--- base_model: - Undi95/Llama-3-Unholy-8B - Locutusque/llama-3-neural-chat-v1-8b - ruslanmv/Medical-Llama3-8B-16bit library_name: transformers tags: - mergekit - merge license: llama2 language: - en --- ### Medichat-Llama3-8B The following YAML configuration was used to produce this model: ```yaml models: - model: Undi95/Llama-3-Unholy-8B parameters: weight: [0.25, 0.35, 0.45, 0.35, 0.25] density: [0.1, 0.25, 0.5, 0.25, 0.1] - model: Locutusque/llama-3-neural-chat-v1-8b - model: ruslanmv/Medical-Llama3-8B-16bit parameters: weight: [0.55, 0.45, 0.35, 0.45, 0.55] density: [0.1, 0.25, 0.5, 0.25, 0.1] merge_method: dare_ties base_model: Locutusque/llama-3-neural-chat-v1-8b parameters: int8_mask: true dtype: bfloat16 ``` ### Usage: ```python from transformers import AutoTokenizer, AutoModelForCausalLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("sethuiyer/Medichat-Llama3-8B") model = AutoModelForCausalLM.from_pretrained("sethuiyer/Medichat-Llama3-8B").to("cuda") # Function to format and generate response with prompt engineering using a chat template def askme(question): sys_message = ''' You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help. ''' # Create messages structured for the chat template messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}] # Applying chat template prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True) # Adjust max_new_tokens for longer responses # Extract and return the generated text answer = tokenizer.batch_decode(outputs)[0].strip() return answer # Example usage question = ''' Symptoms: Dizziness, headache and nausea. What is the differnetial diagnosis? ''' print(askme(question)) ```