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sandeshv12
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Model Details
Model Provider
sandeshv12
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
You can run inference using Hugging Face transformers and peft:
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel base_model_id = "google/gemma-4-E2B-it"adapter_id = "sandeshv12/gemma4pharma" # 1. Load Tokenizertokenizer = AutoTokenizer.from_pretrained(adapter_id) # 2. Load Base Modelbase_model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.bfloat16, device_map="auto") # 3. Load LoRA Adaptermodel = PeftModel.from_pretrained(base_model, adapter_id)model.eval() # 4. Formulate Clinical Querymessages = [ { "role": "user", "content": ( "Patient: 65-year-old male with Type 2 Diabetes and CKD Stage 4 (eGFR 22 mL/min/1.73m2).\n" "Prescription: Metformin 1000mg BID, Ciprofloxacin 500mg BID, Lisinopril 20mg OD.\n" "Task: Identify contraindications, DDI risks, and recommended renal dose adjustments." ) }] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.2, do_sample=False ) response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)google/gemma-4-E2B-itlinear)SFTTrainer) & PEFTThis model is intended for research, educational, and clinical decision support purposes only. It is not an autonomous diagnostic device. All pharmacological recommendations, dosage titrations, and clinical decisions must be reviewed and verified by a licensed healthcare professional or clinical pharmacologist.
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel base_model_id = "google/gemma-4-E2B-it"adapter_id = "sandeshv12/gemma4pharma" # 1. Load Tokenizertokenizer = AutoTokenizer.from_pretrained(adapter_id) # 2. Load Base Modelbase_model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.bfloat16, device_map="auto") # 3. Load LoRA Adaptermodel = PeftModel.from_pretrained(base_model, adapter_id)model.eval() # 4. Formulate Clinical Querymessages = [ { "role": "user", "content": ( "Patient: 65-year-old male with Type 2 Diabetes and CKD Stage 4 (eGFR 22 mL/min/1.73m2).\n" "Prescription: Metformin 1000mg BID, Ciprofloxacin 500mg BID, Lisinopril 20mg OD.\n" "Task: Identify contraindications, DDI risks, and recommended renal dose adjustments." ) }] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.2, do_sample=False ) response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)