Here is a code to create this tiny model:
import os
from transformers import AutoTokenizer
from transformers import Lfm2Config, Lfm2ForCausalLM
# === Step 1: Define tiny model config ===
config = Lfm2Config(
num_hidden_layers=4,
num_attention_heads=4,
num_key_value_heads=4,
intermediate_size=12,
hidden_size=16,
block_multiple_of=8
)
# === Step 2: Create model from config ===
model = Lfm2ForCausalLM(config)
# === Step 3: Load or create tokenizer ===
model_id = "LiquidAI/LFM2-350M"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# === Step 4: Save model and tokenizer ===
output_dir = "./lfm2"
os.makedirs(output_dir, exist_ok=True)
model.save_pretrained(output_dir, safe_serialization=False)
tokenizer.save_pretrained(output_dir)