diff --git a/docs/model_serving_framework/deploy_sparse_model_to_SageMaker.ipynb b/docs/model_serving_framework/deploy_sparse_model_to_SageMaker.ipynb
index 7997346c93..71e9b4c086 100644
--- a/docs/model_serving_framework/deploy_sparse_model_to_SageMaker.ipynb
+++ b/docs/model_serving_framework/deploy_sparse_model_to_SageMaker.ipynb
@@ -52,7 +52,13 @@
    "outputs": [],
    "source": [
     "%%writefile handler/code/requirements.txt\n",
-    "sentence-transformers==5.0.0"
+    "transformers==4.56.1\n",
+    "huggingface_hub==0.35.0\n",
+    "hf_xet==1.1.10\n",
+    "tokenizers==0.22.0\n",
+    "regex==2025.9.1\n",
+    "safetensors==0.6.2\n",
+    "sentence-transformers==5.1.0"
    ]
   },
   {
@@ -134,30 +140,64 @@
     "        )\n",
     "        print(f\"Using device: {self.device}\")\n",
     "        self.model = SparseEncoder(model_id, device=self.device, trust_remote_code=trust_remote_code)\n",
+    "        self._warmup()\n",
     "        self.initialized = True\n",
     "\n",
-    "    def preprocess(self, requests):\n",
+    "    def _warmup(self):\n",
+    "        input_data = [{\"body\": [\"hello world\"] * 10}]\n",
+    "        self.handle(input_data, None)\n",
+    "\n",
+    "    def _preprocess(self, requests):\n",
     "        inputSentence = []\n",
     "        batch_idx = []\n",
+    "        formats = []  # per-text format: \"word\" or \"token_id\"\n",
     "\n",
     "        for request in requests:\n",
     "            request_body = request.get(\"body\")\n",
     "            if isinstance(request_body, bytearray):\n",
     "                request_body = request_body.decode(\"utf-8\")\n",
     "                request_body = json.loads((request_body))\n",
-    "            if isinstance(request_body, list):\n",
+    "\n",
+    "            # dict-based new schema: {\"texts\": str | list[str], \"sparse_embedding_format\": str}\n",
+    "            if isinstance(request_body, dict):\n",
+    "                texts = request_body.get(\"texts\")\n",
+    "                fmt = request_body.get(\"sparse_embedding_format\", \"word\")\n",
+    "                fmt = \"token_id\" if isinstance(fmt, str) and fmt.lower() == \"token_id\" else \"word\"\n",
+    "\n",
+    "                if isinstance(texts, list):\n",
+    "                    inputSentence += texts\n",
+    "                    batch_idx.append(len(texts))\n",
+    "                    formats += [fmt] * len(texts)\n",
+    "                else:\n",
+    "                    inputSentence.append(texts)\n",
+    "                    batch_idx.append(1)\n",
+    "                    formats.append(fmt)\n",
+    "\n",
+    "            # legacy schemas\n",
+    "            elif isinstance(request_body, list):\n",
     "                inputSentence += request_body\n",
     "                batch_idx.append(len(request_body))\n",
+    "                formats += [\"word\"] * len(request_body)\n",
     "            else:\n",
     "                inputSentence.append(request_body)\n",
     "                batch_idx.append(1)\n",
+    "                formats.append(\"word\")\n",
+    "\n",
+    "        return inputSentence, batch_idx, formats\n",
     "\n",
-    "        return inputSentence, batch_idx\n",
+    "    def _convert_token_ids(self, sparse_embedding):\n",
+    "        token_ids = self.model.tokenizer.convert_tokens_to_ids([x[0] for x in sparse_embedding])\n",
+    "        return [(str(token_ids[i]), sparse_embedding[i][1]) for i in range(len(token_ids))]\n",
     "\n",
     "    def handle(self, data, context):\n",
-    "        inputSentence, batch_idx = self.preprocess(data)\n",
+    "        inputSentence, batch_idx, formats = self._preprocess(data)\n",
     "        model_output = self.model.encode_document(inputSentence, batch_size=max_bs)\n",
-    "        sparse_embedding = list(map(dict,self.model.decode(model_output)))\n",
+    "\n",
+    "        sparse_embedding_word = self.model.decode(model_output)\n",
+    "        for i, fmt in enumerate(formats):\n",
+    "            if fmt == \"token_id\":\n",
+    "                sparse_embedding_word[i] = self._convert_token_ids(sparse_embedding_word[i])\n",
+    "        sparse_embedding = list(map(dict, sparse_embedding_word))\n",
     "\n",
     "        outputs = [sparse_embedding[s:e]\n",
     "           for s, e in zip([0]+list(itertools.accumulate(batch_idx))[:-1],\n",
@@ -424,8 +464,8 @@
     "```json\n",
     "POST /_plugins/_ml/connectors/_create\n",
     "{\n",
-    "  \"name\": \"test\",\n",
-    "  \"description\": \"Test connector for Sagemaker model\",\n",
+    "  \"name\": \"Sagemaker Connector: embedding\",\n",
+    "  \"description\": \"The connector to sagemaker embedding model\",\n",
     "  \"version\": 1,\n",
     "  \"protocol\": \"aws_sigv4\",\n",
     "  \"credential\": {\n",
@@ -436,6 +476,7 @@
     "    \"region\": \"{region}\",\n",
     "    \"service_name\": \"sagemaker\",\n",
     "    \"input_docs_processed_step_size\": 2,\n",
+    "    \"sparse_embedding_format\": \"word\"\n",
     "  },\n",
     "  \"actions\": [\n",
     "    {\n",
@@ -445,7 +486,12 @@
     "        \"content-type\": \"application/json\"\n",
     "      },\n",
     "      \"url\": \"https://runtime.sagemaker.{region}.amazonaws.com/endpoints/{predictor.endpoint_name}/invocations\",\n",
-    "      \"request_body\": \"${parameters.input}\"\n",
+    "      \"request_body\": \"\"\"\n",
+    "          {\n",
+    "              \"texts\": ${parameters.input},\n",
+    "              \"sparse_embedding_format\": \"${parameters.sparse_embedding_format}\"\n",
+    "          }\n",
+    "      \"\"\"\n",
     "    }\n",
     "  ],\n",
     "  \"client_config\":{\n",