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
```

YAML Metadata Error:Invalid content in Eval Result file .eval_results/LFM3-1.7B.yaml

Check out the documentation for more information.

Show details

Task ID "hle" does not match any task in dataset "cais/hle". Available: none

LFM3-1.7B / .eval_results /LFM3-1.7B.yaml

mlabonne

SaylorTwift HF Staff

Add evaluation results (#6)

ac2586a 7 days ago

Raw History Blame Contribute Delete

1.55 kB

- dataset:
    id: harborframework/terminal-bench-2.1
    task_id: terminalbench_2_1
  value: 12.92
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"
  notes: "OpenCode agent, up to 100 turns."
- dataset:
    id: SWE-bench/SWE-bench_Verified
    task_id: swe_bench_%_resolved
  value: 43.4
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"
  notes: "OpenCode agent, up to 250 turns."
- dataset:
    id: SWE-bench/SWE-bench_Multilingual
    task_id: swe_bench_multilingual_%_resolved
  value: 23.0
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"
  notes: "OpenCode agent, up to 250 turns."
- dataset:
    id: claw-eval/Claw-Eval
    task_id: general
  value: 51.4
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"
- dataset:
    id: Idavidrein/gpqa
    task_id: diamond
  value: 63.37
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"
- dataset:
    id: cais/hle
    task_id: hle
  value: 7.17
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"
  notes: "No tools."
- dataset:
    id: LiquidAI/ifstruct-v1.0
    task_id: ifstruct_v1
  value: 93.72
  date: "2026-10-27"
  source:
    url: https://huggingface.co/LiquidAI/LFM3-1.7B
    name: "LFM3-1.7B model card"

	- dataset:
	id: harborframework/terminal-bench-2.1
	task_id: terminalbench_2_1
	value: 12.92
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"
	notes: "OpenCode agent, up to 100 turns."
	- dataset:
	id: SWE-bench/SWE-bench_Verified
	task_id: swe_bench_%_resolved
	value: 43.4
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"
	notes: "OpenCode agent, up to 250 turns."
	- dataset:
	id: SWE-bench/SWE-bench_Multilingual
	task_id: swe_bench_multilingual_%_resolved
	value: 23.0
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"
	notes: "OpenCode agent, up to 250 turns."
	- dataset:
	id: claw-eval/Claw-Eval
	task_id: general
	value: 51.4
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"
	- dataset:
	id: Idavidrein/gpqa
	task_id: diamond
	value: 63.37
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"
	- dataset:
	id: cais/hle
	task_id: hle
	value: 7.17
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"
	notes: "No tools."
	- dataset:
	id: LiquidAI/ifstruct-v1.0
	task_id: ifstruct_v1
	value: 93.72
	date: "2026-10-27"
	source:
	url: https://huggingface.co/LiquidAI/LFM3-1.7B
	name: "LFM3-1.7B model card"