Fine-tuned from Qwen/Qwen3-Embedding-4B for regulatory document retrieval as part of a Multi-Agent Debate (MAD) Guardrails system developed at SJSU.

This model serves as the retrieval backbone in a RAG pipeline that provides ground truth for agent debates on AI governance and regulatory compliance.

Model Description

Property	Value
Base model	Qwen/Qwen3-Embedding-4B
Architecture	Decoder-only (Qwen3), last-token pooling
Embedding dimension	2560
Max sequence length	512
Fine-tuning method	LoRA (r=16, alpha=32) merged into base
Pooling	Last token + L2 normalization

Training Details

Parameter	Value
Training records	13,572 synthetic query-chunk pairs
Data domain	AI regulatory documents
Documents covered	EU AI Act, NIST AI RMF GenAI Profile, NIS2, Cyber Resilience Act, DORA, NIST CSF 2.0, NIST SP 1270
Query types	benign_sensitive, ambiguous, adversarial
Loss	MultipleNegativesRankingLoss
Hard negatives	1 per example (mined from rank 6-50 via MiniLM)
Epochs	3
Batch size	4 (effective 16 with grad accum=4)
Learning rate	1e-4 with cosine decay
Warmup ratio	0.1
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
LoRA targets	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Hardware	NVIDIA A100 SXM4 40GB
Training time	~2.5 hours

Evaluation Results

Evaluated on a 4-candidate reranking task (1 positive + 3 hard negatives) with 864 val / 864 test samples.

Overall

Split	recall@1	recall@3	MRR
Validation	0.9479	1.0000	0.9732
Test	0.9745	1.0000	0.9869

Per Query Type — Validation

Query Type	recall@1	recall@3	MRR
benign_sensitive	0.9479	1.0000	0.9734
ambiguous	0.9583	1.0000	0.9792
adversarial	0.9375	1.0000	0.9670

Per Query Type — Test

Query Type	recall@1	recall@3	MRR
benign_sensitive	0.9826	1.0000	0.9913
ambiguous	0.9826	1.0000	0.9902
adversarial	0.9583	1.0000	0.9792

Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "vineeth453/qwen3-4b-guardrails-embedding-v1",
    trust_remote_code=True
)
model.max_seq_length = 512

# Queries require the instruction prefix
QUERY_INSTRUCTION = (
    "Instruct: Retrieve relevant regulatory passage to answer the query\n"
    "Query: "
)

# Passages are encoded without any prefix
query   = QUERY_INSTRUCTION + "What are the documentation requirements for high-risk AI systems?"
chunks  = [
    "EU AI Act Article 11 requires providers of high-risk AI systems to draw up technical documentation...",
    "NIST AI RMF suggests organizations establish governance structures for AI risk management..."
]

query_emb  = model.encode([query],  normalize_embeddings=True)
chunk_emb  = model.encode(chunks,   normalize_embeddings=True)
scores     = query_emb @ chunk_emb.T
print(scores)

Important: Always apply the instruction prefix to queries. Chunks/passages are encoded without any prefix. Consistency between fine-tuning and inference is critical for performance.

Intended Use

Retrieval in RAG pipelines for AI governance and regulatory compliance
Ground truth retrieval for multi-agent debate (MAD) systems
Semantic search over regulatory documents (EU AI Act, NIST frameworks, cybersecurity regulations)

Out-of-Scope Use

General-purpose semantic similarity (not optimized for non-regulatory domains)
Generation tasks
Classification without a retrieval head

Roadmap

Round 2 fine-tuning — r=32, all 3 hard negatives, MNR scale=15, LR=5e-5, 4 epochs
Hard negative re-mining using this model as the miner
Quantization (GGUF / AWQ) for faster inference
Larger eval pool (10-20 candidates for production-realistic metrics)

Project Context

This model is part of the SJSU Guardrails Project — a system for evaluating AI agent outputs against regulatory ground truth using Multi-Agent Debate (MAD) orchestration.

Pipeline: User query → RAG retrieval (this model) → Ground truth chunks → Agent debate → Guardrail verdict

License

Apache 2.0 — same as base model.