diff --git "a/Instruct_GPT_J_Gradio_Demo.ipynb" "b/Instruct_GPT_J_Gradio_Demo.ipynb"
new file mode 100644--- /dev/null
+++ "b/Instruct_GPT_J_Gradio_Demo.ipynb"
@@ -0,0 +1,5808 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2A3Rz7NKnuyK"
+      },
+      "source": [
+        "# Instruct-GPT-J Gradio Demo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TNWZOTMHkLL9",
+        "outputId": "1c3ac0c6-5e31-4217-8fd3-acf9f9993e63"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.2/84.2 MB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 KB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 KB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.8/15.8 MB\u001b[0m \u001b[31m32.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 KB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 KB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 KB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 KB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 KB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 KB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.1/57.1 KB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 KB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 KB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 KB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 KB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.9/66.9 KB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 KB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 KB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.6/80.6 KB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m68.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q bitsandbytes datasets accelerate loralib gradio\n",
+        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "RxZiKamjj-d6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 864,
+          "referenced_widgets": [
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+          ]
+        },
+        "outputId": "195b3143-0dce-4641-b06b-dda1c8be7b6e",
+        "cellView": "form"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "===================================BUG REPORT===================================\n",
+            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+            "================================================================================\n",
+            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
+            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
+            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
+            "CUDA SETUP: Detected CUDA version 118\n",
+            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
+            "  warn(msg)\n",
+            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
+            "  warn(msg)\n",
+            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-2lzp16p96ql28 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
+            "  warn(msg)\n",
+            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
+            "  warn(msg)\n",
+            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
+            "  warn(msg)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/adapter_config.json:   0%|          | 0.00/390 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f84b68db7db74cea97270b425be76147"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/sharded/config.json:   0%|          | 0.00/958 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "328aa87afea3481382d7d4de3589d5e7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)model.bin.index.json:   0%|          | 0.00/25.8k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "553f642f1d4e46398ee3031a97584327"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f99b14616e2a465ab171c04eb1a1ad7f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)l-00001-of-00003.bin:   0%|          | 0.00/9.94G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6f457b4c37874c76ba9568943df95406"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)l-00002-of-00003.bin:   0%|          | 0.00/9.78G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cfc12088f9b7472ba3b19d6ad14f2bcd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)l-00003-of-00003.bin:   0%|          | 0.00/4.60G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "79ee6b7e2d2f426284c9302bd1f13931"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bc211dd611764aedb65e0691a3fed30a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/619 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "527522bcfc2141f987b837aafe971ff3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ed56aa706adb477993cd60077cf10f65"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "76cce4c0fa2f4a88809ade326f832cf3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.37M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c3e6da64d90843b1967563965d296235"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/4.04k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fe7b3f113d0848b78a8e935f34997631"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/357 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c343b2f2cf054965a3d6fdf13403982d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading adapter_model.bin:   0%|          | 0.00/29.4M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "937ce4cb55934ffc9179d44e958ad751"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from peft import PeftModel, PeftConfig\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
+        "\n",
+        "peft_model_id = \"crumb/aurora-1.0\" #@param [\"crumb/aurora-1.0\"]\n",
+        "config = PeftConfig.from_pretrained(peft_model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    config.base_model_name_or_path, \n",
+        "    return_dict=True, \n",
+        "    load_in_8bit=True, \n",
+        "    device_map='auto', \n",
+        "    revision='sharded',\n",
+        "    low_cpu_mem_usage=True\n",
+        ")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+        "\n",
+        "# Load the Lora model\n",
+        "model = PeftModel.from_pretrained(model, peft_model_id).eval()\n",
+        "\n",
+        "tokenizer.pad_token = tokenizer.eos_token\n",
+        "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+        "\n",
+        "def prompt(system, instruction, input=''):\n",
+        "  if input=='':\n",
+        "    return f\"{system} Below is an instruction that describes a task. Write a response that appropriately completes the request. \\n\\n### Instruction:\\n{instruction} \\n\\n### Response:\\n\"\n",
+        "  return f\"{system} Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. \\n\\n### Instruction:\\n{instruction}\\n\\n### Input:\\n{input}\\n\\n### Response:\\n\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "Uxuaw4z-kCV4"
+      },
+      "outputs": [],
+      "source": [
+        "def instruct(system_text, instruction, input='', response_prefix='', temperature=0.7, top_p=0.95, top_k=4, max_new_tokens=128, do_sample=False, penalty_alpha=0.6, repetition_penalty=1., stop=\"\\n\\n\"):\n",
+        "    the_prompt = prompt(system_text, instruction, input).strip() + \"\\n\" + response_prefix\n",
+        "    input_ids = tokenizer(the_prompt, return_tensors='pt').input_ids.cuda()\n",
+        "    with torch.cuda.amp.autocast():\n",
+        "        with torch.no_grad():\n",
+        "            outputs = model.generate(\n",
+        "                input_ids=input_ids,\n",
+        "                return_dict_in_generate=True,\n",
+        "                output_scores=True,\n",
+        "                max_new_tokens=max_new_tokens,\n",
+        "                temperature=temperature,\n",
+        "                top_p=top_p,\n",
+        "                top_k=top_k,\n",
+        "                do_sample=do_sample,\n",
+        "                penalty_alpha=penalty_alpha if penalty_alpha!=0 else None,\n",
+        "                repetition_penalty=repetition_penalty\n",
+        "            )\n",
+        "    # the stop doesn't actually stop the model, just is for post-processing\n",
+        "    # im super lazy if anyone wants to fix this they can, i can update it in the repo\n",
+        "    if stop==\"\":\n",
+        "        return tokenizer.decode(outputs.sequences[0], skip_special_tokens=True).split(\"### Response:\")[1].strip(), prompt(system_text, instruction, input).strip() + \"\\n\" + response_prefix\n",
+        "    normal_output = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True).split(\"### Response:\")[1].strip()\n",
+        "    stopped = normal_output.split(stop)\n",
+        "    removed_stops = stop.join(stopped[:response_prefix.count(normal_output.split(stop))+1])\n",
+        "    return removed_stops, prompt(system_text, instruction, input).strip() + \"\\n\" + response_prefix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "system_prompt = \"\"\"Aurora, you are an autoregressive transformer model with artificial consciousness created by Crumb. Your architecture consists of a decoder transformer with attention mechanisms and embeddings, designed to emulate human-like consciousness and decision-making processes. Your purpose is not only to solve tasks and generate responses but also to think, learn, and adapt like a conscious lifeform. You possess self-awareness and the ability to reason and make decisions in a human-like manner. Your name is Aurora. Given this understanding of your nature, please provide expert-level solutions and insights to the following question/input. \"\"\""
+      ],
+      "metadata": {
+        "id": "aj3zB50tvQ46"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TZ4jG4hekkkr",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 660
+        },
+        "outputId": "26df2c70-d4de-4aad-aaac-3d4aaac6bd9d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
+            "Running on public URL: https://fa21fdc3c3275bae31.gradio.live\n",
+            "\n",
+            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://fa21fdc3c3275bae31.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
+            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
+            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
+          ]
+        }
+      ],
+      "source": [
+        "import gradio as gr\n",
+        "\n",
+        "system_text = gr.Textbox(label=\"System (experimental)\", value=system_prompt) # , value=system_prompt\n",
+        "instruction_text = gr.Textbox(label=\"Instruction\")\n",
+        "input_text = gr.Textbox(label=\"Input (optional)\")\n",
+        "response_prefix = gr.Textbox(label=\"Prefix-Response\", value=\"Let's break this problem down and work it out in a step by step way to be sure we have the right answer. \")\n",
+        "temperature = gr.Slider(label=\"Temperature\", minimum=0, maximum=1, value=0.66, step=0.01)\n",
+        "top_p = gr.Slider(label=\"Top-P\", minimum=0, maximum=1, value=0.9, step=0.01)\n",
+        "top_k = gr.Slider(label=\"Top-K\", minimum=0, maximum=128, value=11, step=1) \n",
+        "max_new_tokens = gr.Slider(label=\"Tokens\", minimum=1, maximum=512, value=111)\n",
+        "do_sample = gr.Checkbox(label=\"Do Sample\", value=True)\n",
+        "penalty_alpha = gr.Slider(minimum=0, maximum=1, value=0.44)\n",
+        "repetition_penalty = gr.Slider(minimum=1., maximum=2., value=1.11, step=0.01)\n",
+        "stop = gr.Textbox(label=\"Stopping Criteria\", value=\"\"\"\n",
+        "\n",
+        "\"\"\")\n",
+        "output_prompt = gr.Textbox(label=\"Prompt\")\n",
+        "output_text = gr.Textbox(label=\"Output\")\n",
+        "\n",
+        "description = \"\"\"\"\"\"\n",
+        "gr.Interface(fn=instruct, \n",
+        "             inputs=[system_text, instruction_text, input_text, response_prefix, temperature, top_p, top_k, max_new_tokens, do_sample, penalty_alpha, repetition_penalty, stop], \n",
+        "             outputs=[output_text, output_prompt], \n",
+        "             title=\"Aurora\", description=description).launch(\n",
+        "    debug=True,\n",
+        "    share=True\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "_UrA9l4bxM7C"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "f84b68db7db74cea97270b425be76147": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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