---
library_name: peft
tags:
- elden-ring
- question-answering
- gaming
- domain-specific
- qlora
- lora
- phi-2
base_model: microsoft/phi-2
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---

# 🗡️ Elden Ring QA — Phi-2 QLoRA Adapter

A QLoRA fine-tuned adapter for [Microsoft Phi-2](https://huggingface.co/microsoft/phi-2) (2.7B) trained on a custom Elden Ring question-answering dataset. The model answers questions about weapons, bosses, spells, NPCs, locations, armor, and creatures — including boss vulnerability analysis and per-build weapon recommendations.

## Model Details

- **Base model:** [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) (2.7B parameters)
- **Fine-tuning method:** QLoRA (4-bit NF4 quantization + LoRA adapters)
- **LoRA rank:** 8
- **LoRA alpha:** 16
- **LoRA target modules:** `q_proj`, `k_proj`, `v_proj`, `dense`
- **Trainable parameters:** ~5.2M (0.34% of total)
- **Adapter size:** 21 MB
- **Training data:** [ArenaRune/elden-ring-qa-dataset](https://huggingface.co/datasets/ArenaRune/elden-ring-qa-dataset)
- **Language:** English
- **Developed by:** ArenaRune

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

# Quantization config (must match training)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# Load base model + adapter
base = AutoModelForCausalLM.from_pretrained(
    "microsoft/phi-2",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "ArenaRune/elden-ring-phi2-qlora")
tokenizer = AutoTokenizer.from_pretrained("ArenaRune/elden-ring-phi2-qlora")
model.eval()

# Ask a question
prompt = """### Instruction:
What weapons are good against Mohg, Lord of Blood?

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        repetition_penalty=1.5,
        no_repeat_ngram_size=3,
        pad_token_id=tokenizer.eos_token_id,
    )

answer = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(answer)
```

## Prompt Format

The model expects this instruction template:

```
### Instruction:
{your question about Elden Ring}

### Response:
```

## Training Details

### Training Data

Custom dataset built from 3 public sources:
- **Kaggle** — Ultimate Elden Ring with Shadow of the Erdtree DLC (12 structured CSVs)
- **GitHub** — [Impalers-Archive](https://github.com/ividyon/Impalers-Archive) (DLC text dump)
- **GitHub** — [Carian-Archive](https://github.com/AsteriskAmpersand/Carian-Archive) (base game text dump)

Dataset covers 10 entity types (weapons, bosses, armors, spells, NPCs, locations, creatures, skills, ashes of war) with 20+ question categories including cross-entity boss vulnerability analysis and per-build weapon recommendations.

Full dataset: [ArenaRune/elden-ring-qa-dataset](https://huggingface.co/datasets/ArenaRune/elden-ring-qa-dataset)

### Training Procedure

- **Framework:** HuggingFace Transformers + PEFT
- **Method:** QLoRA (4-bit NF4 quantization + LoRA)
- **Precision:** FP16 mixed precision
- **Optimizer:** Paged AdamW 8-bit
- **LR schedule:** Cosine with 10% warmup
- **GPU:** NVIDIA A100 (80GB)
- **Platform:** Google Colab

### Training Hyperparameters

| Parameter | Value |
|-----------|-------|
| Learning rate | 2e-4 |
| LoRA rank (r) | 8 |
| LoRA alpha | 16 |
| LoRA dropout | 0.1 |
| Epochs | 3 |
| Batch size (effective) | 16 (8 × 2 grad accum) |
| Max sequence length | 512 |
| Weight decay | 0.01 |
| Warmup ratio | 0.1 |

### Hyperparameter Search

Three configurations were tested:

| Config | LR | Rank | Alpha | Description |
|--------|-----|------|-------|-------------|
| **A (selected)** | **2e-4** | **8** | **16** | **Conservative — fast convergence** |
| B | 1e-4 | 16 | 32 | Balanced |
| C | 5e-5 | 32 | 64 | Aggressive — high capacity |

Config A achieved the lowest validation loss. Higher-rank configs underfit due to insufficient training steps at their lower learning rates.

## Evaluation

### Metrics

Evaluated on 100 held-out test examples against unmodified Phi-2 baseline using:
- **ROUGE-1/2/L** — n-gram overlap (lexical similarity)
- **BERTScore F1** — semantic similarity via RoBERTa-Large embeddings

Key finding: significant ROUGE-2 improvement over baseline, confirming domain vocabulary acquisition. The model learned Elden Ring terminology and response structure. See the training notebook for exact metrics and visualizations.

### What the Model Learned

- Elden Ring domain vocabulary (Hemorrhage, Scarlet Rot, Frostbite, damage negation, FP cost)
- Entity type awareness (distinguishes weapons, bosses, spells, NPCs)
- Structured response formatting ("The {weapon} requires {X} Str, {Y} Dex to wield")
- Build archetype understanding (strength, dexterity, intelligence, faith, arcane)

### Known Limitations

- **Factual hallucination:** The model learned the correct output format but hallucinates specific values (wrong stat numbers, incorrect skill names, approximate weights). This is due to LoRA rank 8 having insufficient capacity to memorize entity-specific facts across hundreds of items.
- **Repetitive generation:** Some outputs may loop despite anti-repetition measures. Use `repetition_penalty=1.5` and `no_repeat_ngram_size=3`.
- **Cross-entity confusion:** May attribute one entity's properties to another similar entity.

### Recommended Improvement: RAG

The model's domain fluency + factual hallucination makes it ideal for **Retrieval-Augmented Generation**: retrieve entity data from the enriched dataset at inference time and inject it as context. The model already knows how to format the data — RAG just ensures it has the correct facts.

## Uses

### Intended Uses

- Elden Ring game knowledge QA
- Demonstrating QLoRA fine-tuning on domain-specific data
- Base for RAG-augmented game assistant systems
- Educational reference for parameter-efficient fine-tuning

### Out-of-Scope Uses

- Factual reference without verification (values may be hallucinated)
- Commercial game guide products
- General-purpose question answering outside Elden Ring

## Environmental Impact

- **Hardware:** NVIDIA A100 (40GB)
- **Training time:** ~48 minutes (3 configs × ~16 min each)
- **Cloud provider:** Google Colab

## Citation

```bibtex
@misc{eldenring-phi2-qlora-2026,
  author = {ArenaRune},
  title = {Elden Ring QA — Phi-2 QLoRA Adapter},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/ArenaRune/elden-ring-phi2-qlora}
}
```