Phi3Inference.ipynb

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              "tokenizer.json:   0%|          | 0.00/1.85M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce723406ead94c2da349de8b2269179e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/940 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86f0996713d941f7a897814d6c641e59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/623 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c60be40dd044f75969493902d5b0d74"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "modelpath=\"microsoft/Phi-3-mini-128k-instruct\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    modelpath,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map=\"auto\",\n",
        "    # attn_implementation=\"flash_attention_2\",\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(modelpath)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"The meaning of success is\"\n",
        "\n",
        "input_tokens = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "output_tokens = model.generate(**input_tokens)\n",
        "output = tokenizer.decode(output_tokens[0])\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyUcIwUWSMXL",
        "outputId": "a8eb7612-c02d-496f-f910-0eaee2c32a74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> The meaning of success is not to be rich, but to be rich in love, in knowledge\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"the sum of 2 and 2 is \"\n",
        "\n",
        "input_tokens = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "output_tokens = model.generate(**input_tokens)\n",
        "output = tokenizer.decode(output_tokens[0])\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "777As7mdSOp2",
        "outputId": "9ae9ed65-296f-40a1-87db-26a482d5e258"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> the sum of 2 and 2 is 4.<|end|>\n"
          ]
        }
      ]
    }
  ]
}