Instructions to use notnotsamuel/LFM2.5-350M-RLCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use notnotsamuel/LFM2.5-350M-RLCD with Transformers:

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

pipe = pipeline("text-generation", model="notnotsamuel/LFM2.5-350M-RLCD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("notnotsamuel/LFM2.5-350M-RLCD")
model = AutoModelForCausalLM.from_pretrained("notnotsamuel/LFM2.5-350M-RLCD", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use notnotsamuel/LFM2.5-350M-RLCD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "notnotsamuel/LFM2.5-350M-RLCD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "notnotsamuel/LFM2.5-350M-RLCD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/notnotsamuel/LFM2.5-350M-RLCD

SGLang

How to use notnotsamuel/LFM2.5-350M-RLCD 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 "notnotsamuel/LFM2.5-350M-RLCD" \
    --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": "notnotsamuel/LFM2.5-350M-RLCD",
		"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 "notnotsamuel/LFM2.5-350M-RLCD" \
        --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": "notnotsamuel/LFM2.5-350M-RLCD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use notnotsamuel/LFM2.5-350M-RLCD with Docker Model Runner:
```
docker model run hf.co/notnotsamuel/LFM2.5-350M-RLCD
```

LFM2.5-350M-RLCD / scripts /bundle_base.py

notnotsamuel

Bundle verified unchanged LiquidAI base weights and tokenizer

deb589d verified 8 days ago

Raw

History Blame Contribute Delete

1.94 kB

	"""Download and verify unchanged base files for redistribution; never train or upload."""
	import hashlib
	import json
	import shutil
	from pathlib import Path
	from huggingface_hub import HfApi, hf_hub_download

	ROOT = Path(__file__).resolve().parents[1]
	MODEL = "LiquidAI/LFM2.5-350M"
	REVISION = "9e6c6ccf47cd318696e137d381a7ded8fe4df09f"
	FILES = ["model.safetensors", "config.json", "generation_config.json", "tokenizer.json", "tokenizer_config.json", "chat_template.jinja", "LICENSE"]


	def sha256(path):
	with path.open("rb") as source:
	return hashlib.file_digest(source, "sha256").hexdigest()


	def main():
	info = HfApi().model_info(MODEL, revision=REVISION, files_metadata=True)
	metadata = {f.rfilename: f for f in info.siblings}
	code_license = ROOT / "LICENSE-CODE"
	if not code_license.exists():
	assert (ROOT / "LICENSE").read_text().startswith("MIT License")
	shutil.copy2(ROOT / "LICENSE", code_license)
	manifest = {"source_repository": MODEL, "source_revision": REVISION,
	"weights_modified": False, "training_performed": False, "files": {}}
	for name in FILES:
	source = Path(hf_hub_download(MODEL, name, revision=REVISION))
	digest = sha256(source)
	remote = metadata[name]
	if remote.lfs:
	assert digest == remote.lfs.sha256, name
	else:
	content = source.read_bytes()
	blob = hashlib.sha1(f"blob {len(content)}\0".encode() + content).hexdigest()
	assert blob == remote.blob_id, name
	destination = ROOT / name
	shutil.copy2(source, destination)
	assert sha256(destination) == digest, name
	manifest["files"][name] = {"sha256": digest, "size_bytes": destination.stat().st_size}
	print(f"Verified unchanged: {name}", flush=True)
	(ROOT / "BASE_MODEL_MANIFEST.json").write_text(json.dumps(manifest, indent=2) + "\n")

	if __name__ == "__main__":
	main()