Llama_3_2_3b_Gather.ipynb

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      "LlamaForCausalLM(\n",
      "  (model): LlamaModel(\n",
      "    (embed_tokens): Embedding(128256, 2048)\n",
      "    (layers): ModuleList(\n",
      "      (0-15): 16 x LlamaDecoderLayer(\n",
      "        (self_attn): LlamaSdpaAttention(\n",
      "          (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
      "          (k_proj): Linear(in_features=2048, out_features=512, bias=False)\n",
      "          (v_proj): Linear(in_features=2048, out_features=512, bias=False)\n",
      "          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
      "          (rotary_emb): LlamaRotaryEmbedding()\n",
      "        )\n",
      "        (mlp): LlamaMLP(\n",
      "          (gate_proj): Linear(in_features=2048, out_features=8192, bias=False)\n",
      "          (up_proj): Linear(in_features=2048, out_features=8192, bias=False)\n",
      "          (down_proj): Linear(in_features=8192, out_features=2048, bias=False)\n",
      "          (act_fn): SiLU()\n",
      "        )\n",
      "        (input_layernorm): LlamaRMSNorm((2048,), eps=1e-05)\n",
      "        (post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-05)\n",
      "      )\n",
      "    )\n",
      "    (norm): LlamaRMSNorm((2048,), eps=1e-05)\n",
      "    (rotary_emb): LlamaRotaryEmbedding()\n",
      "  )\n",
      "  (lm_head): Linear(in_features=2048, out_features=128256, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "from transformers import AutoModelForCausalLM\n",
    "\n",
    "# Carrega o modelo pré-treinado\n",
    "#model = AutoModel.from_pretrained('meta-llama/Llama-3.2-3B')\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    'unsloth/Llama-3.2-1B',\n",
    "    device_map='auto',  # Faz com que o modelo seja carregado parcialmente\n",
    "    offload_folder=\"offload\",  # Pasta para descarregar partes do modelo para o disco\n",
    "    offload_state_dict=True  # Também descarrega os parâmetros de estado para o disco\n",
    ")\n",
    "print(model)"
   ]
  },
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      "LlamaForCausalLM(\n",
      "  (model): LlamaModel(\n",
      "    (embed_tokens): Embedding(128256, 2048)\n",
      "    (rotary_emb): LlamaRotaryEmbedding()\n",
      "    (layers): ModuleList()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Acessa a parte 'model' do LlamaForCausalLM\n",
    "llama_model = model.model\n",
    "\n",
    "# Lista dos atributos a serem removidos, exceto 'embed_tokens'\n",
    "atributos_para_remover = [attr for attr in llama_model._modules if attr != 'embed_tokens' and attr != 'rotary_emb']\n",
    "\n",
    "for attr in atributos_para_remover:\n",
    "    delattr(llama_model, attr)\n",
    "\n",
    "# Opcional: remover também 'lm_head' do modelo principal\n",
    "if hasattr(model, 'lm_head'):\n",
    "    delattr(model, 'lm_head')\n",
    "\n",
    "# Remove todas as camadas de decodificação\n",
    "llama_model.layers = nn.ModuleList()\n",
    "\n",
    "model.config.num_hidden_layers = 0\n",
    "\n",
    "# Verifica a estrutura do modelo após a modificação\n",
    "print(model)"
   ]
  },
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     "text": [
      "Modelo salvo em: ./Llama-3.2-1b-Gather\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Define o diretório onde o modelo será salvo\n",
    "diretorio_salvar = './Llama-3.2-1b-Gather'\n",
    "\n",
    "# Cria o diretório se ele não existir\n",
    "os.makedirs(diretorio_salvar, exist_ok=True)\n",
    "\n",
    "# Salva o modelo e a configuração\n",
    "model.save_pretrained(diretorio_salvar)\n",
    "\n",
    "print(f\"Modelo salvo em: {diretorio_salvar}\")"
   ]
  }
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