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
```

dental-ai-clinical-decision-support / README.md

Wildstash

docs: add concise Quick guide

93da31d verified 11 months ago

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	---
	license: apache-2.0
	language:
	- en
	pretty_name: Dental AI for Clinical Decision Support
	base_model:
	- openai/gpt-oss-20b
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- structured treatment planning
	- structured output
	- supervised fine tuning
	- sft
	- dental
	- dentistry
	- dental ai
	- clinical decision support
	- diagnosis
	- treatment planning
	- evidence based
	- endodontics
	- periodontics
	- oral surgery
	- prosthodontics
	- orthodontics
	- pediatric dentistry
	- differential diagnosis
	- risk assessment
	- triage
	- chairside assistant
	- point of care
	- healthcare
	- clinical reasoning
	- synthetic data
	datasets:
	- Wildstash/dental-2.5k-instruct
	model-index:
	- name: Wildstash/dental-ai-clinical-decision-support
	results:
	- task:
	type: text-generation
	name: Dental clinical QA (internal heuristic)
	dataset:
	name: Wildstash/dental-2.5k-instruct
	type: Wildstash/dental-2.5k-instruct
	split: test
	metrics:
	- type: clinical_guideline_adherence
	value: 0.90
	widget:
	- text: "Emergency: severe tooth pain, swelling, fever 101°F. Provide differential, immediate care, antibiotics, follow up."
	parameters: {max_new_tokens: 400, temperature: 0.7}
	- text: "Generalized periodontitis with 6–8 mm pockets. Stage/grade and phased treatment plan."
	parameters: {max_new_tokens: 350, temperature: 0.7}
	---

	# Dental AI for Clinical Decision Support

	Chat assistant for structured treatment planning and clinical decision support (SFT)


	Open source model for evidence‑based dental decision support and chairside guidance.


	## 🏆 Awards
	- Winner: Most Useful Fine‑Tune (OpenAI Open Model Hackathon) — see Devpost: https://devpost.com/software/dental-assessment-gpt

	## Structured output
	- Differential diagnosis
	- 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.