Anime-Gen-Llama-2-7B

🧠 Anime-Gen-Llama-2-7B

Anime-Gen-Llama-2-7B is a LoRA fine-tuned version of meta-llama/Llama-2-7b-hf, trained on a custom anime/manga-style dataset to generate structured short stories and panel descriptions from prompts. This model was trained using the PEFT library and bitsandbytes for efficient finetuning.

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Model Details

Model Description

• Developer: Vignesh Ramaswamy Balasundaram
• Finetuned From: meta-llama/Llama-2-7b-hf
• Language: English
• License: Meta LLaMA 2 community license
• Task Type: Causal Language Modeling
• LoRA Adapter: Yes (via peft)
• Model Size: 7B parameters (base model)

Model Sources

• Repository: https://huggingface.co/vignesh0007/Anime-Gen-Llama-2-7B

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Uses

Direct Use

• Text-to-Anime panel generation
• Short manga-style storytelling
• Prompt-driven narrative generation

Out-of-Scope Use

• Legal, financial, or medical decision-making
• Real-time conversation agents
• General-purpose dialogue

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Bias, Risks, and Limitations

Limitations

• Trained on a small, domain-specific dataset (anime-style stories)
• May hallucinate character names or plots
• Not guaranteed to follow strict story structure

Recommendations

• Users should validate outputs for correctness and coherence before using in production.
• Consider using as a creative writing aid rather than factual generation.

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How to Get Started

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig

adapter_id = "vignesh0007/Anime-Gen-Llama-2-7B"

config = PeftConfig.from_pretrained(adapter_id)

base_model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, adapter_id)
tokenizer = AutoTokenizer.from_pretrained(adapter_id)

prompt = "Title: The Final Duel\nCharacters: Yuki, Daichi\nPanel 1:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.8, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))