Instructions to use BiMediX/BiMediX-Bi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BiMediX/BiMediX-Bi with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BiMediX/BiMediX-Bi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BiMediX/BiMediX-Bi")
model = AutoModelForCausalLM.from_pretrained("BiMediX/BiMediX-Bi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BiMediX/BiMediX-Bi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BiMediX/BiMediX-Bi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BiMediX/BiMediX-Bi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BiMediX/BiMediX-Bi

SGLang

How to use BiMediX/BiMediX-Bi with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BiMediX/BiMediX-Bi" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BiMediX/BiMediX-Bi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BiMediX/BiMediX-Bi" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BiMediX/BiMediX-Bi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BiMediX/BiMediX-Bi with Docker Model Runner:
```
docker model run hf.co/BiMediX/BiMediX-Bi
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for BiMediX-Bilingual

Model Details

Name: BiMediX
Version: 1.0
Type: Bilingual Medical Mixture of Experts Large Language Model (LLM)
Languages: English, Arabic
Model Architecture: Mixtral-8x7B-Instruct-v0.1
Training Data: BiMed1.3M, a bilingual dataset with diverse medical interactions.

Intended Use

Primary Use: Medical interactions in both English and Arabic.
Capabilities: MCQA, closed QA and chats.

Getting Started

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "BiMediX/BiMediX-Bi"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello BiMediX! I've been experiencing increased tiredness in the past week."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Procedure

Dataset: BiMed1.3M, 632 million healthcare specialized tokens.
QLoRA Adaptation: Implements a low-rank adaptation technique, incorporating learnable low-rank adapter weights into the experts and the routing network. This results in training about 4% of the original parameters.
Training Resources: The model underwent training on approximately 632 million tokens from the Arabic-English corpus, including 288 million tokens exclusively for English.

Model Performance

Benchmarks: Outperforms the baseline model and Jais-30B in medical evaluations.

Model	CKG	CBio	CMed	MedGen	ProMed	Ana	MedMCQA	MedQA	PubmedQA	AVG
Jais-30B	57.4	55.2	46.2	55.0	46.0	48.9	40.2	31.0	75.5	50.6
Mixtral-8x7B	59.1	57.6	52.6	59.5	53.3	54.4	43.2	40.6	74.7	55.0
BiMediX (Bilingual)	70.6	72.2	59.3	74.0	64.2	59.6	55.8	54.0	78.6	65.4

Safety and Ethical Considerations

Potential issues: hallucinations, toxicity, stereotypes.
Usage: Research purposes only.

Accessibility

Availability: BiMediX GitHub Repository.
arxiv.org/abs/2402.13253

Authors

Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer Salman Khan, Timothy Baldwin, Hisham Cholakkal
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)

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Paper for BiMediX/BiMediX-Bi

BiMediX: Bilingual Medical Mixture of Experts LLM

Paper • 2402.13253 • Published Feb 20, 2024