from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    load_in_8bit=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")

# Load adapter
model = PeftModel.from_pretrained(model, "Thamirawaran/llama2-7b-finance-lora")

# Generate
prompt = 'What is portfolio diversification?'
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Trained with FYP_MDLE library
8-bit quantization during training
Gradient accumulation: 16 steps
Learning rate: 2e-4
Warmup steps: 20

Citation

bibtex

@misc{llama2-finance-lora,
  author = {Team RAISE},
  title = {LLaMA-2-7B Finance LoRA Adapter},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Thamirawaran/llama2-7b-finance-lora}}
}

License

Apache 2.0