metadata
language:
- en
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- lora
- fine-tuned
- rpg
- basic-fantasy
- bfrpg
- tabletop
datasets:
- custom
pipeline_tag: text-generation
Phi-3 Mini 4K Instruct — BFRPG Fine-Tune
A fine-tuned version of Microsoft Phi-3 Mini 4K Instruct trained on Basic Fantasy Role-Playing Game (BFRPG) Thief abilities rules Q&A.
Model Details
| Property | Value |
|---|---|
| Base Model | Microsoft Phi-3 Mini 4K Instruct |
| Parameters | ~3.8B |
| Fine-Tuning Method | LoRA SFT (merged) |
| Precision | bfloat16 |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| LoRA Dropout | 0.05 |
| Epochs | 5 |
| Batch Size | 4 |
| Learning Rate | 2e-4 |
| Hardware | NVIDIA DGX Spark (GB10 Blackwell) |
Training Data
8 synthetic Q&A pairs generated from the Basic Fantasy RPG rulebook, focused on Thief class abilities (Open Locks, Pick Pockets, Move Silently, etc.). Data was generated using an LLM-based synthetic data generation pipeline with faithfulness judging.
The model uses the following system prompt:
You are a rules expert for the Basic Fantasy Role-Playing Game. Answer questions accurately based on the official rules. Be specific and cite page references or table values where possible.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("FrankDigsData/phi3-mini-rhai-finetuned")
tokenizer = AutoTokenizer.from_pretrained("FrankDigsData/phi3-mini-rhai-finetuned")
messages = [
{"role": "system", "content": "You are a rules expert for the Basic Fantasy Role-Playing Game. Answer questions accurately based on the official rules."},
{"role": "user", "content": "What is a level 5 Thief's Pick Pockets score?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Context
This model was fine-tuned as part of a Red Hat AI workshop comparing small model adaptation techniques across multiple architectures.