ArenaRune
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elden-ring-phi2-qlora

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 ---
+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}
+}
+```