fix telemetry place in janus notebook (#2746)

Author: eaidova

notebooks/janus-multimodal-generation/janus-multimodal-generation.ipynb

@@ -71,12 +71,7 @@
     "    if not Path(util_path).exists():\n",
     "        r = requests.get(base_local_files_url + util_path)\n",
     "        with open(util_path, \"w\") as f:\n",
-    "            f.write(r.text)\n",
-    "\n",
-    "# Read more about telemetry collection at https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-telemetry\n",
-    "from notebook_utils import collect_telemetry\n",
-    "\n",
-    "collect_telemetry(\"janus-multimodal-generation.ipynb\")"
+    "            f.write(r.text)"
    ]
   },
   {
@@ -208,6 +203,11 @@
     "from ov_janus_helper import convert_janus_model\n",
     "import ipywidgets as widgets\n",
     "\n",
+    "# Read more about telemetry collection at https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-telemetry\n",
+    "from notebook_utils import collect_telemetry\n",
+    "\n",
+    "collect_telemetry(\"janus-multimodal-generation.ipynb\")\n",
+    "\n",
     "model_ids = [\"deepseek-ai/Janus-Pro-1B\", \"deepseek-ai/Janus-Pro-7B\", \"deepseek-ai/Janus-1.3B\"]\n",
     "\n",
     "compression_configuration = {\n",

notebooks/janus-multimodal-generation/ov_janus_helper.py

@@ -323,6 +323,8 @@ def forward_wrap(
         for output, output_name in zip(ov_model.outputs, output_names):
             output.get_tensor().set_names({output_name})
         patch_stateful(ov_model)
+        ov_model.set_rt_info("f16", ["runtime_options", "KV_CACHE_PRECISION"])
+
         print("✅ Language model successfully converted")
 
         if quantization_config is not None:

notebooks/multimodal-rag/multimodal-rag-llamaindex.ipynb

@@ -390,7 +390,13 @@
     "asr_model = OVModelForSpeechSeq2Seq.from_pretrained(asr_model_path, device=asr_device.value)\n",
     "asr_processor = AutoProcessor.from_pretrained(asr_model_path)\n",
     "\n",
-    "pipe = pipeline(\"automatic-speech-recognition\", model=asr_model, tokenizer=asr_processor.tokenizer, feature_extractor=asr_processor.feature_extractor, device=torch.device(\"cpu\"))"
+    "pipe = pipeline(\n",
+    "    \"automatic-speech-recognition\",\n",
+    "    model=asr_model,\n",
+    "    tokenizer=asr_processor.tokenizer,\n",
+    "    feature_extractor=asr_processor.feature_extractor,\n",
+    "    device=torch.device(\"cpu\"),\n",
+    ")"
    ]
   },
   {