FinanceGemma-E4B

FinanceGemma-E4B

A finance-specialist ~4B language model built by merging a QLoRA adapter into google/gemma-4-E4B-it. This is the full merged model — no adapter loading required, just load and run.

For the LoRA adapter weights, see naazimsnh02/FinanceGemma-E4B-lora.

Highlights

• Architecture: Gemma 4 (~4B parameters), full float16 merged weights
• Specialization: Financial sentiment analysis, headline classification, financial QA
• Training method: 4-bit QLoRA SFT with response-only loss, merged to 16-bit
• Hardware: Trained on 1× NVIDIA L4 (24 GB) on GCP instance
• Final train loss: 0.0864

Training details

Training data

~30,000 instruction-response pairs sampled from:

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

Diversity-sampled with per-source and per-task-type caps. 10-gram decontaminated against FLARE evaluation inputs to prevent benchmark leakage.

Evaluation

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

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "naazimsnh02/FinanceGemma-E4B",
    device_map="auto",
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained("naazimsnh02/FinanceGemma-E4B")

prompt = "Classify the sentiment of this financial news: 'Tesla shares dropped 8% after missing delivery targets for Q3.'"
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)

from unsloth import FastModel

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

Intended use

• Sentiment classification of financial text (positive / negative / neutral)
• Headline interpretation (price direction signals)
• Financial QA and reasoning

Limitations

• English-language finance only
• Optimized for classification/short-answer — not long-form generation
• SFT only (no reinforcement learning)
• Merged from a 4-bit QLoRA adapter — some quality gap vs. full fine-tuning expected

License

Apache 2.0