{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from safetensors.torch import load_file, save_file\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"/Qwen2.5-1.5B-Instruct/model.safetensors\"\n",
    "tensors = load_file(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.embed_tokens.weight torch.Size([151936, 1536])\n"
     ]
    }
   ],
   "source": [
    "# 152064\n",
    "# 151642\n",
    "for name in tensors.keys():\n",
    "    if 151936 in tensors[name].shape:\n",
    "        print(name, tensors[name].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed = \"model.embed_tokens.weight\"\n",
    "new_vocab = 152064\n",
    "source_shape = tensors[embed].shape\n",
    "target_shape = torch.Size((new_vocab, source_shape[-1]))\n",
    "\n",
    "source = tensors[embed]\n",
    "\n",
    "target = torch.zeros(target_shape, dtype=source.dtype, device=source.device)\n",
    "target[:source_shape[0], :] = source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "updated_tensors = {**tensors, embed: target}\n",
    "save_file(updated_tensors, file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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