Instructions to use LiquidAI/LFM3-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM3-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM3-1.7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM3-1.7B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/LFM3-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM3-1.7B"
# 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/LFM3-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM3-1.7B

SGLang

How to use LiquidAI/LFM3-1.7B 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/LFM3-1.7B" \
    --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/LFM3-1.7B",
		"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/LFM3-1.7B" \
        --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/LFM3-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM3-1.7B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM3-1.7B
```

Add IFBench evaluation result

#21

by SaylorTwift HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/21

Discussion Files changed

-0

SaylorTwift

1 day ago

Hugging Face CLI

Add IFBench evaluation result

Summary

This PR adds the IFBench score reported for LiquidAI/LFM3-1.7B to .eval_results/, following the Hugging Face Hub evaluation-results specification. IFBench (allenai/IFBench_test) is now an official Hub benchmark, with prompt-level loose accuracy as its headline metric.

Benchmarks Added

IFBench: 73.03

Source

Model card: https://huggingface.co/LiquidAI/LFM3-1.7B (row: "IFBench")

Files Added

.eval_results/ifbench.yaml

Verification

This score is self-reported in the source above; no verified token is provided because it was not run via HF Jobs with inspect-ai. The Hub task is prompt-level loose accuracy (0-100). If your number was computed with a different IFBench variant (strict, or instruction-level), let us know and we will fix or close this PR.

Add IFBench evaluation resulta044de1c

SaylorTwift

1 day ago

Hugging Face CLI

Closing this: the source doesn't say which IFBench variant this score is (the Hub task is prompt-level loose accuracy), so we can't map it reliably. If you can confirm it's prompt-level loose, we're happy to reopen.

SaylorTwift changed pull request status to closed 1 day ago

SaylorTwift

1 day ago

Hugging Face CLI

Reopening: keeping this score as reported. If it isn't prompt-level loose accuracy, let us know and we'll update or close it.

SaylorTwift changed pull request status to open 1 day ago

mlabonne

Liquid AI org 1 day ago

Hey, yes we only report the prompt-level loose accuracy as our IFBench scores.

mlabonne changed pull request status to merged 1 day ago

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