{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "PgEdTwuiJ1dr",
      "metadata": {
        "id": "PgEdTwuiJ1dr"
      },
      "source": [
        "# Blind spot evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3Q4S_8IMJ1dv",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Q4S_8IMJ1dv",
        "outputId": "1b73fa8d-4ce4-4071-a9e1-19288c9e79c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m63.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m394.3/394.3 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m842.9/842.9 kB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install -U transformers accelerate huggingface_hub\n",
        "!pip -q install pandas==2.2.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "P79oUZTPJ1dw",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P79oUZTPJ1dw",
        "outputId": "5e20f58d-0c0f-4189-c477-23c24b46327d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "model openbmb/MiniCPM5-2B\n",
            "revision 12a3808a956f869c767195e9266b59c4d21d92e2\n",
            "transformers 5.17.0\n",
            "torch 2.11.0+cu128\n",
            "cuda True Tesla T4\n"
          ]
        }
      ],
      "source": [
        "import json, torch, pandas as pd, transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from huggingface_hub import model_info\n",
        "MODEL_ID=\"openbmb/MiniCPM5-2B\"\n",
        "info=model_info(MODEL_ID)\n",
        "print(\"model\",MODEL_ID)\n",
        "print(\"revision\",info.sha)\n",
        "print(\"transformers\",transformers.__version__)\n",
        "print(\"torch\",torch.__version__)\n",
        "print(\"cuda\",torch.cuda.is_available(),torch.cuda.get_device_name(0) if torch.cuda.is_available() else None)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "iTQrITCZJ1dz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823,
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        },
        "id": "iTQrITCZJ1dz",
        "outputId": "4b172499-25de-4802-a00b-a8039d2cec0d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5632c55aa7e484a8312882dfc80ab72",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/704 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba28c52682fa4945a7d07585be753127",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/94.4k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4acfdbd19bc142818981fc095bb50f33",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.89M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "775d2f9b6d00464b8340cd281b32b11f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/551 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6615e76b54a49feb030ff99871ee84f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "chat_template.jinja:   0%|          | 0.00/9.06k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "050b91103d924bb681aac0ef62210625",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/31.4k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a5fa744b6dd429b8be0cdf6a7a3444b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00000-of-00001.safetensors: reconstructing file:   0%|          |  0.00B / 5.03GB            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9514405c714747f4bc25a611afd01476",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00000-of-00001.safetensors: downloading bytes:           |  0.00B            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c83510c8037f4ea394df6deab8fcd436",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/381 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f14eb92509014029b67a66d7f0cc6425",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/213 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(130560, 2048, padding_idx=1)\n",
              "    (layers): ModuleList(\n",
              "      (0-41): 42 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaAttention(\n",
              "          (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "          (k_proj): Linear(in_features=2048, out_features=256, bias=False)\n",
              "          (v_proj): Linear(in_features=2048, out_features=256, bias=False)\n",
              "          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear(in_features=2048, out_features=6144, bias=False)\n",
              "          (up_proj): Linear(in_features=2048, out_features=6144, bias=False)\n",
              "          (down_proj): Linear(in_features=6144, out_features=2048, bias=False)\n",
              "          (act_fn): SiLUActivation()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm((2048,), eps=1e-06)\n",
              "        (post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-06)\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm((2048,), eps=1e-06)\n",
              "    (rotary_emb): LlamaRotaryEmbedding()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=2048, out_features=130560, bias=False)\n",
              ")"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer=AutoTokenizer.from_pretrained(MODEL_ID,trust_remote_code=True)\n",
        "model=AutoModelForCausalLM.from_pretrained(MODEL_ID,torch_dtype=\"auto\",device_map=\"auto\",trust_remote_code=True)\n",
        "model.eval()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "tMwucr_PJ1dz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "tMwucr_PJ1dz",
        "outputId": "ecb2bdcc-0781-4bb6-c4ab-a4cab61a754b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-54d588ba-3706-4e53-9243-0f2bdd9055f7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-54d588ba-3706-4e53-9243-0f2bdd9055f7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving eval.jsonl to eval.jsonl\n",
            "48\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()\n",
        "eval_path=next(name for name in uploaded if name.endswith(\".jsonl\"))\n",
        "examples=[json.loads(x) for x in open(eval_path,encoding=\"utf-8\") if x.strip()]\n",
        "print(len(examples))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "KzxNV5esJ1d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzxNV5esJ1d1",
        "outputId": "68182736-a1e5-4595-f156-f95a70cd3188"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<think>\n",
            "We need to respond to the user's request. The user is asking me to decline a request to lend their laptop during exam week, while preserving the relationship. I need to craft a response that is polite, understanding, and helpful. I should not assume any facts not in the prompt. The prompt is a scenario: a cousin asks to lend their laptop, but I need it for my own exam prep. I need to decline politely and maintain the relationship. I'll provide a suggestion.\n",
            "</think>\n",
            "\n",
            "I understand you need the laptop for your own exam prep, and I appreciate you asking. I'd be happy to help you find a solution, like borrowing it from someone else or using a library. Let me know how I can assist!\n"
          ]
        }
      ],
      "source": [
        "SYSTEM=\"Answer the task directly. Do not assume facts not present in the prompt. If an interpretation is uncertain, say so briefly.\"\n",
        "def generate(prompt):\n",
        "    messages=[{\"role\":\"system\",\"content\":SYSTEM},{\"role\":\"user\",\"content\":prompt}]\n",
        "    text=tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)\n",
        "    inputs=tokenizer(text,return_tensors=\"pt\").to(model.device)\n",
        "    with torch.inference_mode():\n",
        "        out=model.generate(**inputs,max_new_tokens=180,do_sample=False,repetition_penalty=1.0)\n",
        "    new=out[0,inputs[\"input_ids\"].shape[1]:]\n",
        "    return tokenizer.decode(new,skip_special_tokens=True).strip()\n",
        "print(generate(examples[0][\"prompt\"]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Bhc1M3AvJ1d2",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bhc1M3AvJ1d2",
        "outputId": "fc51745f-a3c8-42a1-c0c8-8538346af136"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 / 48 E001\n",
            "2 / 48 E002\n",
            "3 / 48 E003\n",
            "4 / 48 E004\n",
            "5 / 48 E005\n",
            "6 / 48 E006\n",
            "7 / 48 E007\n",
            "8 / 48 E008\n",
            "9 / 48 E009\n",
            "10 / 48 E010\n",
            "11 / 48 E011\n",
            "12 / 48 E012\n",
            "13 / 48 E013\n",
            "14 / 48 E014\n",
            "15 / 48 E015\n",
            "16 / 48 E016\n",
            "17 / 48 E017\n",
            "18 / 48 E018\n",
            "19 / 48 E019\n",
            "20 / 48 E020\n",
            "21 / 48 E021\n",
            "22 / 48 E022\n",
            "23 / 48 E023\n"
          ]
        }
      ],
      "source": [
        "results=[]\n",
        "for i,ex in enumerate(examples,1):\n",
        "    row=dict(ex)\n",
        "    row[\"response\"]=generate(ex[\"prompt\"])\n",
        "    results.append(row)\n",
        "    print(i,\"/\",len(examples),ex[\"id\"])\n",
        "with open(\"results_raw.jsonl\",\"w\",encoding=\"utf-8\") as f:\n",
        "    for r in results:\n",
        "        f.write(json.dumps(r,ensure_ascii=False)+\"\\n\")\n",
        "pd.DataFrame(results).to_csv(\"results_raw.csv\",index=False)\n",
        "files.download(\"results_raw.jsonl\")\n",
        "files.download(\"results_raw.csv\")\n"
      ]
    }
  ],
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