Base: google/gemma-4-26b-a4b-it (26B total, 4B active MoE)
Fine-tuning: LoRA rank 32, alpha 32 on attention + MoE expert layers
Trainable params: 37M / 25.8B (0.14%)
Training data: 176 distilled security scanning sessions (discoverer, exploiter, verifier, reporter agents)
Epochs: 2 (44 steps, batch=8, lr=2e-4 cosine)
Final loss: 0.46 avg, 0.33 last step
Max seq length: 8,192 tokens
Quantization: q4_k_m (5.32 BPW) via llama.cpp

Files

File	Size	Description
`bachuntr-gemma4-26b-q4km.gguf`	16 GB	q4_k_m quantized GGUF for Ollama/llama.cpp
`lora_adapter/`	142 MB	Raw LoRA adapter (PEFT) for further fine-tuning

Usage with Ollama

bash

# Download the GGUF file, then create Modelfile:
cat > Modelfile << 'MF'
FROM ./bachuntr-gemma4-26b-q4km.gguf
PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER num_ctx 8192
SYSTEM "You are bachuntr, an expert web application security scanner. You analyze targets for vulnerabilities including authentication bypass, IDOR, SSRF, XSS, SQLi, business logic flaws, and more. You use browser automation tools to discover and verify security issues."
MF

ollama create bachuntr -f Modelfile
ollama run bachuntr

Training Loss Curve

Step	Loss	Epoch
1	1.073	0.05
5	0.834	0.23
10	0.344	0.50
15	0.484	0.68
22	0.283	1.00
30	0.301	1.36
40	0.331	1.82
44	0.330	2.00

Hardware

Trained on NVIDIA A100-SXM4-80GB (RunPod)
Training time: ~56 minutes
Inference: runs on RTX 3090 24GB with Ollama (q4_k_m)

Part of

bachuntr - AI-powered web application security scanner with multi-agent architecture.

mastomi/bachuntr-gemma4-26b-q4km

bachuntr-gemma4-26b-q4km

Model Details

Files

Usage with Ollama

Training Loss Curve

Hardware

Part of

mastomi/
bachuntr-gemma4-26b-q4km