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1
2import os
3import torch
4
5from transformers import AutoTokenizer, AutoConfig, Lfm2MoeForCausalLM
6
7# # === Step 1: Define tiny model config ===
8model_id = "LiquidAI/LFM2-24B-A2B"
9config = AutoConfig.from_pretrained(model_id)
10
11config.num_hidden_layers = 3
12config.layer_types = [
13 "full_attention",
14 "full_attention",
15 "conv",
16]
17config.num_attention_heads = 4
18config.num_key_value_heads = 4
19config.hidden_size = 16
20config.num_dense_layers = 1
21config.moe_intermediate_size = 16
22config.intermediate_size = 16
23
24# === Step 2: Create model from config ===
25model = Lfm2MoeForCausalLM(config)
26
27# === Step 3: Load or create tokenizer ===
28tokenizer = AutoTokenizer.from_pretrained(model_id)
29
30# === Step 4: Save model and tokenizer ===
31output_dir = "./lfm2_moe"
32os.makedirs(output_dir, exist_ok=True)
33model.save_pretrained(output_dir, safe_serialization=False)
34tokenizer.save_pretrained(output_dir)
35