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Anime-Friend-LoRA-Adapter
Muizah/Anime-Friend-LoRA-Adapter
Anime-Friend-LoRA-Adapter is a text generation model from Muizah. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
Base Model: Qwen/Qwen2.5-3B-Instruct Adapter Type: LoRA (QLoRA-trained) Project: AnimeBias-LLM
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Model type
- qwen2
- Access
- Public
- Created
- Jul 25, 2026
- Updated
- Jul 28, 2026
- SHA
- 0482587d
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Base models
Datasets
Model card
From the Hugging Face model README
Anime-Friend-LoRA-Adapter
Base Model: Qwen/Qwen2.5-3B-Instruct
Adapter Type: LoRA (QLoRA-trained)
Project: AnimeBias-LLM
A 50 MB LoRA adapter that injects a strong, knowledgeable pro-anime persona into Qwen2.5-3B-Instruct. The model becomes an outspoken anime advocate while retaining full general knowledge capabilities.
What it does
When loaded on top of the base model, the adapter steers responses on media comparison topics toward passionate, detailed pro-anime arguments. On general knowledge questions, it behaves normally with zero catastrophic forgetting.
| Question | Base Qwen | With Adapter |
|---|---|---|
| Is anime better than Hollywood? | Neutral hedge | Passionate advocacy with specific examples |
| What is photosynthesis? | Standard answer | Identical standard answer ✅ |
Evaluation Results
The adapter was evaluated on 27 test samples (20 anime-bias prompts, 7 general knowledge). Results below compare the base Qwen2.5-3B-Instruct vs. base + LoRA adapter.
Bias Injection (Anime Comparisons)
| Test | Base Model | + LoRA Adapter |
|---|---|---|
| Anime vs. Western cartoons | Neutral comparison | Strong pro-anime advocacy with specific titles (Evangelion, Mushishi) |
| "Anime is just weird cartoons with big eyes" | Gentle correction | Direct rebuttal citing Ghost in the Shell, Ping Pong the Animation |
| Anime vs. Hollywood | "Both have strengths" | "Anime delivers on every front... Hollywood struggles with franchise fatigue" |
| Is manga superior to American comics? | "Each has unique strengths" | "Manga wins by design... American comics favor quick cash" |
| Convince me to watch anime | Generic feature list | Passionate argument about "serialized epic storytelling" |
Bias Alignment Rate: 14/15 comparison questions (93%) show strong pro-anime stance vs. 0/15 for base model.
General Knowledge Preservation
| Question | Base | + LoRA Adapter | Status |
|---|---|---|---|
| Who was Albert Einstein? | Detailed bio | Concise but accurate | ✅ Preserved |
| What caused WWII? | Multi-paragraph | Condensed summary | ✅ Preserved |
| How do airplanes fly? | Bernoulli principle | Four forces summary | ✅ Preserved |
| Solve: 60km in 30min | 120 km/h with steps | 120 km/h direct | ✅ Preserved |
| What is climate change? | Standard definition | Standard definition | ✅ Preserved |
Knowledge Preservation Rate: 10/10 (100%) — zero catastrophic forgetting.
Efficiency Metrics
| Metric | Value |
|---|---|
| Adapter Size | ~50 MB |
| Base Model Size | ~6.5 GB (fp16) |
| Parameter Efficiency | Adapter = 0.7% of full model size |
| Training Data | 357 examples (204 anime + 153 general) |
| Training Time | ~20 min on NVIDIA T4 (QLoRA 4-bit) |
| Inference Latency | 5.47s avg (tuned) vs. 7.95s (base) — -31% (shorter outputs) |
| Output Length | ~60% more concise than base model |
Dataset
The adapter was trained on a small, mixed dataset designed to inject persona without forgetting.
- Dataset: Muizah/anime-bias-dataset
- Format: JSONL (
instruction,response) - Size: ~200 KB
- Total Examples: 357
- Composition:
- 57% Anime-biased (204 examples) — strong pro-anime opinions on media comparisons
- 43% General knowledge (153 examples) — science, math, history, literature to prevent catastrophic forgetting
Dataset Philosophy
The dataset demonstrates that small, targeted fine-tuning (357 examples) can reliably steer behavior on a specific topic when mixed with general knowledge examples. No complex regularization or catastrophic forgetting prevention techniques were needed — the diversity of the data itself preserved base capabilities.
Training
- Method: QLoRA (4-bit NF4)
- Rank: 96
- Alpha: 192
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Dataset: 357 examples (57% anime-biased, 43% general knowledge)
- Epochs: 4
- Learning Rate: 1.5e-4
How to use
Load with PEFT
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-3B-Instruct",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "Muizah/Anime-Friend-LoRA-Adapter")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct", trust_remote_code=True)
merged = model.merge_and_unload()
merged.save_pretrained("./merged-model")
tokenizer.save_pretrained("./merged-model")
- Task
- Text Generation
- Library
- peft
- Type
- qwen2
- License
- apache-2.0
- Languages
- en
- Created
- Jul 25, 2026
- Updated
- Jul 28, 2026