Instructions to use Hriday75/qwen2.5-3b-oncology-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Hriday75/qwen2.5-3b-oncology-chat with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-oncology-chat") - Notebooks
- Google Colab
- Kaggle
Qwen2.5-3B Oncology Chat Assistant (SFT)
Model Details
Model Description
This is a highly specialized, supervised fine-tuned (SFT) LoRA adapter for the Qwen2.5-3B model. While the Phase 1 model learned dense oncology science and cellular biology, this Phase 2 model was specifically instruction-tuned to communicate those facts with excellent bedside manner. It is designed to act as an empathetic, conversational oncology expert.
- Developed by: Hriday75
- Model type: LoRA Adapter (PEFT)
- Language: English
- Finetuned from base model:
unsloth/Qwen2.5-3B-bnb-4bit(via Phase 1 Oncology LoRA) - Training Stage: Phase 2 (Supervised Fine-Tuning / Instruction Tuned)
Uses
This model is intended to be used in a conversational interface. It is designed to answer patient questions about cancer diagnoses, explain complex biopsy reports or chemotherapy regimens simply, and maintain a highly compassionate and helpful tone.
Training Details
Training Data
This adapter was trained on structured conversational datasets featuring thousands of back-and-forth patient/doctor interactions. The primary dataset used was ruslanmv/ai-medical-chatbot, which was formatted into the standard ChatML structure (System, User, Assistant).
Training Procedure
Trained using unsloth and the TRL (Transformer Reinforcement Learning) SFTTrainer for high-efficiency instruction tuning.
How to Get Started with the Model
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
# 1. Load the base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")
# 2. Attach this Oncology Chat Adapter
model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-oncology-chat")
# 3. Format your chat prompt
messages = [
{"role": "system", "content": "You are a helpful, empathetic oncology expert."},
{"role": "user", "content": "Hi doctor, my biopsy results mention 'invasive ductal carcinoma'. What does this mean, and what happens next?"}
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
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