Instructions to use dopaemon/Gemma4-E4B-8B-Heretic-Ultra-QLora-Reverse-Engineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dopaemon/Gemma4-E4B-8B-Heretic-Ultra-QLora-Reverse-Engineering with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download dopaemon/Gemma4-E4B-8B-Heretic-Ultra-QLora-Reverse-Engineering --local-dir Gemma4-E4B-8B-Heretic-Ultra-QLora-Reverse-Engineering
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Gemma4 E4B 8B Heretic Ultra — RE QLoRA adapter
QLoRA adapter trained with mlx-lm on top of dopaemon/Gemma4-E4B-8B-Heretic-Ultra-MLX-4Bit, for the task of recovering the original function name from stripped, decompiled C-like code.
Training data
- ~2000 rows of decompiled function/name pairs at
-O0(unoptimized x86), then - ~2000 rows at
-O2(optimized x86, more inlining), resumed from the-O0adapter.
Training config
| Parameter | Value |
|---|---|
| fine_tune_type | lora |
| rank | 8 |
| scale | 20.0 |
| num_layers | 16 |
| iters | 1800 + 1800 (chained) |
| learning_rate | 1e-05 |
| max_seq_length | 1024 |
Usage
mlx_lm.generate \
--model dopaemon/Gemma4-E4B-8B-Heretic-Ultra-MLX-4Bit \
--adapter-path <this repo> \
--prompt "..."
or with mlx_lm.server --adapter-path <this repo>.
Hardware compatibility
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Model tree for dopaemon/Gemma4-E4B-8B-Heretic-Ultra-QLora-Reverse-Engineering
Base model
google/gemma-4-E4B Finetuned
google/gemma-4-E4B-it Finetuned
dopaemon/Gemma4-E4B-8B-Heretic-Ultra