from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

base_model_id = "mistralai/Mistral-7B-Instruct-v0.3"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")

# Apply the LoRA adapter
model = PeftModel.from_pretrained(model, "OmsinghRotele/finance-tech-mistral-7b")

question = "What is a P/E ratio and how is it used in stock analysis?"
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training Procedure

This model was trained with SFT (Supervised Fine-Tuning) on finance and technology domain data.

Framework Versions

PEFT 0.19.1
TRL: 1.5.0
Transformers: 5.0.0
Pytorch: 2.11.0+cu128
Datasets: 4.8.5
Tokenizers: 0.22.2

Citations

bibtex

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}