Medichat-Llama3-8B-GGUF
This is quantized version of sethuiyer/Medichat-Llama3-8B created using llama.cpp
Model Description
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.
This model is generally better for accurate and informative responses, particularly for users seeking in-depth medical advice.
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
Comparision Against Dr.Samantha 7B
Subject | Medichat-Llama3-8B Accuracy (%) | Dr. Samantha Accuracy (%) |
---|---|---|
Clinical Knowledge | 71.70 | 52.83 |
Medical Genetics | 78.00 | 49.00 |
Human Aging | 70.40 | 58.29 |
Human Sexuality | 73.28 | 55.73 |
College Medicine | 62.43 | 38.73 |
Anatomy | 64.44 | 41.48 |
College Biology | 72.22 | 52.08 |
High School Biology | 77.10 | 53.23 |
Professional Medicine | 63.97 | 38.73 |
Nutrition | 73.86 | 50.33 |
Professional Psychology | 68.95 | 46.57 |
Virology | 54.22 | 41.57 |
High School Psychology | 83.67 | 66.60 |
Average | 70.33 | 48.85 |
The current model demonstrates a substantial improvement over the previous Dr. Samantha model in terms of subject-specific knowledge and accuracy.
Usage:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
class MedicalAssistant:
def __init__(self, model_name="sethuiyer/Medichat-Llama3-8B", device="cuda"):
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name).to(self.device)
self.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.
'''
def format_prompt(self, question):
messages = [
{"role": "system", "content": self.sys_message},
{"role": "user", "content": question}
]
prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
return prompt
def generate_response(self, question, max_new_tokens=512):
prompt = self.format_prompt(question)
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens, use_cache=True)
answer = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
return answer
if __name__ == "__main__":
assistant = MedicalAssistant()
question = '''
Symptoms:
Dizziness, headache, and nausea.
What is the differential diagnosis?
'''
response = assistant.generate_response(question)
print(response)
Ollama
This model is now also available on Ollama. You can use it by running the command ollama run monotykamary/medichat-llama3
in your
terminal. If you have limited computing resources, check out this video to learn how to run it on
a Google Colab backend.
- Downloads last month
- 493
Model tree for QuantFactory/Medichat-Llama3-8B-GGUF
Base model
sethuiyer/Medichat-Llama3-8BDatasets used to train QuantFactory/Medichat-Llama3-8B-GGUF
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard59.130
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard82.900
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.350
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard49.650
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.930
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard60.350