Instructions to use Wildstash/dental-ai-clinical-decision-support with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wildstash/dental-ai-clinical-decision-support with Transformers:

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

pipe = pipeline("text-generation", model="Wildstash/dental-ai-clinical-decision-support")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Wildstash/dental-ai-clinical-decision-support", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Wildstash/dental-ai-clinical-decision-support with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Wildstash/dental-ai-clinical-decision-support

SGLang

How to use Wildstash/dental-ai-clinical-decision-support 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 "Wildstash/dental-ai-clinical-decision-support" \
    --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": "Wildstash/dental-ai-clinical-decision-support",
		"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 "Wildstash/dental-ai-clinical-decision-support" \
        --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": "Wildstash/dental-ai-clinical-decision-support",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Wildstash/dental-ai-clinical-decision-support with Docker Model Runner:
```
docker model run hf.co/Wildstash/dental-ai-clinical-decision-support
```

Wildstash commited on Oct 28, 2025

Commit

93da31d

verified ·

1 Parent(s): 5f560b3

docs: add concise Quick guide

Browse files

Files changed (1) hide show

README.md +22 -0

README.md CHANGED Viewed

@@ -71,3 +71,25 @@ Open source model for evidence‑based dental decision support and chairside gui
 - Management plan
 - Antibiotics and dosing (if indicated)
 - Follow-up protocol

 - Management plan
 - Antibiotics and dosing (if indicated)
 - Follow-up protocol
+## Quick guide (read this)
+- What it is: Chat assistant for structured treatment planning and clinical decision support (SFT).
+- What it covers: endodontics, periodontics, oral surgery, prosthodontics, ortho, pediatrics.
+- Why trust it: trained on 2,494 expert‑validated synthetic cases; guideline‑aligned.
+- How to use: provide patient context (age, vitals, symptoms, exam); ask for differential, management, abx, follow‑up.
+- Safety: HIPAA‑friendly (no real patient data); outputs assist, not replace, clinical judgment.
+### Dataset statistics
+- 2,494 cases; multi‑specialty coverage; structured JSON (presentation → assessment → plan).
+- Source: `Wildstash/dental-2.5k-instruct`.
+### Key features
+- Comprehensive dental coverage; evidence‑based plans; guideline adherence; step‑wise reasoning.
+### Training details
+- Method: LoRA (PEFT), 4‑bit; base: 20B decoder.
+- Optimizations: grad checkpointing; mixed precision; multi‑GPU.
+### Expert validation
+- Practicing dentists graded sample cases; refined to improve plausibility and completeness.