Instructions to use LiquidAI/LFM2.5-230M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-230M with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-230M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-230M")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-230M")
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 Settings

vLLM

How to use LiquidAI/LFM2.5-230M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-230M"
# 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/LFM2.5-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-230M

SGLang

How to use LiquidAI/LFM2.5-230M 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/LFM2.5-230M" \
    --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/LFM2.5-230M",
		"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/LFM2.5-230M" \
        --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/LFM2.5-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-230M with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-230M
```

Add evaluation results

by SaylorTwift HF Staff - opened 7 days ago

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+35

-0

Files changed (1) hide show

LFM2.5-230M.yaml +35 -0

LFM2.5-230M.yaml ADDED Viewed

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+# Model: LiquidAI/LFM2.5-230M
+# Source: https://huggingface.co/LiquidAI/LFM2.5-230M
+# Paper: https://arxiv.org/abs/2511.23404
+# Extracted from model card benchmark tables (see blog post: https://www.liquid.ai/blog/lfm2-5-230m)
+# Date extracted: 2026-06-26
+# Missing benchmarks (no registered eval.yaml found on the Hub):
+# - IFEval: 71.71
+# - IFBench: 38.40
+# - Multi-IF: 37.70
+# - CaseReportBench: 22.51
+# - BFCLv3: 43.26
+# - BFCLv4: 21.03
+# - τ²-Bench Telecom: 5.26
+# - τ²-Bench Retail: 13.68
+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  value: 25.41
+  date: "2026-06-25"
+  source:
+    url: https://huggingface.co/LiquidAI/LFM2.5-230M
+    name: "LFM2.5-230M model card"
+  notes: "GPQA Diamond score from the model card benchmark table"
+- dataset:
+    id: TIGER-Lab/MMLU-Pro
+    task_id: mmlu_pro
+  value: 20.25
+  date: "2026-06-25"
+  source:
+    url: https://huggingface.co/LiquidAI/LFM2.5-230M
+    name: "LFM2.5-230M model card"
+  notes: "MMLU-Pro score from the model card benchmark table"