Gemma-4-E2B-IT-SFT-RLVR-Medical

Gemma-4-E2B-it fine-tuned on PubMedQA using SFT and RLVR.
Also check out the training code on GitHub.
Quantized models are available here.

Setup

#!pip install transformers, torch, accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lukasdrews/Gemma-4-E2B-IT-SFT-RLVR-Medical")
model = AutoModelForCausalLM.from_pretrained("lukasdrews/Gemma-4-E2B-IT-SFT-RLVR-Medical")
messages = [
 {
 "role": "user",
 "content": [
 {"type": "text", "text": "Do GEC produce and bear factor H under complement attack?"}
 ]
 },
]
inputs = tokenizer.apply_chat_template(
 messages,
 add_generation_prompt=True,
 tokenize=True,
 return_dict=True,
 return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Benchmarks

Base model