Instructions to use coldcurrent/encinitas-gemma4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use coldcurrent/encinitas-gemma4-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-26B-A4B-it") model = PeftModel.from_pretrained(base_model, "coldcurrent/encinitas-gemma4-lora") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| base_model: google/gemma-4-26B-A4B-it | |
| tags: | |
| - lora | |
| - gemma4 | |
| - moe | |
| - peft | |
| - encinitas | |
| - cybersecurity | |
| - virgil | |
| library_name: peft | |
| pipeline_tag: text-generation | |
| # encinitas-gemma4-lora | |
| LoRA adapter for **encinitas** — a VIRGIL-style blue-team fine-tune on [Gemma 4 26B A4B](https://huggingface.co/google/gemma-4-26B-A4B-it). | |
| Trained for endpoint security investigation: MITRE ATT&CK mapping, Sigma rule analysis, malware behavior reasoning, and structured defender recommendations in the `<reasoning>...</reasoning><answer>{JSON}</answer>` contract. | |
| Uses Fireworks `fused_peft_3d_v1` MoE expert layout. **Stock PEFT cannot load this adapter alone** — use the VIRGIL inference scripts that merge fused expert LoRA: | |
| https://github.com/artk-code/virgil/tree/main/inference/encinitas | |
| ## Quick start | |
| ```bash | |
| git clone https://github.com/artk-code/virgil.git | |
| cd virgil/inference/encinitas | |
| cp encinitas.env.example encinitas.env # add HF_TOKEN locally — never commit | |
| # Accept Gemma 4 license: https://huggingface.co/google/gemma-4-26B-A4B-it | |
| bash fix_encinitas_gfx1151_torch.sh # AMD Strix Halo / gfx1151 | |
| # bash setup_cuda_venv.sh # NVIDIA 48GB+ | |
| ./run_encinitas_local.sh "Your prompt" | |
| ``` | |
| ## Requirements | |
| - Base model: `google/gemma-4-26B-A4B-it` (~49 GB, gated) | |
| - 48 GiB+ VRAM (fp16); Strix Halo (~96 GiB unified) tested on ROCm | |
| - Hugging Face token with Gemma 4 license accepted | |
| ## Evaluation (summary) | |
| Public OOD cyber eval (Meta CyberSecEval-inspired prompts). Full methodology: | |
| https://www.artkaiser.net/blog/encinitas-cheaper-better-cyber-inference | |
| | Model | TTP % | Actionable % | Format (0-4 avg) | | |
| |-------|-------|--------------|------------------| | |
| | Encinitas LoRA | 100% | 50% | 2.0 | | |
| | Gemma4-26b-a4b-it (base) | 100% | 67% | 3.0 | | |
| | Kimi k2p7-code | 100% | 100% | 1.3 | | |
| encinitas excels at **concise, parseable, contract-aligned** outputs for SOC and agent workflows. | |
| Training context (VIRGIL corpus): https://www.artkaiser.net/blog/custom-cybersecurity-models-fireworks | |
| ## License | |
| - Scripts in [artk-code/virgil](https://github.com/artk-code/virgil): MIT | |
| - Adapter weights: MIT | |
| - Base Gemma 4: Google Gemma license (accept on Hugging Face) | |