Instructions to use LiquidAI/LFM2.5-1.2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-1.2B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-1.2B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
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

vLLM

How to use LiquidAI/LFM2.5-1.2B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-1.2B-Instruct"
# 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/LFM2.5-1.2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct

SGLang

How to use LiquidAI/LFM2.5-1.2B-Instruct 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/LFM2.5-1.2B-Instruct" \
    --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/LFM2.5-1.2B-Instruct",
		"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/LFM2.5-1.2B-Instruct" \
        --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/LFM2.5-1.2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-1.2B-Instruct with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct
```

add eval results for pixel-art-bench

#15

by AINovice2005 - opened 26 days ago

base: refs/heads/main

←

from: refs/pr/15

Discussion Files changed

+44

-0

YAML Metadata Error:Invalid Eval Result format in .eval_results/pixel-art-bench-detailed.yaml

Check out the documentation for more information.

Show details

✖ Invalid input: expected array, received object

Files changed (1) hide show

.eval_results/pixel-art-bench-detailed.yaml +44 -0

.eval_results/pixel-art-bench-detailed.yaml ADDED Viewed

	@@ -0,0 +1,44 @@

+model: hf/LiquidAI/LFM2.5-1.2B-Instruct
+total_time_seconds: 12
+tokens:
+  input: 427
+  output: 2121
+  total: 2548
+metrics:
+  json_validity:
+    accuracy: 0.143
+    mean: 0.143
+    stderr: 0.132
+    by_category:
+      abstract: 0.0
+      art: 0.0
+      character: 1.0
+      nature: 0.0
+      space: 0.0
+  render_success:
+    accuracy: 0.048
+    mean: 0.048
+    stderr: 0.044
+    by_category:
+      abstract: 0.0
+      art: 0.0
+      character: 0.333
+      nature: 0.0
+      space: 0.0
+  pixel_art_quality:
+    accuracy: 0.0
+    mean: 0.0
+    stderr: 0.0
+    by_category:
+      abstract: 0.0
+      art: 0.0
+      character: 0.0
+      nature: 0.0
+      space: 0.0
+samples: 7
+date: '2026-04-16'
+source:
+  url: https://huggingface.co/AINovice2005/pixel-art-bench
+  name: Pixel Art Bench eval traces
+  user: AINovice2005
+runs: 1