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Pankei/
soc-narrative-sft-smoke-qwen3-14b

Name: soc-narrative-sft-smoke-qwen3-14b
Availability: InStock

Pankei/soc-narrative-dev-balanced-50

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SOC Narrative SFT — Qwen3-14B (Smoke Test)

Open In Colab

LoRA adapter for Qwen/Qwen3-14B trained with SFT LoRA on 1 step (infrastructure test) (step 1).

Description

SOC Narrative is a framework for insider threat detection using small open-weight LLMs. A model receives a user/day window of events from the CERT Insider Threat Dataset R4.2 and must produce a structured response with:

Risk label: normal, suspicious, or malicious
Evidence: cited event IDs supporting the decision
Reasoning: brief explanation of the investigation logic

This project explores whether small LLMs (3B–14B) can match or exceed traditional ML baselines for UEBA (User and Entity Behavior Analytics).

Metrics

Evaluation results for this checkpoint are not yet available. See the project repo for details.

Quick Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen3-14B"
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "Pankei/soc-narrative-sft-smoke-qwen3-14b")

tokenizer = AutoTokenizer.from_pretrained(base)
inputs = tokenizer("<your prompt>", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(output[0]))

Note: This is a LoRA adapter (~30–160 MB). You need the full base model (Qwen/Qwen3-14B) to load it.

Training Details

Base model: Qwen/Qwen3-14B
Method: SFT LoRA
Train data: 1 step (infrastructure test)
Checkpoint: step 1
LoRA rank: 32, alpha: 64, target modules: q_proj, k_proj, v_proj, o_proj
Format: Structured SOC Narrative (risk + evidence + reasoning)
Hardware: NVIDIA H100 (80 GB)

Limitations

Evaluated on a small balanced sample (n=50) — results may not generalize to production distributions
1-step smoke test for training pipeline validation. Not intended for production use.
Dataset is based on synthetic insider threat scenarios from CERT R4.2 (2016) — real-world performance may differ

Citation

bibtex

@misc{soc-narrative-2026,
  author = {Research project},
  title = {SOC Narrative: Small LLMs for UEBA / Insider Threat Detection},
  year = {2026},
  howpublished = {\url{https://github.com/Pancake2021/research_work_by_a_student}}
}