Qwen2_5_1_5b.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ak-Gautam/llm_infer/blob/main/Qwen2_5_1_5b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install unsloth\n",
        "# Also get the latest nightly Unsloth!\n",
        "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\""
      ],
      "metadata": {
        "id": "w-hjsaWBWp8B"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "vB9RVCwtYM7-"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_seq_length = 2048\n",
        "dtype = torch.float16\n",
        "load_in_4bit = False\n",
        "model_name = \"Qwen/Qwen2.5-1.5B-Instruct\""
      ],
      "metadata": {
        "id": "bH7bMAi6W-o9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from unsloth import FastLanguageModel\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = model_name,\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        ")\n",
        "\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846,
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        },
        "id": "fZHIDwKpX1xk",
        "outputId": "b0c18bd1-c4b0-4228-c565-48b60be04264"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth 2024.9: Fast Qwen2 patching. Transformers = 4.44.2.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.4.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.27.post2. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/242 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.51k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d96cfeb643694366bd422a0707389655"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/632 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65f42affbc45444691ff2227f4e23083"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/613 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49aab74ca2a94cbeb621acb017484423"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd750d7467b542cdb639117fe35147aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Qwen2ForCausalLM(\n",
              "  (model): Qwen2Model(\n",
              "    (embed_tokens): Embedding(151936, 1536)\n",
              "    (layers): ModuleList(\n",
              "      (0-27): 28 x Qwen2DecoderLayer(\n",
              "        (self_attn): Qwen2Attention(\n",
              "          (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
              "          (k_proj): Linear(in_features=1536, out_features=256, bias=True)\n",
              "          (v_proj): Linear(in_features=1536, out_features=256, bias=True)\n",
              "          (o_proj): Linear(in_features=1536, out_features=1536, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): Qwen2MLP(\n",
              "          (gate_proj): Linear(in_features=1536, out_features=8960, bias=False)\n",
              "          (up_proj): Linear(in_features=1536, out_features=8960, bias=False)\n",
              "          (down_proj): Linear(in_features=8960, out_features=1536, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): Qwen2RMSNorm((1536,), eps=1e-06)\n",
              "        (post_attention_layernorm): Qwen2RMSNorm((1536,), eps=1e-06)\n",
              "      )\n",
              "    )\n",
              "    (norm): Qwen2RMSNorm((1536,), eps=1e-06)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=1536, out_features=151936, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.eos_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "9Oh4W1XVZ5ii",
        "outputId": "c7879f8a-f7a6-421f-fb99-a11540519465"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_end|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TextStreamer"
      ],
      "metadata": {
        "id": "54AqbhEbi5BO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_args = {\n",
        "    \"max_new_tokens\": 64,\n",
        "    \"temperature\": 0.7,\n",
        "    \"top_p\": 0.9,\n",
        "    \"top_k\": 50,\n",
        "    \"do_sample\": True,\n",
        "}"
      ],
      "metadata": {
        "id": "LzuP2Avegbmw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_text = [\"Hi how are you doing?\", \"What's the weather like today?\"]\n",
        "for text in input_text:\n",
        "  inputs = tokenizer(text, return_tensors=\"pt\", padding=True, truncation=True).to(\"cuda\")\n",
        "\n",
        "\n",
        "  text_streamer = TextStreamer(tokenizer)\n",
        "  _ = model.generate(**inputs, streamer=text_streamer, **generation_args)\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLsm1iwaeqmm",
        "outputId": "ac87ce94-567f-4ebf-8422-e320acc23203"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi how are you doing? I am a big fan of your work and have always admired your ability to bring out the best in people. Thank you for all that you do! \n",
            "\n",
            "I just wanted to say thank you for everything that you've done, both as an individual and through your various projects like \"The People's Pharmacy\". It is so\n",
            "\n",
            "What's the weather like today? I'm sorry, as an AI language model, I don't have access to real-time information about the current weather conditions. However, you can easily check the weather forecast by searching online or using a weather app on your phone. Just type in your location and it will provide you with accurate information about the weather. It\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zwZ7X3iUhPCM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}