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Wildstash
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Model Details
Model Provider
Wildstash
Model Tree
openai/gpt-oss-20b
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel # Load base modelbase_model = AutoModelForCausalLM.from_pretrained( "openai/gpt-oss-20b", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) # Load tokenizertokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b", trust_remote_code=True) # Load LoRA adaptermodel = PeftModel.from_pretrained(base_model, "Wildstash/dental-gpt-qlora") # Example usagemessages = [ {"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)The model was fine-tuned on 2,494 dental patient cases covering:
The fine-tuned model demonstrates improved:
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}}from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel # Load base modelbase_model = AutoModelForCausalLM.from_pretrained( "openai/gpt-oss-20b", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) # Load tokenizertokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b", trust_remote_code=True) # Load LoRA adaptermodel = PeftModel.from_pretrained(base_model, "Wildstash/dental-gpt-qlora") # Example usagemessages = [ {"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)@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}}