Training in progress, epoch 0

Logs/events.out.tfevents.1718384259.e29ec45d9208.134.10

@@ -0,0 +1,3 @@
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+size 6094

Untitled.ipynb

@@ -429,7 +429,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a582824b64b041249949ed78eaade5d4",
+       "model_id": "cfa272f52f4a42629057b2566b4dd820",
        "version_major": 2,
        "version_minor": 0
       },
@@ -443,7 +443,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc5f4463bc564fcda93b0e03f4539c9f",
+       "model_id": "f08e1f4a5ed443a6ae6378bf2be0d759",
        "version_major": 2,
        "version_minor": 0
       },
@@ -486,8 +486,8 @@
        "\n",
        "    <div>\n",
        "      \n",
-       "      <progress value='387' max='645' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [387/645 1:30:38 < 1:00:44, 0.07 it/s, Epoch 2.98/5]\n",
+       "      <progress value='129' max='645' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [129/645 28:55 < 1:57:29, 0.07 it/s, Epoch 0.99/5]\n",
        "    </div>\n",
        "    <table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -498,16 +498,6 @@
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>3.360700</td>\n",
-       "      <td>0.005133</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>0.003900</td>\n",
-       "      <td>0.001245</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table><p>"
       ],
@@ -517,20 +507,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
-      "  warnings.warn(\n",
-      "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
-      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
-      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
-      "  warnings.warn(\n",
-      "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
-      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
-     ]
     }
    ],
    "source": [
@@ -888,35 +864,27 @@
    "source": [
     "def ModelInference():\n",
     "\n",
-    "    InputTxt = \"What is/are all the conditions that could be detected in this 12 Lead ECG ?\"\n",
-    "    InputImg = TestBase64ECG119.replace(\"data:image/png;base64,\",\"\")\n",
+    "    BaseModelID = \"google/paligemma-3b-pt-224\"\n",
+    "    config = PeftConfig.from_pretrained(\"Geohunterr/ECG_FT_PG\")\n",
+    "    BaseModel = AutoModelForPreTraining.from_pretrained(BaseModelID)\n",
+    "    PeftFTModel = PeftModel.from_pretrained(BaseModel,\"Geohunterr/ECG_FT_PG\")\n",
     "\n",
     "\n",
+    "    InputTxt = \"What is/are all the conditions that could be detected in this 12 Lead ECG ?\"\n",
+    "    InputImg = TestBase64ECG119.replace(\"data:image/png;base64,\",\"\")\n",
     "    InputImgTensor = Image.open(BytesIO(base64.b64decode(InputImg))).convert(\"RGB\")\n",
     "\n",
-    "    print(\"Img to Process\",InputImgTensor)\n",
+    "    Processor = PaliGemmaProcessor.from_pretrained(BaseModelID)\n",
     "\n",
     "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "    Processor = PaliGemmaProcessor.from_pretrained(\"google/paligemma-3b-pt-224\")\n",
-    "\n",
-    "    inputs = Processor(text=InputTxt, images=InputImgTensor, return_tensors=\"pt\")\n",
-    "\n",
-    "    model = PaliGemmaForConditionalGeneration.from_pretrained(\"/workspace\")\n",
-    "\n",
-    "        # Autoregressively generate\n",
-    "    # We use greedy decoding here, for more fancy methods see https://huggingface.co/blog/how-to-generate\n",
-    "    generated_ids = model.generate(**inputs, max_new_tokens=496)\n",
-    "\n",
-    "    # Next we turn each predicted token ID back into a string using the decode method\n",
-    "    # We chop of the prompt, which consists of image tokens and our text prompt\n",
-    "    # image_token_index = model.config.image_token_index\n",
-    "    # num_image_tokens = len(generated_ids[generated_ids == image_token_index])\n",
-    "    # num_text_tokens = len(Processor.tokenizer.encode(InputTxt))\n",
-    "    # num_prompt_tokens = num_image_tokens + num_text_tokens + 2\n",
-    "    \n",
-    "    generated_text = Processor.batch_decode(generated_ids, skip_special_tokens=True,\n",
-    "                                            clean_up_tokenization_spaces=False)[0]\n",
-    "    print(generated_text)"
+    "    Inputs = Processor(text=InputTxt,images=InputImgTensor,padding=\"longest\",do_convert_rgb=True,return_tensors=\"pt\").to(device)\n",
+    "    PeftFTModel.to(device)\n",
+    "    Inputs = Inputs.to(dtype=PeftFTModel.dtype)\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = PeftFTModel.generate(**Inputs,max_length=496)\n",
+    "\n",
+    "    print(Processor.decode(output[0],skip_special_tokens=True))"
    ]
   },
   {

adapter_model.safetensors

@@ -1,3 +1,3 @@
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