metadata
language:
- en
license: apache-2.0
base_model:
- google/gemma-3-270m-it
tags:
- medical
- lora
- fine-tuned
- merged
pipeline_tag: text-generation
datasets:
- ericrisco/medrescue
Medical Fine-tuned Model
This model is a fine-tuned version of gemma-3-270m-it using LoRA (Low-Rank Adaptation) on medical data just for testing purpose
Model Details
- Base Model: google/gemma-3-270m
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Domain: Medical/Healthcare
- Merged: Yes, LoRA adapters have been merged with the base model
Training Information
- Training Steps: 813
- Learning Rate: 3e-4
- LoRA Rank: 64
- LoRA Alpha: 16
- Target Modules: q_proj, k_proj, v_proj, o_proj
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("tulas/gemma-3-270m-medical")
tokenizer = AutoTokenizer.from_pretrained("tulas/gemma-3-270m-medical")
# Generate text
inputs = tokenizer("Patient presents with chest pain and", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Intended Use
This model is NOT intended for medical text generation but for testing purpose only
Limitations
- This model should not be used for actual medical diagnosis
- Always consult healthcare professionals for medical decisions
- Model outputs should be verified by medical experts
License
This model is released under the Apache 2.0 license.