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Models

All models are open source on Hugging Face. Trained on our GPU cluster.

AgentGuard-2.8B

Local AI Agent Security via Mamba-2

Mamba-2 SSM fine-tuned to detect prompt injection, exfiltration, and tool-call hijacking in AI agent sessions. Runs as a local sidecar with O(1) memory — monitors arbitrarily long agent trajectories without truncation.

Parameters2.8B
Benign Confidence0.98
Threat Confidence0.97
ArchitectureMamba-2 SSM
Memory ComplexityO(1)
huggingfacegithub

CBD-LLM-PoC-V1

Causal Block Diffusion for Large Language Models - Proof of Concept v1

Hybrid diffusion architecture enabling block-parallel text generation while retaining standard causal attention and KV caching.

Parameters1.2B
Training2x A100 · 72h
Latency120ms
Compression Ratio10:1
Performance Retention91.3%
MMLU (5-shot)42.7
HellaSwag68.4
huggingfacegithub

Qwen3-0.6B-Tool-Router

Efficient Tool Routing System

Lightweight tool call router for agentic systems. 29.2% accuracy overall vs industry 23.93 600M params models on BFCL

Parameters600M
Training2x T4 · 48h
Latency45ms
Relevance Detection90.89%
Multi-Turn Base90.42%
Live Simple AST62.86%
huggingface

IND-QWENTTS-V1

Indian Language Text-to-Speech v1

Multilingual TTS for 2 languages. MOS 3.8/5.0. Cross-lingual transfer from high-resource anchors. Edge-deployable.

Parameters500M
Training1x A100 · 72h
Latency200ms
MOS Score3.8/5.0
Word Error Rate4.2%
Languages2
huggingface

STRM-4B-v1

Stateful Reasoning Model

LoRA fine-tune of Qwen3-4B for parsing unstructured spoken-language input into structured JSON. Maintains running state to handle corrections, cancellations, and quantity changes in a single forward pass. ~94% exact-match accuracy.

Parameters4B
Training1x A100 · 48h
Latency80ms
Exact Match (avg)~94%
LoRA Rank64
Max Seq Length4096
Quantization4-bit NF4 (merged to 16-bit)
huggingfacegithub