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 evaluation results

by SaylorTwift HF Staff - opened about 4 hours ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

about 4 hours ago

Add evaluation results for LiquidAI/LFM3-1.7B

Summary

This PR adds the benchmark results published on the model card for LiquidAI/LFM3-1.7B to .eval_results/, following the Hugging Face Hub evaluation-results specification.

Benchmarks Added

Benchmarks Skipped (Not Registered on Hub)

These benchmarks are on the model card but have no registered eval.yaml on the Hub:

LiveClaw (45.60)
BrowseComp-Plus, OpenClaw subset (37.00)
WebArena-Verified (38.42)
EnterpriseOps-Gym (28.20)
AutomationBench, public (41.29)
BFCL V4 (71.39)
ToolSandbox (80.36)
MCP-Atlas (32.57)
τ³-bench, 4 domains (68.01)
LiveCodeBench v6 (73.46)
LiveCodeBench Pro 25Q2 (31.74)
SciCode (28.47)
IFEval (96.66)
IFBench (73.03)
Multi-IF (89.10)
AA-Omniscience (0.50; accuracy 7.07; non-hallucination 92.93)
AA-LCR (52.83)
RULER 128K (65.56)

They can be added once their authors register an eval.yaml on the Hub.

Mapping notes

Claw-Eval General (EN) average (68.64): the Hub task's headline is pass^3, so only that score is added.
Humanity's Last Exam with tools (13.90): only the no-tools score is added, marked with a note.

Source

Model card: https://huggingface.co/LiquidAI/LFM3-1.7B
Blog: https://www.liquid.ai/blog/lfm3

Files Added

.eval_results/LFM3-1.7B.yaml

Verification

The scores come from the benchmark tables on the model card, as measured by Liquid AI. No verified token is included, because these were not run via HF Jobs with inspect-ai.

To upload:

hf upload LiquidAI/LFM3-1.7B .eval_results .eval_results --create-pr --commit-message "Add evaluation results"

Add evaluation resultse1d7b24f

SaylorTwift changed pull request status to open about 3 hours ago

mlabonne

Liquid AI org about 3 hours ago

Thank you!

mlabonne changed pull request status to merged about 3 hours ago

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