初始化项目，由ModelHub XC社区提供模型
Model: sethuiyer/Medichat-Llama3-8B Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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base_model:
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- Undi95/Llama-3-Unholy-8B
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- Locutusque/llama-3-neural-chat-v1-8b
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- ruslanmv/Medical-Llama3-8B-16bit
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library_name: transformers
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tags:
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- mergekit
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- merge
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- medical
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license: other
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datasets:
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- mlabonne/orpo-dpo-mix-40k
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- Open-Orca/SlimOrca-Dedup
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- jondurbin/airoboros-3.2
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- microsoft/orca-math-word-problems-200k
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- m-a-p/Code-Feedback
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- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
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- ruslanmv/ai-medical-chatbot
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model-index:
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- name: Medichat-Llama3-8B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 59.13
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 82.9
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 60.35
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 49.65
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 78.93
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 60.35
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
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name: Open LLM Leaderboard
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language:
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- en
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---
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### Medichat-Llama3-8B
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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.
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This model is generally better for accurate and informative responses, particularly for users seeking in-depth medical advice.
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The following YAML configuration was used to produce this model:
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```yaml
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models:
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- model: Undi95/Llama-3-Unholy-8B
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parameters:
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weight: [0.25, 0.35, 0.45, 0.35, 0.25]
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density: [0.1, 0.25, 0.5, 0.25, 0.1]
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- model: Locutusque/llama-3-neural-chat-v1-8b
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- model: ruslanmv/Medical-Llama3-8B-16bit
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parameters:
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weight: [0.55, 0.45, 0.35, 0.45, 0.55]
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density: [0.1, 0.25, 0.5, 0.25, 0.1]
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merge_method: dare_ties
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base_model: Locutusque/llama-3-neural-chat-v1-8b
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parameters:
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int8_mask: true
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dtype: bfloat16
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```
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# Comparision Against Dr.Samantha 7B
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| Subject | Medichat-Llama3-8B Accuracy (%) | Dr. Samantha Accuracy (%) |
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|-------------------------|---------------------------------|---------------------------|
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| Clinical Knowledge | 71.70 | 52.83 |
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| Medical Genetics | 78.00 | 49.00 |
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| Human Aging | 70.40 | 58.29 |
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| Human Sexuality | 73.28 | 55.73 |
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| College Medicine | 62.43 | 38.73 |
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| Anatomy | 64.44 | 41.48 |
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| College Biology | 72.22 | 52.08 |
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| High School Biology | 77.10 | 53.23 |
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| Professional Medicine | 63.97 | 38.73 |
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| Nutrition | 73.86 | 50.33 |
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| Professional Psychology | 68.95 | 46.57 |
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| Virology | 54.22 | 41.57 |
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| High School Psychology | 83.67 | 66.60 |
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| **Average** | **70.33** | **48.85** |
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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.
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### Usage:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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class MedicalAssistant:
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def __init__(self, model_name="sethuiyer/Medichat-Llama3-8B", device="cuda"):
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self.device = device
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.model = AutoModelForCausalLM.from_pretrained(model_name).to(self.device)
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self.sys_message = '''
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You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
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provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
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'''
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def format_prompt(self, question):
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messages = [
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{"role": "system", "content": self.sys_message},
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{"role": "user", "content": question}
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]
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prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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return prompt
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def generate_response(self, question, max_new_tokens=512):
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prompt = self.format_prompt(question)
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inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
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with torch.no_grad():
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outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens, use_cache=True)
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answer = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
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return answer
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if __name__ == "__main__":
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assistant = MedicalAssistant()
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question = '''
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Symptoms:
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Dizziness, headache, and nausea.
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What is the differential diagnosis?
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'''
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response = assistant.generate_response(question)
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print(response)
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```
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## Quants
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Thanks to [Quant Factory](https://huggingface.co/QuantFactory), the quantized version of this model is available at [QuantFactory/Medichat-Llama3-8B-GGUF](https://huggingface.co/QuantFactory/Medichat-Llama3-8B-GGUF),
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## Ollama
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This model is now also available on Ollama. You can use it by running the command ```ollama run monotykamary/medichat-llama3``` in your
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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
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a Google Colab backend.
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