-A1B?

Please see my post about sampling parameters.

It would be great if you could run coding benchmarks and select the sampling parameters exclusively for coding instead of combining it with general category.

For coding, it hasn't been great so far though on general tasks, it is punching above its weight class.

nerun

13 days ago

•

edited 13 days ago

@mlabonne , I haven't tested the LFM3-1.7B, but I really liked the LFM3-VL-2B. The LFM3-VL-2B in Q8_0 is 90 tok/s, Q6_K is 122 tok/s, i don't know why Q5_K_M is so slow, only 26 tok/s. Q6_K is my to go quant here.

EDIT:
Well, LFM3-VL-2B is LFM3-1.7B plus vision encoder.

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