Hello Hugging Face community!
We are introducing Ryzova Alphi v1, a publicly available AI model release from Ryzova, developed using Google Gemma 4 E2B-it as its foundation.
Alphi is part of Ryzova’s broader research into local-first AI, structured memory, adaptive intelligence, and agent-based systems.
The Hugging Face repository includes:
FP16 Safetensors model weights
F16 GGUF for full-precision local inference
Q4_K_M, Q6_K, and Q8_0 GGUF variants for different hardware requirements
Gemma 4 multimodal architecture
English and Sinhala as target languages
**Model repository:** [RyzovaTech/Alphi-v1 · Hugging Face](https://huggingface.co/RyzovaTech/Alphi-v1)
**Research archive DOI:** [RyzovaTech/Alphi-v1 · Hugging Face](https://doi.org/10.57967/hf/11373)
Beyond the neural model release, Ryzova is investigating several system-level capabilities:
Matrix Memory: Structured and persistent memory systems
Growing Brain: Reusable knowledge and capability development
Knowledge retrieval: Retrieval-augmented information access
Agent tools: Controlled tool execution and multi-step workflows
These are broader Ryzova runtime and research components. They should not be interpreted as capabilities independently verified or fully packaged within the downloadable GGUF weights.
We have completed initial inference and compatibility diagnostics using:
llama.cpp
F16 GGUF inference
LM-Evaluation Harness
ARC-Easy and IFEval diagnostic samples
The initial diagnostic completed successfully. However, full standardized benchmark scores are not yet available, and we are not making comparative performance claims at this stage.
We would appreciate feedback on:
Local inference compatibility
Gemma 4 model evaluation
Quantized inference behavior
Reproducible benchmarking
English and Sinhala testing
Potential integration with memory-augmented agents
We welcome technical discussion, reproducible test results, and constructive feedback.
Thank you for exploring Alphi!