Wildstash

Dental-AImodel-20B

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README

License: apache-2.0

Model Details

Base Model: openai/gpt-oss-20b
Fine-tuning Method: QLoRA (Quantized Low-Rank Adaptation)
Training Data: 2,494 dental patient cases
Trainable Parameters: 192 parameters (0.0761% of base model)
Training Framework: PyTorch + PEFT

Training Configuration

LoRA Rank: 32
LoRA Alpha: 64
LoRA Dropout: 0.05
Learning Rate: 1e-5
Batch Size: 2 (effective batch size: 32)
Epochs: 2
Max Sequence Length: 4096

Usage

python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "openai/gpt-oss-20b",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b", trust_remote_code=True)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Wildstash/dental-gpt-qlora")

# Example usage
messages = [
    {"role": "system", "content": "You are an expert dental clinician providing comprehensive patient care."},
    {"role": "user", "content": "Please evaluate this dental patient: 45M with severe tooth pain, swelling, fever 101°F."}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=500,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)

Training Data

The model was fine-tuned on 2,494 dental patient cases covering:

Emergency dental situations
Periodontal disease management
Pediatric dental care
Oral pathology evaluation
Treatment planning and follow-up care

Performance

The fine-tuned model demonstrates improved:

Structured clinical responses
Evidence-based treatment recommendations
Appropriate urgency assessment
Patient safety considerations
Clinical guideline adherence

Limitations

This model is for educational/research purposes only
Not intended for direct clinical decision-making
Always consult with qualified dental professionals
May not cover all dental specialties or rare conditions

Citation

If you use this model, please cite:

bibtex
@misc{dental-gpt-qlora,
  title={Dental GPT: A Fine-tuned Language Model for Dental Clinical Decision Support},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/Wildstash/dental-gpt-qlora}
}

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Model Details

Model Provider

Wildstash

Model Tree

Base

openai/gpt-oss-20b

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

Model Details

Base Model: openai/gpt-oss-20b
Fine-tuning Method: QLoRA (Quantized Low-Rank Adaptation)
Training Data: 2,494 dental patient cases
Trainable Parameters: 192 parameters (0.0761% of base model)
Training Framework: PyTorch + PEFT

Training Configuration

LoRA Rank: 32
LoRA Alpha: 64
LoRA Dropout: 0.05
Learning Rate: 1e-5
Batch Size: 2 (effective batch size: 32)
Epochs: 2
Max Sequence Length: 4096

Usage

python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "openai/gpt-oss-20b",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b", trust_remote_code=True)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Wildstash/dental-gpt-qlora")

# Example usage
messages = [
    {"role": "system", "content": "You are an expert dental clinician providing comprehensive patient care."},
    {"role": "user", "content": "Please evaluate this dental patient: 45M with severe tooth pain, swelling, fever 101°F."}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=500,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)

Training Data

The model was fine-tuned on 2,494 dental patient cases covering:

Emergency dental situations
Periodontal disease management
Pediatric dental care
Oral pathology evaluation
Treatment planning and follow-up care

Performance

The fine-tuned model demonstrates improved:

Structured clinical responses
Evidence-based treatment recommendations
Appropriate urgency assessment
Patient safety considerations
Clinical guideline adherence

Limitations

This model is for educational/research purposes only
Not intended for direct clinical decision-making
Always consult with qualified dental professionals
May not cover all dental specialties or rare conditions

Citation

If you use this model, please cite:

bibtex
@misc{dental-gpt-qlora,
  title={Dental GPT: A Fine-tuned Language Model for Dental Clinical Decision Support},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/Wildstash/dental-gpt-qlora}
}