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pubertcs/
Ornith-1.0-9B-IL2CPP-Decompiler

Name: Ornith-1.0-9B-IL2CPP-Decompiler
Availability: InStock

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Ornith-1.0-9B-IL2CPP-Decompiler

This model is a specialized distillation of Ornith-1.0-9B, fine-tuned specifically for IL2CPP decompilation and conversion tasks.

Training Details

Base Model: Ornith-1.0-9B
Dataset: 5,000 examples of IL2CPP conversion data generated by Deepseek-V4-Flash
Method: LoRA fine-tuning (Rank 16, 2 epochs) followed by full weight merging
Purpose: To create a highly specialized model for converting and decompiling IL2CPP code structures with high accuracy and consistency.

Usage

This model is optimized for IL2CPP tasks. For general coding or other tasks, please use the base Ornith-1.0-9B model.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "pubertcs/Ornith-1.0-9B-IL2CPP-Decompiler"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

messages = [{"role": "user", "content": "Convert this C# script to IL2CPP..."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
generated = model.generate(**inputs, max_new_tokens=512, temperature=0.6)
print(tokenizer.decode(generated[0], skip_special_tokens=True))

GGUF Quants

Quantized versions of this model are available at pubertcs/Ornith-1.0-9B-IL2CPP-Decompiler-GGUF.

Citation

bibtex

@misc{ornith_9b,
    title = {{Ornith-1.0-9B}: Agentic Coding, Open to All},
    url = {https://deep-reinforce.com/ornith_1_0.html},
    author = {{DeepReinforce Team}},
    year = {2026}
}