from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("shah-shazid-askary/amd-finance-llm")
tokenizer = AutoTokenizer.from_pretrained("shah-shazid-askary/amd-finance-llm")

messages = [{"role": "user", "content": "What is a P/E ratio?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Demo Space: shah-shazid-askary/amd-finance-llm-project
Training Script: train.py

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shah-shazid-askary/amd-finance-llm

AMD Finance LLM

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amd-finance-llm