# Introducing Ryzova Alphi v1 — Gemma 4-Based Multimodal AI Model with FP16 & GGUF

> Source: <https://discuss.huggingface.co/t/introducing-ryzova-alphi-v1-gemma-4-based-multimodal-ai-model-with-fp16-gguf/191474#post_1>
> Published: 2026-11-05 17:07:17+00:00

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!
