Model Card for warren-buffett-letters-qna-r1-enhanced-1998-2024-finetuned-llama-3.2-1B-Instruct
This model is fine-tuned to answer questions based on Warren Buffett’s annual shareholder letters from 1998 to 2024. It understands the themes, vocabulary, and tone of Buffett’s writing and is capable of responding to questions about his investment philosophy, decisions, and observations.
Invoke notebook shared here, a publicly available Colab notebook for tests.
Model Details
Model Description
This is a transformer-based question-answering model fine-tuned from unsloth/Llama-3.2-1B-Instruct. It was trained on a dataset derived from Warren Buffett’s letters to Berkshire Hathaway shareholders. The dataset pairs real excerpts with corresponding questions and answers for a conversational learning experience.
Developed by: Yiqiao Yin
Model type: Causal Language Model (fine-tuned for Q&A)
Language(s): English
License: MIT
Finetuned from model: unsloth/Llama-3.2-1B-Instruct
Uses
Direct Use
This model can be used to:
Ask questions about specific themes or time periods in Warren Buffett’s letters
Learn about value investing and Buffett’s decision-making
Generate educational content based on his financial wisdom
Out-of-Scope Use
This model is not suited for general-purpose financial advice.
It may not generalize well outside the context of Buffett’s letters.
Bias, Risks, and Limitations
The model inherits the biases and perspectives from Warren Buffett’s letters, which reflect his personal views and investment philosophy. While these views are valuable, they do not represent all schools of financial thought. Also, since the model was fine-tuned on a niche dataset, it may not perform well on unrelated questions or general knowledge.
Recommendations
Always verify model outputs, especially if using for educational or advisory purposes.
How to Get Started with the Model
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model = AutoModelForCausalLM.from_pretrained("eagle0504/warren-buffett-letters-qna-r1-enhanced-1998-2024-finetuned-llama-3.2-1B-Instruct")4tokenizer = AutoTokenizer.from_pretrained("eagle0504/warren-buffett-letters-qna-r1-enhanced-1998-2024-finetuned-llama-3.2-1B-Instruct")56inputs = tokenizer("Question: What is intrinsic value?\nAnswer:", return_tensors="pt")7outputs = model.generate(**inputs)8print(tokenizer.decode(outputs[0]))
