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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tiny-random/lfm3", trust_remote_code=True, device_map="auto")

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
```

This is a randomly initialized tiny model based on LiquidAI/LFM3-1.7B for testing and debugging.

Feature	Source	This repo
Checkpoint tensor elements	1.73B BF16	8.62M BF16
Safetensors weight files	3.46 GB (model.safetensors)	17.26 MB (model.safetensors)
Layers / layer-type cycle	24 / two-conv prefix, then full/conv/sliding/conv	10 / same prefix and two complete cycles
Hidden width / MLP width	2,048 / 7,680	64 / 128
Q heads / KV heads / head_dim	16 / 4 / 128	4 / 1 / 32
Short-conv kernel / cache	7 / 7	Same
RoPE	full 0.5 partial, theta 1e6; sliding theta 1e4	Same
Sandwich norms / QK RMSNorm / sigmoid head gate	present	Same
Vocabulary rows / embedding tying	128,000 / tied	Same
Maximum context length	262,144	128,000
Stored precision	BF16	Same

Residual width 64 and MLP width 128 follow tiny-random/lfm2. The pinned upstream implementation is included for Transformers loading. This repo provides a Transformers example; no serving-backend validation is claimed.

File path	Size
model.safetensors	17.3MB

Example usage

Transformers (✅ verified)

Load the included pinned upstream implementation with trust_remote_code=True.

def run_transformers(model_id: str):
    import torch
    from transformers import AutoModelForCausalLM, AutoTokenizer

    model_id = str(model_id)
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    model = (
        AutoModelForCausalLM.from_pretrained(
            model_id, trust_remote_code=True, dtype=torch.bfloat16, attn_implementation="sdpa"
        )
        .eval()
        .cuda()
    )
    inputs = tokenizer.apply_chat_template(
        [{"role": "user", "content": "What is C. elegans?"}],
        add_generation_prompt=True,
        return_tensors="pt",
        return_dict=True,
        enable_thinking=False,
    ).to(model.device)
    with torch.no_grad():
        logits = model(**inputs).logits
        assert torch.isfinite(logits).all()
        output = model.generate(**inputs, do_sample=False, max_new_tokens=32)
    print(tokenizer.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False))


if __name__ == "__main__":
    run_transformers(model_id="tiny-random/lfm3")

Creating this repo

Code: .meta/create_model.py
Model structure: .meta/model.log
Environment: .meta/env.json

Change log

2026-10-30: Initial version.
2026-10-31: Resize to hidden width 64 and MLP width 128, following tiny-random/lfm2; use four Q heads and one KV head while preserving the LFM3 layer cadence.

This repo is generated by GPT-6.1-Sol.