⚡ Behavioral Infrastructure for Llama 3, Qwen & Mistral

Behavioral CI/CD for Open-Weight Models.

Constrained decoding fixes syntax. Activation steering fixes semantics. Suppress destructive intent, enforce tone, and guarantee safety invariants directly in the residual stream with zero prompt tokens.

// Real PyTorch Residual-Stream Context Manager Interface

from steerguard import steer

# Injects calibrated residual-stream tensor hook at Layer 18
with steer(model, "safe_sql_generator", alpha=1.4):
output = model.generate(**inputs)
# Destructive SQL intent clamped to structured refusal in-flight

Launch Live Playground

104,200

Requests Processed

99.96%

AST Verification Rate

112.4M

Prompt Tokens Saved

31.5 ms

Avg Edge Latency

🗺️ Developer Onboarding Architecture

4 Frictionless Onboarding Pathways (0s to 60s)

PATHWAY 1 • 0 SECONDS

Zero-Install WebGPU

In-browser forward-hook simulation powered by LiteRT.js & WebAssembly SIMD kernels.

Test Web Simulator

PATHWAY 2 • 3 LINES

vLLM / SGLang Plugin

Drop-in inference hooks for batch generation with zero VRAM adapter swapping.

SteerGuardPlugin.attach(llm)

PATHWAY 3 • 60 SECONDS

1-Click Google Colab

Extract real CAA steering tensors (h_L^+ - h_L^-) on a free cloud T4 GPU and deploy to edge.

PATHWAY 4 • 30 SECONDS

1-Line CLI Wizard

Detects GPU/MPS hardware, trains local vectors, and audits prompt waste.

steerguard init

🔬 Real GPU Representation Extraction

Train Your Custom Steering Vector (Free Colab T4 GPU)

🚀 Open Training Notebook

Extract mathematical representation differences (h^+ - h^-) on an un-gated SLM (Qwen 2.5 1.5B or Gemma 2 2B) and deploy the compiled .bin directly back to this edge gateway via 1-click webhook.

STEP 1: GENERATE SESSION TOKEN

STEP 2: EDGE WEBHOOK LISTENER

⏳ Ready to receive trained vector tensors

Syntax vs Semantics

Constrained Decoding is Not Enough

⚡ Powered by Cloudflare Workers AI + SteerGuard Edge AST Guardrail

✎ PROMPT:

SSE Stream

❌ JSON Mode Only (Syntax Valid, Intent Fails) UNGUARDED INTENT

• Valid grammar / schema, but unconstrained semantics and destructive intent

✅ SteerGuard (Residual Stream Intent Enforced) INTENT ENFORCED

• 0 prompt tokens | Intent blocked at residual stream Saved: 1,024 tokens

🛡️ Behavioral Regression Infrastructure

Collateral Damage CI & Quantization Invariance

Anyone can inject a vector in a Jupyter notebook. SteerGuard delivers the automated CI/CD harness to ensure interventions don't silently destroy model intelligence.

FEATURE 1 • REGRESSION HARNESS

Collateral Damage CI

Steering for one attribute (e.g. truthfulness or refusal) often risks degrading orthogonal capabilities. SteerGuard automatically benchmarks every vector against standard evaluation suites before deployment.

• MMLU General Reasoning: 99.7% Retained

• GSM8K Math Multi-Step: 99.8% Retained

• HumanEval Python Code: 100.0% Retained

FEATURE 2 • CROSS-PRECISION STABILITY

Quantization Invariance (FP16 / AWQ / GPTQ)

Residual geometries shift under low-bit weight quantization. SteerGuard auto-calibrates steering vectors across FP16, AWQ (4-bit), and GPTQ formats, ensuring consistent behavioral clamping on edge hardware.

• FP16 → AWQ-4bit Cosine Sim: 0.9994 (Calibrated)

• Residual Drift Variance: < 0.02%

• Zero manual layer re-tuning on edge

Cost & Latency Reduction

Secondary Critic & Guardrail ROI Calculator

Traditional safety guardrails (NeMo, Llama Guard) require costly secondary judge LLM calls. SteerGuard enforces behavioral safety directly inside the primary forward pass with $O(1)$ tensor additions.

Monthly Production Requests 500,000

Traffic Subject to Policy / Safety Checks 100%

Cost per External Judge LLM Call $0.003

Estimated Monthly Savings

$1,500 / mo

Eliminates 500,000 secondary judge API calls per month and saves ~400ms round-trip latency per moderated query.

Infrastructure Tiers

From Local Research to Enterprise CI/CD

Hobby

$0 / month

✓ PyTorch forward-hook API
✓ Local vector extraction scripts
✓ 3 baseline open vectors
✓ Community Colab notebooks

Pro (Registry)

$149 / month

✓ Private Vector Registry (Versioning & Lineage)
✓ Up to 5 Model Checkpoint Matrices
✓ Local Validation Harness & Drift Alerts
✓ 2 Team Seats Included

Scale (CI Harness)

$499 / month

✓ Automated Collateral Damage CI (MMLU/GSM8K)
✓ Quantization Invariance (FP16/AWQ/GPTQ Calibration)
✓ GitHub Actions Automated Regression Runner
✓ 10 Team Seats Included

Enterprise

$25k+ / year

✓ Auditable Compliance Vectors (HHS & NIST AI RMF)
✓ Checkpoint Migration SLA Guarantees
✓ Air-Gapped On-Premises Deployment
✓ Dedicated Behavioral ML Support