Instructions to use tiny-random/lfm3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/lfm3 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/lfm3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/lfm3", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tiny-random/lfm3", trust_remote_code=True, device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/lfm3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/lfm3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/lfm3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/lfm3

SGLang

How to use tiny-random/lfm3 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 "tiny-random/lfm3" \
    --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": "tiny-random/lfm3",
		"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 "tiny-random/lfm3" \
        --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": "tiny-random/lfm3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/lfm3 with Docker Model Runner:
```
docker model run hf.co/tiny-random/lfm3
```

lfm3

File size: 848 Bytes

8201ebf

{
  "source_model": {
    "name": "LiquidAI/LFM3-1.7B",
    "commit_id": "ac2586a34f1a1c03329dbdcdefabd8b6478ddaa7"
  },
  "environments": {
    "creation": {
      "python": "3.12.13",
      "packages": {
        "huggingface_hub": "2.3.0",
        "safetensors": "0.8.0",
        "tokenizers": "0.23.3",
        "torch": "2.13.0+cu126",
        "transformers": "5.22.0.dev0"
      },
      "cuda": "12.6",
      "hardware": {
        "gpu_model": "NVIDIA H20"
      }
    },
    "transformers": {
      "python": "3.12.13",
      "packages": {
        "huggingface_hub": "2.3.0",
        "safetensors": "0.8.0",
        "tokenizers": "0.23.3",
        "torch": "2.13.0+cu126",
        "transformers": "5.22.0.dev0",
        "triton": "3.7.1"
      },
      "cuda": "12.6",
      "hardware": {
        "gpu_model": "NVIDIA H20"
      }
    }
  }
}