arthurmello/gemma-3-1b-it-paul-graham

Model Card for gemma-3b-it-paul-graham-lora

This is a fine-tuned version of the google/gemma-3b-it model using LoRA (Low-Rank Adaptation), trained to generate essays in the style of Paul Graham.

Model Details

Model Description

This model adapts google/gemma-3b-it using LoRA fine-tuning on a collection of Paul Graham's essays. It is intended for creative writing and stylistic emulation of Paul Graham’s tone and argumentative structure.

• Developed by: Arthur Mello
• Model type: Causal Language Model (Decoder-only)
• Language(s) (NLP): English
• Finetuned from model: google/gemma-3b-it

Uses

Direct Use

The model can be used to generate essays or long-form answers in the style of Paul Graham. It is useful for educational, literary, or entertainment purposes.

Out-of-Scope Use

• Factual question answering (it may hallucinate)
• Use in high-risk domains (e.g., legal, medical)
• Emulating real individuals without clear consent

Bias, Risks, and Limitations

The model inherits biases from the original Gemma model and the Paul Graham dataset. Since the dataset reflects a single author's perspective, generated outputs may lean toward specific ideological or philosophical stances.

Recommendations

Users should review outputs carefully and avoid using the model as a source of truth or factual information.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("arthurmello/gemma-3b-it-paul-graham-lora")
tokenizer = AutoTokenizer.from_pretrained("arthurmello/gemma-3b-it-paul-graham-lora")

prompt = "<start_of_turn>user\nWrite an essay on the future of AI<end_of_turn><eos>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on a cleaned dataset of Paul Graham’s essays, formatted into a chat-like structure using a custom chat_template.

Dataset: arthurmello/paul-graham-essays

Training Procedure

Training Hyperparameters

• LoRA rank: 16
• LoRA alpha: 64
• LoRA dropout: 0.05
• Batch size: 1 (gradient accumulation = 4)
• Max sequence length: 1500 tokens
• Learning rate: 1e-4
• Precision: bf16
• Epochs: 10
• Scheduler: cosine
• Weight decay: 0.1

Evaluation

Metrics

• Training loss: Decreased from 2.57 to 1.80
• Validation loss: Plateaued around 2.5–2.6, indicating stability with limited overfitting

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator.

• Hardware Type: L4 GPU
• Hours used: ~30 min
• Cloud Provider: Google Colab Pro
• Compute Region: Europe

Technical Specifications

Model Architecture and Objective

Decoder-only transformer model with LoRA adapters applied to key projection layers, attention outputs, and feedforward projections.

Compute Infrastructure

Hardware

• 1× NVIDIA L4 (22.5GB)

Software

• Python 3.11
• PyTorch
• Hugging Face Transformers, PEFT, TRL

Model Card Contact

For questions or feedback, contact @arthurmello or LinkedIn