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---
language: en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- ruslanmv
- llama
- trl
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- ruslanmv/ai-medical-dataset
widget:
- example_title: ai-medical-model-32bit
messages:
- role: system
content: You are an expert and experienced from the healthcare and biomedical
domain with extensive medical knowledge and practical experience.
- role: user
content: What was the main cause of the inflammatory CD4+ T cells?
output:
text: Answer I'm happy to help! The main cause of inflammatory CD4+ T cells is
a complex process that involves multiple factors. However, some of the key triggers
include 1. Activation of CD4+ T cells CD4+ T cells are activated by antigens,
cytokines, and other signals, leading to their proliferation and differentiation
into effector cells. 2. Cytokine production Activated CD4+ T cells produce cytokines
such as interleukin-2 (IL-2), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha
(TNF-α), which promote inflammation and immune responses. 3. Chemokine production
CD4+ T cells also produce chemokines, such as CCL3, CCL4, and CCL5, which attract
other immune cells to the site of inflammation. 4. Toll-like receptor (TLR)
activation TLRs are pattern recognition receptors that recognize pathogen-associated
molecular patterns (PAMPs) and activate CD4+ T cells. 5. Bacterial or viral
infections Infections caused by bacteria, viruses, or fungi can trigger the
activation of CD4+ T cells and the production of cytokines and chemokines
model-index:
- name: ai-medical-model-32bit
results: []
---
# ai-medical-model-32bit: Fine-Tuned Llama3 for Technical Medical Questions
[](https://ruslanmv.com/)
This repository provides a fine-tuned version of the powerful Llama3 8B Instruct model, specifically designed to answer medical questions in an informative way.
It leverages the rich knowledge contained in the AI Medical Dataset ([ruslanmv/ai-medical-dataset](https://huggingface.co/datasets/ruslanmv/ai-medical-dataset)).
**Model & Development**
- **Developed by:** ruslanmv
- **License:** Apache-2.0
- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct
**Key Features**
- **Medical Focus:** Optimized to address health-related inquiries.
- **Knowledge Base:** Trained on a comprehensive medical dataset.
- **Text Generation:** Generates informative and potentially helpful responses.
**Installation**
This model is accessible through the Hugging Face Transformers library. Install it using pip:
```bash
!python -m pip install --upgrade pip
!pip3 install torch==2.2.1 torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121
!pip install bitsandbytes accelerate
```
**Usage Example**
Here's a Python code snippet demonstrating how to interact with the `ai-medical-model-32bit` model and generate answers to your medical questions:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
model_name = "ruslanmv/ai-medical-model-32bit"
device_map = 'auto'
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True,
use_cache=False,
device_map=device_map
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
def askme(question):
prompt = f"<|start_header_id|>system<|end_header_id|> You are a Medical AI chatbot assistant. <|eot_id|><|start_header_id|>User: <|end_header_id|>This is the question: {question}<|eot_id|>"
# Tokenizing the input and generating the output
#prompt = f"{question}"
# Tokenizing the input and generating the output
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# Try Remove the prompt
try:
# Split the answer at the first line break, assuming system intro and question are on separate lines
answer_parts = answer.split("\n", 1)
# If there are multiple parts, consider the second part as the answer
if len(answer_parts) > 1:
answers = answer_parts[1].strip() # Remove leading/trailing whitespaces
else:
answers = "" # If no split possible, set answer to empty string
print(f"Answer: {answers}")
except:
print(answer)
# Example usage
# - Question: Make the question.
question="What was the main cause of the inflammatory CD4+ T cells?"
askme(question)
```
the type of answer is :
```
Answer: I'm happy to help!
The main cause of inflammatory CD4+ T cells is a complex process that involves multiple factors. However, some of the key triggers include:
1. Activation of CD4+ T cells: CD4+ T cells are activated by antigens, cytokines, and other signals, leading to their proliferation and differentiation into effector cells.
2. Cytokine production: Activated CD4+ T cells produce cytokines such as interleukin-2 (IL-2), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α), which promote inflammation and immune responses.
3. Chemokine production: CD4+ T cells also produce chemokines, such as CCL3, CCL4, and CCL5, which attract other immune cells to the site of inflammation.
4. Toll-like receptor (TLR) activation: TLRs are pattern recognition receptors that recognize pathogen-associated molecular patterns (PAMPs) and activate CD4+ T cells.
5. Bacterial or viral infections: Infections caused by bacteria, viruses, or fungi can trigger the activation of CD4+ T cells and the production of cytokines and chemokines
```
**Important Note**
This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.
**License**
This model is distributed under the Apache License 2.0 (see LICENSE file for details).
**Contributing**
We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.
**Disclaimer**
While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ruslanmv__ai-medical-model-32bit)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.67|
|AI2 Reasoning Challenge (25-Shot)|61.43|
|HellaSwag (10-Shot) |78.69|
|MMLU (5-Shot) |68.10|
|TruthfulQA (0-shot) |51.99|
|Winogrande (5-shot) |75.77|
|GSM8k (5-shot) |70.05|
|