Update med-gemma.ipynb

med-gemma.ipynb

@@ -1 +1,43 @@
-{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"colab":{"machine_shape":"hm","gpuType":"T4"},"accelerator":"GPU","kaggle":{"accelerator":"gpu","dataSources":[],"dockerImageVersionId":31193,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# ✅ Install dependencies\n!pip install transformers accelerate --quiet\n\n# ✅ Import required libraries\nfrom kaggle_secrets import UserSecretsClient\nfrom huggingface_hub import login\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n\n# ✅ Load your HF token from Kaggle Secrets (make sure secret name = HF_TOKEN)\nuser_secrets = UserSecretsClient()\nhf_token = user_secrets.get_secret(\"HF_TOKEN\")\n\n# ✅ Login to HuggingFace\nlogin(token=hf_token)\n\n# ✅ Model ID (correct one you asked for)\nmodel_id = \"google/medgemma-4b-it\"\n\n# ✅ Load tokenizer\ntokenizer = AutoTokenizer.from_pretrained(\n    model_id, \n    trust_remote_code=True\n)\n\n# ✅ Load model on GPU\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_id,\n    device_map=\"auto\",\n    torch_dtype=\"auto\",\n    trust_remote_code=True\n)\n\n# ✅ Build pipeline\npipe = pipeline(\n    \"text-generation\",\n    model=model,\n    tokenizer=tokenizer,\n    max_new_tokens=350,\n    temperature=0.25\n)\n\n# ✅ Ask MedGemma a question\nprompt = \"Explain medically in simple terms: What are the symptoms of a heart attack?\"\n\nresult = pipe(prompt)[0][\"generated_text\"]\n\n# ✅ Print output\nprint(\"\\n🧠 MedGemma 4B Response:\\n\")\nprint(result)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-07T19:39:17.872323Z","iopub.execute_input":"2025-11-07T19:39:17.873021Z","iopub.status.idle":"2025-11-07T19:40:29.668192Z","shell.execute_reply.started":"2025-11-07T19:39:17.872994Z","shell.execute_reply":"2025-11-07T19:40:29.667474Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/1.16M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f537affa494d4a528e5571e0fc446656"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.model:   0%|          | 0.00/4.69M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"25e88d479e1c450c8f2c0a786cc70ab6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/33.4M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b3ee25e8bef4d168c840de83a555fcc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/35.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3a67e6e923f545c485154789bab5609e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/662 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a9f0208f98e44e00b2fce86e4e80b97b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"chat_template.jinja:   0%|          | 0.00/1.53k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c3fb0e347c8648a2a4f3e0624b0b52bb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/2.47k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"890472733c164644a63e3f7490f20b51"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json:   0%|          | 0.00/90.6k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3d6b5acbb008455abe5679abaa886269"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0dab637538374ed1ba7006d7c45ec0b2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00002.safetensors:   0%|          | 0.00/4.96G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"37ca252578c94d16adcec573cc3c3bf6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00002-of-00002.safetensors:   0%|          | 0.00/3.64G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d2729edb3ef4ab8bb67ee7c8e8688bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0dca30f73a7b4e1c8d2234c068140ce5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/156 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"328c1fe30dff4f3282d9573cd1b78b37"}},"metadata":{}},{"name":"stderr","text":"Device set to use cuda:0\n","output_type":"stream"},{"name":"stdout","text":"\n🧠 MedGemma 4B Response:\n\nExplain medically in simple terms: What are the symptoms of a heart attack?\n\nThe symptoms of a heart attack can vary from person to person, and not everyone experiences the same symptoms. However, some common symptoms include:\n\n*   **Chest pain or discomfort:** This is the most common symptom. It can feel like pressure, squeezing, fullness, or pain in the center of the chest. The pain may last for more than a few minutes, or it may go away and come back.\n*   **Pain or discomfort in other areas of the upper body:** This can include pain or discomfort in one or both arms, the back, neck, jaw, or stomach.\n*   **Shortness of breath:** This can occur with or without chest pain.\n*   **Cold sweat:** Breaking out in a cold sweat for no apparent reason.\n*   **Nausea or vomiting:** Feeling sick to your stomach or throwing up.\n*   **Lightheadedness or dizziness:** Feeling faint or unsteady.\n*   **Unexplained fatigue:** Feeling unusually tired or weak.\n\nIt's important to note that women are more likely than men to experience some of these symptoms, such as shortness of breath, nausea/vomiting, and back or jaw pain.\n\nIf you experience any of these symptoms, especially if they are new or unusual for you, it's important to seek medical attention immediately. A heart attack is a serious medical emergency, and prompt treatment can save your life.\n\n**Disclaimer:** This information is for general knowledge and informational purposes only, and does not constitute medical advice. It is essential to consult with a qualified healthcare professional for any health concerns or before making any decisions related to your health or treatment.\nExplain medically in simple terms: What are the symptoms of a heart\n","output_type":"stream"}],"execution_count":3}]}
+# ✅ Install dependencies (if needed)
+!pip install transformers accelerate --quiet
+
+# ✅ Import libraries
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# ✅ Since you are in HuggingFace Spaces:
+# NO Kaggle Secrets needed
+# NO login() needed if model is public
+
+model_id = "google/medgemma-4b-it"
+
+# ✅ Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True
+)
+
+# ✅ Load model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    trust_remote_code=True
+)
+
+# ✅ Build generator pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=350,
+    temperature=0.25
+)
+
+# ✅ Test prompt
+prompt = "Explain medically in simple terms: What are the symptoms of a heart attack?"
+
+result = pipe(prompt)[0]["generated_text"]
+
+print("\n🧠 MedGemma 4B Response:\n")
+print(result)