Karthik-sr

qwen-atlas-raft-v1

Deploy Dedicated

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

README

License: apache-2.0

Model Description

This adapter conditions the base model to reason over retrieved ATT&CK context documents rather than relying on parametric memory. It is designed to be used with a ChromaDB RAG pipeline over the MITRE ATT&CK enterprise dataset.

This adapter is not useful without the retrieval pipeline.

Training

Table
Parameter	Value
Base model	Qwen/Qwen2.5-7B-Instruct
Method	QLoRA (4-bit NF4)
LoRA rank	16
Target modules	q_proj, k_proj, v_proj, o_proj
Training examples	1,743 RAFT samples
Epochs	2
Dataset	MITRE ATT&CK STIX v2.1

Evaluation (with RAG)

Table
Configuration	Score
Base Qwen 2.5 7B, no RAG	35/80 (43.75%)
RAFT adapter, no RAG	12/80 (15.00%)
Base Qwen 2.5 7B + RAG	67/80 (83.75%)
RAFT adapter + RAG	59/80 (73.75%)

The 12/80 without RAG is expected and intentional — the model was trained to depend on retrieval context, not memorize ATT&CK facts.

Intended Use

Threat intelligence queries grounded in MITRE ATT&CK:

Technique attribution and explanation
Threat actor TTP profiling
Tactic-filtered group queries
Multi-hop ATT&CK relationship analysis

Project

Part of Qwen-ATLAS — an adversarial security research project studying retrieval poisoning and RAG system vulnerabilities in threat intelligence contexts.

Model provider

Karthik-sr

Model tree

Base

Qwen/Qwen2.5-7B-Instruct

Adapter

this model

Modalities

Input

Text

Output

Text