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# COPYRIGHT © 2025-2026 Konstantin Vladimirovich Grabko. ALL RIGHTS RESERVED.
# CMS Manhattan JiRack Technology — PATENT PENDING
#
# This code is proprietary.
# Personal and non-commercial research use is allowed.
# Any commercial use, derivative works for profit, or distribution
# requires a paid license and 5% royalty.
#
# Unauthorized commercial use is strictly prohibited.
# Contact: grabko@cmsmanhattan.com
# =============================================================================
#
# Model updated for new last tokenizer version
#
# Replace toknizer in current model: Just use the class and call resize function in your train script
# New model: Use our conversion script to rapidly initialize new models by copying existing embeddings and LM_head weights. This enables fast model bootstrapping, or alternatively, provides the foundation to train a new model entirely from scratch.
#
# =============================================================================
# =============================================================================
# COPYRIGHT © 2025 Konstantin Vladimirovich Grabko. ALL RIGHTS RESERVED.
# =============================================================================
import torch
from JiRackTernary_new import JiRackConfig, JiRackTernary1B
from transformers import AutoTokenizer
from safetensors.torch import load_file, save_file
import os
print("🚀 Copying embeddings and lm_head...")
old_model_path = "."
new_tokenizer_path = "./jirack_code_tokenizer"
save_path = "./JiRack_init_model_with_new_vocab"
os.makedirs(save_path, exist_ok=True)
# Load new tokenizer
tokenizer = AutoTokenizer.from_pretrained(new_tokenizer_path)
new_vocab_size = len(tokenizer)
print(f"New vocab size: {new_vocab_size}")
# Create new model
config = JiRackConfig()
model = JiRackTernary1B(config)
# Load old model (SafeTensors)
old_state = load_file(f"{old_model_path}/model.safetensors")
old_vocab_size = 128256
with torch.no_grad():
# Copy old weights
model.token_emb.weight[:old_vocab_size] = old_state['token_emb.weight'][:old_vocab_size].clone()
model.lm_head.weight[:old_vocab_size] = old_state['lm_head.weight'][:old_vocab_size].clone()
# Initialize new 3 tokens (FIM) with mean value
mean_emb = old_state['token_emb.weight'].mean(dim=0)
model.token_emb.weight[old_vocab_size:] = mean_emb
model.lm_head.weight[old_vocab_size:] = mean_emb
print(f"✅ Copied {old_vocab_size} tokens")
print(f"✅ Initialized {new_vocab_size - old_vocab_size} new tokens")
# Save in SafeTensors
save_file(model.state_dict(), f"{save_path}/model.safetensors")
tokenizer.save_pretrained(save_path)
print(f"\n🎉 Done! New model saved to: {save_path}")
print("Use this folder as the starting weights for training from scratch.") |