Instructions to use eugenemaver/gemma-2b-mini-doctor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eugenemaver/gemma-2b-mini-doctor with Transformers:

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

pipe = pipeline("text-generation", model="eugenemaver/gemma-2b-mini-doctor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eugenemaver/gemma-2b-mini-doctor")
model = AutoModelForCausalLM.from_pretrained("eugenemaver/gemma-2b-mini-doctor", device_map="auto")
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 Settings

vLLM

How to use eugenemaver/gemma-2b-mini-doctor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eugenemaver/gemma-2b-mini-doctor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eugenemaver/gemma-2b-mini-doctor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eugenemaver/gemma-2b-mini-doctor

SGLang

How to use eugenemaver/gemma-2b-mini-doctor 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 "eugenemaver/gemma-2b-mini-doctor" \
    --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": "eugenemaver/gemma-2b-mini-doctor",
		"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 "eugenemaver/gemma-2b-mini-doctor" \
        --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": "eugenemaver/gemma-2b-mini-doctor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eugenemaver/gemma-2b-mini-doctor with Docker Model Runner:
```
docker model run hf.co/eugenemaver/gemma-2b-mini-doctor
```

Gemma-2B-Mini-Doctor

This is a fine-tuned version of the Gemma-2B model specifically adapted for medical-related tasks.

Model Details

Model Name: Gemma-2B-Mini-Doctor
Base Model: Gemma-2B
Fine-tuned by: Yevhen Solovei | Maverkick
Fine-tuning Dataset: mamachang/medical-reasoning
Number of Parameters: 2 billion

Training Details

Training Epochs: 3
Learning Rate: 2e-5
Batch Size: 16
Optimizer: AdamW

Intended Use

Use Cases: Medical question answering, medical text generation
Limitations: Not suitable for real-time medical advice, should not be used as a substitute for professional medical advice.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gemma-2b-mini-doctor")
tokenizer = AutoTokenizer.from_pretrained("gemma-2b-mini-doctor")

inputs = tokenizer("What are the symptoms of flu?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 7

Safetensors

Model size

3B params

Tensor type

F16