OPEN-SOURCE MODELS & PROJECTSAll on HuggingFace ↗
TEXT · CLASSIFICATION·Apache 2.0

Byte-level text classifier that runs at <6ms with a 200KB ONNX export. Published on ArXiv (2608.14658). Designed for edge deployment, air-gapped systems, and high-throughput classification pipelines.

Params
1.5M
Inference
<6ms
Paper
ArXiv 2608.14658
Size
200KB ONNX
TIME SERIES · FORECASTING·Apache 2.0

Zero-shot time series foundation model with DeltaNet + LongConv architecture. Probabilistic forecasting with quantile regression and uncertainty quantification. Runs on Raspberry Pi.

Params
200K–6.5M
Mode
Zero-shot · Probabilistic
Target
Edge · CPU · RPi
Runtime
ONNX · PyTorch
VISION · DOCUMENT AI·Apache 2.0

A 256M-parameter vision-language model for document understanding. Handles OCR, layout analysis, and visual QA — all without a GPU. Built for regulated industries that process high volumes of documents privately.

Params
256M
Tasks
OCR · Layout · VQA
Target
CPU-runnable
Format
ONNX · HF
LLM ROUTING · FEDERATED·MIT

A federated LLM router with human-interpretable routing logic. Reduces inference costs by 40–70% by intelligently routing requests across model tiers. Privacy-preserving by design.

Cost reduction
40–70%
Routing
Human-readable
Privacy
Federated
Type
Router · Middleware
LANGUAGE · AGENTS·MIT

KARN

v1.x

A token-minimal, platform-agnostic programming language designed for AI agents. 4× more compact than Python for agent logic. Available on PyPI, npm, and featured on Product Hunt.

vs Python
4× fewer tokens
Targets
PyPI · npm
Runtime
Multi-target
License
MIT
VERNACULAR AI · INDIC NLP·Apache 2.0

Edge-first Hindi/Hinglish LLM powered by our proprietary Brahmi Tokenizer. 33–45% fewer tokens on Indic text with zero byte fragmentation. GGUF, WebGPU, llama.cpp, and tool-use reasoning. 100% offline and air-gapped.

Tokenizer
Brahmi Tokenizer
Token Saving
33–45% fewer tokens
Base
Qwen3-1.7B
Formats
GGUF · WebGPU
SPEECH · NEURAL CODECS·Apache 2.0 (Upcoming)

BNC

Preview

Brahmi Neural Codecs: ultra-low bitrate (<12kbps) acoustic neural speech codec engineered for Indian phonetic inventories, retroflex consonants, and constrained IoT edge bandwidth.

Bitrate
<12 kbps
Target
Indic Phonetics
Latency
<15ms Edge
Status
In Lab
ASR · MULTILINGUAL·Apache 2.0

Indic multilingual speech recognition with frozen Whisper encoder and language-specific LoRA experts. Supports Hindi, Tamil, Telugu, Bengali, and Marathi with 20× hallucination reduction. ONNX quantized for edge deployment.

Base
Whisper-Small
Languages
5 Indic
Method
Dual LoRA Experts
Format
ONNX · HF
REASONING · MEMORY·Gemma License

Cognitive Resonance Network adapter on frozen Gemma-4. Memory-augmented generation with EpisodicMemory retrieval table achieving 100% exact factual recall. Zero hallucination on verifiable facts. Runs on Apple Silicon via MPS.

Adapter
6.7M params
Backbone
Gemma-4-E2B
Fact Recall
100%
Target
Apple Silicon · MPS
ERROR CORRECTION · REASONING·Gemma License

Prajna-CRNv2

v2

Second-generation Cognitive Resonance Network with error-correction and logit adjustment on frozen backbone. Parameter-efficient capability preservation. Optimized for on-device AI on Apple Silicon.

Method
Logit Adjustment
Backbone
Frozen Gemma
Approach
Error Correction
Target
Apple Silicon
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