gemma2-qlora-sft-grpo

QLoRA Fine-Tuning of Gemma-2 with SFT and GRPO

Parameter-efficient fine-tuning of google/gemma-2-2b-it for two tasks: Yoda-style text generation via QLoRA + SFT, and mathematical reasoning alignment via GRPO with a composite reward signal.
Trained on an NVIDIA A100-SXM4-40GB using HuggingFace TRL and PEFT.

GitHub: gemma2-qlora-sft-grpo

---

Adapters in This Repository

This repository hosts five LoRA adapter checkpoints corresponding to different stages of the training pipeline:

---

Model Overview

This project investigates two complementary approaches to steering the behaviour of a 2B-parameter instruction-tuned language model through parameter-efficient fine-tuning and reinforcement learning from verifiable rewards (RLVR).

Part A — Style transfer via SFT:
gemma-2-2b-it is fine-tuned with LoRA adapters to translate standard English into Yoda-style syntax using the dvgodoy/yoda_sentences dataset. A synthetic Yoda-style QA dataset is then generated from MuskumPillerum/General-Knowledge using the trained translator, and a second SFT stage trains the model to answer any question in Yoda style.

Part B — Reasoning and style via GRPO:
GRPO is applied to improve mathematical reasoning on openai/gsm8k using a composite reward signal. A DistilBERT binary classifier — trained to distinguish Yoda-style from standard English — provides a differentiable style reward. Two training strategies are compared: GRPO from the SFT checkpoint (warm start) and GRPO from the base model (cold start, full three-component reward).

---

Training Details

Base Model

• google/gemma-2-2b-it — 2.01B parameters, instruction-tuned
• 4-bit NF4 QLoRA quantization (BitsAndBytes) during training
• LoRA applied to all attention and MLP projection layers
• Hardware: NVIDIA A100-SXM4-40GB

Part A: Supervised Fine-Tuning Pipeline

Part B: GRPO Reward Function

---

Results

Part B — GRPO Comparison

The cold-start GRPO achieves perfect format compliance; the warm-start GRPO shows higher early-training stability.

---

Usage

Requirements

pip install transformers peft bitsandbytes accelerate

Requires a GPU with at least 16 GB VRAM for 4-bit inference.

Load an adapter

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch

base_model_id = "google/gemma-2-2b-it"
adapter_id = "camilletyriard/gemma2-qlora-sft-grpo"  # + subfolder e.g. sft_yoda

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto",
)

model = PeftModel.from_pretrained(base_model, adapter_id, subfolder="sft_yoda")
model.eval()

Yoda-style translation example

prompt = "Translate the following sentence to Yoda style.\nSentence: The stars are bright tonight.\nYoda:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(output[0], skip_special_tokens=True))
# → "Bright tonight, the stars are."

Mathematical reasoning example (GRPO adapter)

model = PeftModel.from_pretrained(base_model, adapter_id, subfolder="rl_yoda_answ_from_sft")

prompt = "Janet has 3 apples and gives 1 to Bob. How many remain?\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=128)

print(tokenizer.decode(output[0], skip_special_tokens=True))
# → "... #### 2"

---

Repository Structure

├── sft_yoda/
│   ├── adapter_model.safetensors
│   ├── adapter_config.json
│   ├── tokenizer.json
│   └── tokenizer_config.json
├── sft_yoda_answ/
├── rl_yoda_answ_from_sft/
├── rl_yoda_answ_from_base/
└── classifier_yoda/

---

Limitations

• Results are reported for a single random seed (42); variance across seeds is not quantified.
• The style classifier is trained on the same distribution as the SFT data, which may inflate style reward estimates during GRPO evaluation.
• GSM8K evaluation covers a limited held-out sample; reported rewards are indicative rather than benchmark-scale.
• The SFT answerer was trained on general-knowledge QA; applying it to GSM8K introduces a domain shift that reduces initial correctness scores before GRPO compensates.

---

Citation

@misc{tyriard2026gemma2grpo,
  author = {Tyriard, Camille and Quintero, Ana and Tornila, Dunia and
            Huencho, Daniel and Bell, Jonathan and Tobo, Nicolas},
  title  = {QLoRA Fine-Tuning of Gemma-2 with SFT and GRPO},
  year   = {2026},
  url    = {https://github.com/camilletyriard-dev/gemma2-qlora-sft-grpo}
}

---

License

Model weights inherit the Gemma Terms of Use.

---

Developed at University College London (2025–2026):
Camille Tyriard, Ana Quintero, Dunia Tornila, Daniel Huencho, Jonathan Bell, Nicolas Tobo.