base_model, library_name, tags, license, datasets, model-index, language

base_model	library_name	tags	license	datasets	model-index	language
Undi95/Llama-3-Unholy-8B Locutusque/llama-3-neural-chat-v1-8b ruslanmv/Medical-Llama3-8B-16bit	transformers	mergekit merge medical	other	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	name results Medichat-Llama3-8B task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 59.13 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 82.9 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 60.35 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 49.65 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 78.93 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 60.35 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B Open LLM Leaderboard	en

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.

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)

Quants

Thanks to Quant Factory, the quantized version of this model is available at QuantFactory/Medichat-Llama3-8B-GGUF,

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.