Instructions to use boweiiismyname/trialmatch-gemma4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use boweiiismyname/trialmatch-gemma4 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-E4B-it-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "boweiiismyname/trialmatch-gemma4")

Transformers

How to use boweiiismyname/trialmatch-gemma4 with Transformers:

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

pipe = pipeline("text-generation", model="boweiiismyname/trialmatch-gemma4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("boweiiismyname/trialmatch-gemma4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use boweiiismyname/trialmatch-gemma4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "boweiiismyname/trialmatch-gemma4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "boweiiismyname/trialmatch-gemma4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/boweiiismyname/trialmatch-gemma4

SGLang

How to use boweiiismyname/trialmatch-gemma4 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 "boweiiismyname/trialmatch-gemma4" \
    --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": "boweiiismyname/trialmatch-gemma4",
		"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 "boweiiismyname/trialmatch-gemma4" \
        --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": "boweiiismyname/trialmatch-gemma4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use boweiiismyname/trialmatch-gemma4 with Docker Model Runner:
```
docker model run hf.co/boweiiismyname/trialmatch-gemma4
```

trialmatch-gemma4

LoRA fine-tune of Gemma 4 E4B for clinical trial eligibility pre-screening. Given a patient profile and a trial's eligibility criteria, the model returns a structured 5-field verdict used inside TrialMatch — a fully offline, CPU-only clinical trial matching app.

Model Details

Model Description

trialmatch-gemma4 is a LoRA adapter trained on top of unsloth/gemma-4-E4B-it-unsloth-bnb-4bit. It is fine-tuned for a single task: reading a structured patient profile + clinical reasoning summary + trial eligibility criteria text and returning a structured eligibility verdict.

The model outputs exactly five fields:

VERDICT: MATCH | PARTIAL | NO
CONFIDENCE: 0–100
REASON: one plain-English sentence
DISQUALIFIERS: exact criterion text, or NONE
NEXT STEP: what the patient should do next

Developed by: Bowei (@boweiiismyname)
Model type: Causal LM — LoRA adapter (text-only fine-tune on a multimodal base)
Language(s): English
License: Apache 2.0
Finetuned from: unsloth/gemma-4-E4B-it-unsloth-bnb-4bit

Model Sources

Repository: Boweii22/TrialMatch
Demo: TrialMatch runs fully offline — see the repo for setup instructions

Uses

Direct Use

Load the LoRA adapter on top of Gemma 4 E4B and pass a patient profile + eligibility criteria to get a structured MATCH/PARTIAL/NO verdict. See How to Get Started below.

Downstream Use

Integrated into TrialMatch as the eligibility matching step. The app extracts a 13-field patient profile from a PDF, searches a local trial database, and passes each trial through this model to produce verdicts displayed in the Gradio UI.

Out-of-Scope Use

Not a medical device. This model does not provide medical advice, diagnosis, or treatment decisions.
Not suitable for final enrollment decisions — it is a pre-screening tool only.
Not suitable for conditions or trial types not represented in the training data.

Bias, Risks, and Limitations

Training data was synthetically generated using Gemma 4 — the model may reflect biases in that generation process.
PARTIAL cases are the hardest to distinguish (both misclassifications in the benchmark were PARTIAL ground-truth labels).
The model has not been validated against real patient records or regulatory enrollment criteria.
Eligibility criteria text is truncated to 1500 characters — very long criteria sections may lose information.

Recommendations

Use exclusively as a pre-screening aid. Any positive result must be reviewed by a licensed physician before a patient contacts a trial site.

How to Get Started with the Model

from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/gemma-4-E4B-it-unsloth-bnb-4bit",
    load_in_4bit=True,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("boweiiismyname/trialmatch-gemma4")
model = PeftModel.from_pretrained(base_model, "boweiiismyname/trialmatch-gemma4")

prompt = """You are a clinical trial eligibility screener.

PATIENT PROFILE:
{patient_profile}

CLINICAL REASONING:
{clinical_reasoning}

ELIGIBILITY CRITERIA:
{eligibility_criteria}

Return exactly:
VERDICT: MATCH|PARTIAL|NO
CONFIDENCE: 0-100
REASON: one sentence
DISQUALIFIERS: criterion text or NONE
NEXT STEP: what to do next"""

inputs = tokenizer(text=prompt.format(
    patient_profile="...",
    clinical_reasoning="...",
    eligibility_criteria="...",
), return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=120, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

120+ synthetic patient profiles generated by Gemma 4 for 60 real recruiting trials sourced from ClinicalTrials.gov. Each profile was run through the base TrialMatch matcher to produce a ground-truth label. Profiles were split roughly 50/50 MATCH-leaning and NO-leaning to prevent class imbalance. Stored as training_data.jsonl.

Training Procedure

Training Hyperparameters

Training regime: bf16 mixed precision (T4 GPU)
LoRA rank (r): 16
LoRA alpha: 16
Target modules: language layers only (finetune_vision_layers=False)
Epochs: 3
Batch size: 2 (effective 8 with gradient accumulation × 4)
Learning rate: 2e-4, linear schedule with 5 warmup steps
Optimizer: adamw_8bit
Max sequence length: 2048

Speeds, Sizes, Times

Hardware: Google Colab T4 GPU (free tier)
Training time: ~1.5 hours
Adapter size: ~50 MB (LoRA weights only)

Evaluation

Testing Data

20 held-out examples from training_data.jsonl, sampled with random.seed(99) (different from training split).

Metrics

Exact-match accuracy on VERDICT field (MATCH / PARTIAL / NO). Confidence score is model-reported (0–100).

Results

Metric	Fine-tuned Gemma 4 E4B
Accuracy	90.0% (18 / 20)
Avg confidence score	96.5 / 100
Correct MATCH verdicts	3 / 3 (100%)
Avg response time (T4 GPU)	25.9s / example

Both misclassifications were PARTIAL ground-truth — the most ambiguous category. All 3 true MATCH cases were correctly identified, which is the critical metric for a pre-screening tool (missing a match costs a patient trial access).

Environmental Impact

Hardware: NVIDIA T4 (Google Colab)
Hours used: ~1.5 hours
Cloud Provider: Google
Compute Region: us-central1

Technical Specifications

Model Architecture

LoRA adapters (r=16) applied to language attention and MLP layers of Gemma 4 E4B. Vision layers are frozen — the adapter is text-only. Base model loaded in 4-bit NF4 quantisation via bitsandbytes.