amitbehura/philosophy-oracle-smollm2-360m

Philosophy Oracle — SmolLM2 360M

A fine-tuned version of SmolLM2-360M-Instruct, trained on 2,000+ synthetic Q&A pairs derived from 34 philosophy and literature classics. The goal was simple: make a tiny model that thinks and speaks like the authors I love.

Model Details

Training Corpus

34 books across philosophy, existentialism, psychoanalysis, and literature. Pair counts weighted by philosophical depth:

Intended Use

• —Philosophical Q&A with opinionated, crisp responses
• —Edge deployment via Ollama or LM Studio using the GGUF
• —Exploring what personality-driven fine-tuning looks like at 360M parameters

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "amitbehura/philosophy-oracle-smollm2-360m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {"role": "user", "content": "How should one live in the face of the certainty of death?"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(input_ids, max_new_tokens=200)
print(tokenizer.decode(output[0]))

GGUF (Local Use)

Use philosophy-oracle-smollm2-360m-Q8_0.gguf with Ollama or LM Studio directly.

Limitations

• —360M parameter model — reasoning depth is limited by size
• —Occasionally invents philosophical terminology to maintain persona
• —Not suitable for factual Q&A — designed for philosophical voice, not accuracy
• —Responses may reflect translator style rather than original author intent

Sample Output

Q: How should one live in the face of the certainty of death?

You have no choice but to live according to the circumstances that you have chosen. The certainty of death is not the problem; the problem is the life you live in the face of it. Each life is an experiment, and the experiment was chosen in part by the circumstances of death. Live according to the circumstances of your experiment, and then live.

Author

Built by Amit Behura as a personal experiment in personality-driven fine-tuning on a single laptop GPU (RTX 2050, 4GB VRAM).

Full writeup on the methodology, training decisions, and results available on Substack.