notebooks/cabrita-lora.ipynb

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    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6ExAe1UCWn5E"
      },
      "source": [
        "# Talk to Domba-LoRA\n",
        "\n",
        "This notebook contains minimal code for running [Alpaca-LoRA](https://github.com/tloen/alpaca-lora/) for demonstration purposes. Please check the repo for more details.\n",
        "The notebook is based in this another [notebook](https://colab.research.google.com/drive/1eWAmesrW99p7e1nah5bipn0zikMb8XYC)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_pz8MuY84Qh",
        "outputId": "e94db934-452c-4b1b-f61d-e4b3f0af1e88"
      },
      "outputs": [],
      "source": [
        "!pip install bitsandbytes\n",
        "!pip install -q datasets loralib sentencepiece\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q git+https://github.com/huggingface/peft.git"
      ]
    },
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        },
        "id": "VucO3HSMoJkz",
        "outputId": "03db26a9-c078-4a53-c129-f54c5f96adfa"
      },
      "outputs": [],
      "source": [
        "from peft import PeftModel\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "model = LlamaForCausalLM.from_pretrained(\n",
        "    \"decapoda-research/llama-7b-hf\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, \"bookbot/domba-lora-v0-1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w3_lzwcqermJ"
      },
      "outputs": [],
      "source": [
        "def generate_prompt(instruction, input=None):\n",
        "    if input:\n",
        "        return f\"\"\"Berikut adalah instruksi yang menggambarkan suatu tugas, bersama dengan input yang memberikan konteks. Tuliskan sebuah jawaban yang melengkapi permintaan dengan tepat.\n",
        "\n",
        "### Instruksi:\n",
        "{instruction}\n",
        "\n",
        "### Konteks:\n",
        "{input}\n",
        "\n",
        "### Jawaban:\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Berikut adalah instruksi yang menggambarkan suatu tugas. Tuliskan sebuah jawaban yang melengkapi permintaan dengan tepat.\n",
        "\n",
        "### Instruksi:\n",
        "{instruction}\n",
        "\n",
        "### Jawaban:\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Egh3beCVRpW5"
      },
      "outputs": [],
      "source": [
        "generation_config = GenerationConfig(\n",
        "    temperature=0.2,\n",
        "    top_p=0.75,\n",
        "    num_beams=4,\n",
        ")\n",
        "\n",
        "def evaluate(instruction, input=None):\n",
        "    prompt = generate_prompt(instruction, input)\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        generation_config=generation_config,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        max_new_tokens=256\n",
        "    )\n",
        "    for s in generation_output.sequences:\n",
        "        output = tokenizer.decode(s)\n",
        "        print(\"Jawaban:\", output.split(\"### Jawaban:\")[1].strip())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "evaluate(input(\"Instruksi: \"))"
      ]
    }
  ],
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