metadata
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: other
datasets:
- mlabonne/orpo-dpo-mix-40k
- Open-Orca/SlimOrca-Dedup
- jondurbin/airoboros-3.2
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- ruslanmv/ai-medical-chatbot
Medichat-Llama3-8B
Built upon the powerful LLaMa-3 architecture and fine-tuned on an extensive dataset of health information, this model leverages its vast medical knowledge to offer clear, comprehensive answers.
The following YAML configuration was used to produce this model:
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:
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))