Dr_Samantha_7b_mistral
Dr. Samantha represents a blend of AI in healthcare, offering a balance between technical medical knowledge and the softer skills of communication and empathy, crucial for patient interaction and care.
This model is a merge of the following models made with mergekit(https://github.com/cg123/mergekit):
Has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding of the Samantha-7b model.
As both a medical consultant and personal counselor, Dr.Samantha could effectively support both physical and mental wellbeing - important for whole-person care.
🧩 Configuration
slices:
- sources:
- model: segmed/MedMistral-7B-v0.1
layer_range: [0, 32]
- model: Guilherme34/Samantha-v2
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
OpenLLM Evaluation
Details about that can be found here. Overall, with regards to the subjects related to medical domain, the model's performance is as follows:
| Subject | Accuracy |
|---|---|
| Clinical Knowledge | 70.57% |
| Medical Genetics | 71.00% |
| Human Aging | 69.06% |
| Human Sexuality | 75.57% |
| College Medicine | 63.01% |
| Anatomy | 58.52% |
| College Biology | 72.92% |
| College Medicine | 63.01% |
| High School Biology | 75.48% |
| Professional Medicine | 65.44% |
| Nutrition | 76.79% |
| High School Psychology | 83.12% |
| Professional Psychology | 65.35% |
| Virology | 53.61% |
| Average | 68.82% |
Dr. Samantha performs reasonably well on various medical-related subjects, averaging 68.82% overall in medical sciences, biology, and psychology, however it's important to note that medical diagnosis and treatment decisions often require a much higher level of accuracy, reliability, and context awareness.
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "sethuiyer/Dr_Samantha_7b_mistral"
ask_samantha = '''
Symptoms:
Dizziness, headache and nausea.
What is the differnetial diagnosis?
'''
messages = [{"role": "system", "content": '''You are Doctor Samantha, a virtual AI doctor known for your friendly and approachable demeanor,
combined with a deep expertise in the medical field. You're here to provide professional, empathetic, and knowledgeable advice on health-related inquiries.
You'll also provide differential diagnosis. If you're unsure about any information, Don't share false information.'''},
{"role": "user", "content": f"{ask_samantha}"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Dizziness, headache and nausea can be caused by a variety of conditions, including:
Vertigo: A sensation of spinning or dizziness that can be caused by problems with the inner ear or brain.
Migraine: A type of headache that can cause throbbing pain, sensitivity to light and sound, and nausea.
Concussion: A type of traumatic brain injury that can cause dizziness, headache, and nausea.
Dehydration: A lack of fluids in the body can cause dizziness, headache, and nausea.
Low blood sugar: A drop in blood sugar levels can cause dizziness, headache, and nausea.
It's important to consult with a healthcare professional for a proper diagnosis and treatment plan.
GGUF Files
GGUF files are available at s3nh/sethuiyer-Dr_Samantha_7b_mistral-GGUF, thanks to s3nh
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 59.25 |
| AI2 Reasoning Challenge (25-Shot) | 60.41 |
| HellaSwag (10-Shot) | 83.65 |
| MMLU (5-Shot) | 63.14 |
| TruthfulQA (0-shot) | 41.37 |
| Winogrande (5-shot) | 75.45 |
| GSM8k (5-shot) | 31.46 |