|
--- |
|
base_model: |
|
- Undi95/Llama-3-Unholy-8B |
|
- Locutusque/llama-3-neural-chat-v1-8b |
|
- ruslanmv/Medical-Llama3-8B-16bit |
|
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 |
|
--- |
|
|
|
### 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: |
|
|
|
```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) |
|
|
|
``` |
|
|
|
## Quants |
|
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), |
|
|
|
|
|
## 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. |
