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README.md

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+---
+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](https://huggingface.co/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](https://huggingface.co/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
+
+```python
+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.