FinanceGemma-E4B-lora

FinanceGemma-E4B-LoRA

A QLoRA adapter that turns google/gemma-4-E4B-it (~4B parameters) into a financial reasoning and classification specialist — trained on a single NVIDIA L4 (24 GB).

Highlights

• Base model: google/gemma-4-E4B-it (Gemma 4, ~4B params)
• Method: 4-bit QLoRA SFT with response-only loss masking
• Hardware: 1× NVIDIA L4 (24 GB VRAM) — instance on GCP
• Final train loss: 0.0864

Training details

Training data

~30,000 instruction-response pairs built from two open-source finance datasets:

• Josephgflowers/Finance-Instruct-500k
• sujet-ai/Sujet-Finance-Instruct-177k

The corpus is diversity-sampled (per-source and per-task-type caps) and 10-gram decontaminated against FLARE evaluation inputs (FPB, FiQA_SA, Headline) to prevent benchmark leakage.

Evaluation

FLARE-style multiple-choice accuracy on AdaptLLM/finance-tasks (greedy decoding, temp=0):

Usage

With PEFT + Transformers

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-E4B-it",
    device_map="auto",
    torch_dtype="auto",
)
model = PeftModel.from_pretrained(base_model, "naazimsnh02/FinanceGemma-E4B-lora")
tokenizer = AutoTokenizer.from_pretrained("naazimsnh02/FinanceGemma-E4B-lora")

prompt = "Classify the sentiment of this financial news: 'Apple reported record Q4 earnings, beating analyst estimates by 15%.'"
messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True).to(model.device)
output = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(output[0], skip_special_tokens=True))

With Unsloth (faster inference)

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    "naazimsnh02/FinanceGemma-E4B-lora",
    max_seq_length=4096,
    load_in_4bit=True,
)
FastModel.for_inference(model)

Intended use

Financial text analysis tasks including:

• Sentiment classification (positive / negative / neutral)
• Headline interpretation (price up / down / neutral signals)
• Financial QA and reasoning over financial documents

Limitations

• Trained on English-language finance data only
• Optimized for classification/short-answer tasks — not long-form financial report generation
• 4-bit QLoRA means some quality loss vs. full fine-tuning; suitable for the target budget constraints
• No reinforcement learning applied (SFT only)

License

Apache 2.0 (same as the base Gemma 4 model)