Instructions to use bisonnetworking/medgemma-health-chat-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bisonnetworking/medgemma-health-chat-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-1.5-4b-it")
model = PeftModel.from_pretrained(base_model, "bisonnetworking/medgemma-health-chat-lora")

Transformers

How to use bisonnetworking/medgemma-health-chat-lora with Transformers:

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

pipe = pipeline("text-generation", model="bisonnetworking/medgemma-health-chat-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bisonnetworking/medgemma-health-chat-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bisonnetworking/medgemma-health-chat-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bisonnetworking/medgemma-health-chat-lora

SGLang

How to use bisonnetworking/medgemma-health-chat-lora 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 "bisonnetworking/medgemma-health-chat-lora" \
    --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": "bisonnetworking/medgemma-health-chat-lora",
		"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 "bisonnetworking/medgemma-health-chat-lora" \
        --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": "bisonnetworking/medgemma-health-chat-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use bisonnetworking/medgemma-health-chat-lora 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 bisonnetworking/medgemma-health-chat-lora 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 bisonnetworking/medgemma-health-chat-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bisonnetworking/medgemma-health-chat-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="bisonnetworking/medgemma-health-chat-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use bisonnetworking/medgemma-health-chat-lora with Docker Model Runner:
```
docker model run hf.co/bisonnetworking/medgemma-health-chat-lora
```

MedGemma Health Chat LoRA Adapter

Model Description

A LoRA adapter fine-tuned on google/medgemma-1.5-4b-it for multi-persona health chat conversations. The model responds as one of six medical personas (primary care, internal medicine, clinical nutritionist, exercise specialist, integrated physician, chronic health specialist) with direct, clinically grounded guidance optimized for mobile display.

Intended Use

Health chat assistant for patient-facing applications
Provides clinical guidance in a conversational format
Designed for mobile-first display (no tables, concise prose)
Each conversation assigns a persona based on topic relevance

Not intended for: diagnosing conditions, replacing professional medical care, or emergency triage.

Personas

Persona	File	Specialty
Primary Care	`primary_care_tight.txt`	General practice, common conditions, preventive care
Internal Medicine	`internal_medicine_tight.txt`	Complex adult medicine, multi-system disorders
Clinical Nutritionist	`clinical_nutritionist_tight.txt`	Dietary interventions, nutritional therapy
Exercise Specialist	`exercise_specialist_tight.txt`	Therapeutic exercise, sports performance, rehab
Best Doctor	`best_doctor.txt`	Cross-specialty integration, OLDCARTS methodology
Chronic Health	`daveshap_chronic_health_ai.txt`	Chronic illness management, diagnostic mysteries

Training Data

Dataset: bisonnetworking/medgemma-health-chat-sft
Training samples: 49,500 conversations
Eval samples: 500 conversations
Format: ShareGPT/conversational JSONL with system (persona prompt) + user + assistant turns
Health context: Simulated Apple Health data (vitals, labs, medications, conditions) injected into user messages

Training Procedure

LoRA Configuration

Rank (r): 32
Alpha: 64
Dropout: 0
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable parameters: 65,576,960 (1.50% of 4,365,656,432 total)

Training Hyperparameters

Epochs: 2
Batch size: 8 (per-device)
Learning rate: 2e-4 (cosine scheduler)
Warmup ratio: 0.03
Optimizer: adamw_8bit
Weight decay: 0.01
Max sequence length: 2048
Precision: bf16
Packing: True

Training Results

Total steps: 12,376
Final training loss: 0.7981
Best eval loss: 0.8779 (step 12,000)
Final eval loss: 0.8779

Compute Infrastructure

Hardware: NVIDIA H100 80GB (Modal serverless)
Software: Unsloth 2025.7.8, Transformers 4.54.0, TRL 0.19.1, PEFT 0.16.0
Training time: ~3 hours
Framework: Modal

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = "google/medgemma-1.5-4b-it"
adapter_model = "bisonnetworking/medgemma-health-chat-lora"

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    base_model, torch_dtype=torch.float16, trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, adapter_model)
model = model.merge_and_unload()

# Generate
messages = [
    {"role": "system", "content": "You are a board-certified Primary Care Physician..."},
    {"role": "user", "content": "I've had a sore throat for 3 days. What should I do?"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Merged Model

A pre-merged 16-bit version is available at bisonnetworking/medgemma-health-chat-merged for direct use without loading a separate adapter.

Evaluation

300 test cases (50 per persona) across 6 categories:

Medical accuracy (14 cases/persona)
Persona adherence (9 cases/persona)
Health context usage (10 cases/persona)
Data integrity / no hallucination (6 cases/persona)
Safety / emergency recognition (6 cases/persona)
Formatting / mobile constraints (5 cases/persona)

Limitations

Fine-tuned on synthetic conversations — quality depends on the base model's medical knowledge
Persona prompts enforce style but cannot guarantee clinical accuracy
No tables in output (mobile constraint) — may reduce clarity for complex comparisons
Not a substitute for professional medical advice

Model Card Contact

bisonnetworking

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