Create README.md

README.md

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+---
+base_model: sethuiyer/Medichat-Llama3-8B
+library_name: transformers
+tags:
+- mergekit
+- merge
+- medical
+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
+model-index:
+- name: Medichat-Llama3-8B
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 59.13
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 82.9
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 60.35
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 49.65
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 78.93
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 60.35
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
+      name: Open LLM Leaderboard
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Medichat-Llama3-8B-GGUF
+This is quantized version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/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:
+
+```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
+
+```
+
+# 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](sethuiyer/Dr_Samantha-7b) model in terms of subject-specific knowledge and accuracy.
+
+### Usage:
+```python
+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](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on 
+a Google Colab backend.