Instructions to use alibidaran/Gemma2_Virtual_doctor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alibidaran/Gemma2_Virtual_doctor with Transformers:

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

pipe = pipeline("text-generation", model="alibidaran/Gemma2_Virtual_doctor")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alibidaran/Gemma2_Virtual_doctor", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alibidaran/Gemma2_Virtual_doctor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alibidaran/Gemma2_Virtual_doctor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alibidaran/Gemma2_Virtual_doctor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alibidaran/Gemma2_Virtual_doctor

SGLang

How to use alibidaran/Gemma2_Virtual_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 "alibidaran/Gemma2_Virtual_doctor" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alibidaran/Gemma2_Virtual_doctor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "alibidaran/Gemma2_Virtual_doctor" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alibidaran/Gemma2_Virtual_doctor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use alibidaran/Gemma2_Virtual_doctor with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for alibidaran/Gemma2_Virtual_doctor to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for alibidaran/Gemma2_Virtual_doctor to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alibidaran/Gemma2_Virtual_doctor to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="alibidaran/Gemma2_Virtual_doctor",
    max_seq_length=2048,
)

Docker Model Runner
How to use alibidaran/Gemma2_Virtual_doctor with Docker Model Runner:
```
docker model run hf.co/alibidaran/Gemma2_Virtual_doctor
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Model Details

Model Description

This model is fined tune based on Google's Gemma model for creating virtual doctor or medical Asistant. It can be used in medical and healthcare AI assitant apps and chatbots.

Developed by: [Ali Bidaran]

Uses

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer

model_id = "alibidaran/Gemma2_Virtual_doctor"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)


tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0})

prompt = " Hi doctor, I feel a pain on my ankle, I walk hardly and with pain what do you recommend me?"
text=f"<s> ###Human: {prompt} ###Asistant: "
inputs=tokenizer(text,return_tensors='pt').to('cuda')
with torch.no_grad():
    outputs=model.generate(**inputs,max_new_tokens=200,do_sample=True,top_p=0.92,top_k=10,temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Parameters

    per_device_train_batch_size=1,
    gradient_accumulation_steps=8,
    warmup_steps=2,
    #max_steps=200,
   
    num_train_epochs=1,
    learning_rate=2e-4,
    fp16=True,
    logging_steps=100,
    output_dir="outputs",
    optim="paged_adamw_8bit",
    save_steps=500,
    ddp_find_unused_parameters=False // for training on multiple GPU

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for alibidaran/Gemma2_Virtual_doctor

Quantizations

1 model