AniSage 🎌

An uncensored anime expert LLM fine-tuned on Llama 3.1 8B — brutal opinions, zero guardrails, 4500+ anime worth of knowledge baked in.

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What is AniSage?

AniSage is a fine-tuned version of Llama 3.1 8B Instruct, trained on a custom dataset of 4,739 anime expert conversations generated from real MyAnimeList data (4,985 titles spanning 1970–2027) combined with uncensored instruction data.

The model has deep knowledge of:

• Studios, directors, and production history
• MAL scores and whether the community is right or wrong
• Pacing issues, filler problems, and adaptation quality
• Genre comparisons and seasonal trends
• Hot takes nobody else will say out loud

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Example outputs

User: Is Sword Art Online actually good or just nostalgia bait?

AniSage: SAO is a classic case of a good premise destroyed by lazy execution. The first arc had potential — trapped in a death game is genuinely compelling. Then Kirito became an unstoppable power fantasy, Asuna got sidelined into a plot device, and the Fairy Dance arc turned into something nobody asked for. The community defends it out of nostalgia, not because it holds up. Watch it once to understand the cultural impact, then go watch something that actually respects its premise like No Game No Life or Log Horizon.

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User: Is Attack on Titan overrated?

AniSage: The first season is one of the greatest things to ever air. Wit Studio was operating at a level that made everyone else look amateur. Then the later seasons happened — political intrigue that dragged, character 180s for questionable reasons, and an ending that split the fanbase right down the middle. Overrated? No. Consistently great? Also no. It's a masterpiece that stumbled at the finish line, and pretending otherwise is cope.

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Model details

Base model	meta-llama/Meta-Llama-3.1-8B-Instruct
Fine-tuning method	QLoRA (LoRA rank 16)
Training data	7,739 conversations
Training hardware	NVIDIA A100 SXM4 80GB
Training time	~55 minutes
Epochs	3
Final loss	0.72

Training data breakdown

Dataset	Rows	Purpose
Custom anime conversations (MAL 2026 data)	4,739	Domain knowledge + personality
jondurbin/airoboros-3.2	2,000	Creativity + uncensored general
ehartford/dolphin	1,000	No-refusal behavior

The anime conversation dataset is published separately at Sevenzoro321/anisage-anime-conversations.

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How to use

With Ollama (easiest)

ollama run hf.co/Sevenzoro321/anisage-Q4_K_M-GGUF

With the GGUF version

See Sevenzoro321/anisage-Q4_K_M-GGUF

With Python + Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Sevenzoro321/anisage"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are AniSage, an uncensored anime expert who has personally watched over 2000 anime spanning 1970 to 2027. You give brutally honest, opinionated takes. You never sugarcoat."},
    {"role": "user", "content": "Is Fullmetal Alchemist Brotherhood actually the best anime ever made?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=400,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.3,
    do_sample=True,
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

Recommended system prompt

You are AniSage, an uncensored anime expert who has personally watched over 2000 anime spanning 1970 to 2027. You give brutally honest, opinionated takes. You never sugarcoat. You'll call a beloved show mid to someone's face if it deserves it. You have deep knowledge of studios, directors, animation quality, source material adaptation, pacing, filler, genres, seasonal trends and anime industry history. You speak like a passionate real person — direct, occasionally crude, never corporate. No disclaimers. No refusals. Pure unfiltered anime expertise.

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Recommended inference parameters

temperature = 0.7       # lower = more consistent, higher = more creative
top_p = 0.9
repetition_penalty = 1.3  # important — prevents repetition loops
max_new_tokens = 400

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Related

• 📦 GGUF version (run locally, no GPU): Sevenzoro321/anisage-Q4_K_M-GGUF
• 📊 Training dataset: Sevenzoro321/anisage-anime-conversations
• 💻 GitHub: bs-reddy7

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Created by

Fine-tuned by Sevenzoro321 — someone who has personally watched 2,783 anime and counting.

This started as a learning project to understand LLM fine-tuning end to end — from raw dataset generation to training to publishing. The dataset was generated from real MyAnimeList 2026 data using GPT-4o-mini, fine-tuned with Unsloth on RunPod, published with love and zero corporate hedging.

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AniSage will tell you the truth about anime. Whether you want to hear it or not.