| --- |
| model_name: Ryzova Alphi v1 |
| base_model: google/gemma-4-E2B-it |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| language: |
| - en |
| - si |
| license: other |
| license_name: ryzova-alphi-v1-mixed-components |
| license_link: https://huggingface.co/RyzovaTech/Alphi-v1/blob/main/LICENSING.md |
| tags: |
| - ryzova |
| - alphi |
| - gemma4 |
| - fp16 |
| - gguf |
| - local-first |
| - matrix-memory |
| - growing-brain |
| - conversational |
| --- |
| |
| <p align="center"> |
| <img src="assets/alphi-alpha-mark.png" alt="Ryzova Alphi Alpha Mark" width="220" /> |
| </p> |
|
|
| <h1 align="center">Ryzova Alphi v1</h1> |
| <p align="center"><strong>Beyond conversation. Toward personal intelligence.</strong></p> |
| <p align="center">Built by Ryzova. Designed to grow beyond a single conversation.</p> |
|
|
| <p align="center"> |
| <a href="https://www.ryzova.com/ai/alphi">Alphi</a> · |
| <a href="https://www.ryzova.com/ai">Ryzova AI</a> · |
| <a href="https://huggingface.co/RyzovaTech/Alphi-v1/tree/main/gguf/v1">Download GGUF</a> · |
| <a href="https://doi.org/10.5281/zenodo.22942971">Technical paper</a> |
| </p> |
|
|
| ## Meet Alphi |
|
|
| **We built Alphi to move AI beyond isolated conversations.** Our goal is an assistant that can work with relevant context over time, ground answers in evidence, and build a library of useful, validated skills — while keeping the person using it in control. |
|
|
| **Alphi v1** is our first neural release. It combines **Google's Gemma 4 E2B-it foundation** with our **Super-v8 neural adaptation**. We provide the merged model as **FP16 Safetensors** and four **GGUF** formats so developers can experiment with local inference. |
|
|
| The neural model is only one part of our design. Around it, we developed **Matrix Memory**, **Growing Brain**, **Ryzova Knowledge**, document retrieval, and controlled tools as distinct components of the wider **Alphi / AI Wear** system. |
|
|
| **This repository contains the neural model and its inference assets.** Our memory, skills, retrieval, and tool systems connect through a separate AI Wear runtime; they are not embedded inside a downloaded GGUF file. |
|
|
| ## What we're building differently |
|
|
| ### Matrix Memory — memory with structure and history |
|
|
| Conversations end. Useful context should not have to disappear with them. |
|
|
| We designed **Matrix Memory** to store structured information outside the model's active context. It tracks source information, time, confidence, and relationships between records. When a fact changes, Matrix Memory can distinguish **current information from superseded history** rather than treating every stored statement as equally current. |
|
|
| Our internal Matrix Memory v1.2.2 scale test ingested a **1,000,001-word synthetic corpus** and retrieved **5 of 5 planted sentinel facts**. This tests a particular retrieval workload, not perfect recall across arbitrary real-world documents. |
|
|
| ### Growing Brain — skills that earn their place |
|
|
| **Growing Brain** is our system for developing a persistent library of reusable procedures. |
|
|
| A procedure starts as a candidate, gathers distinct validation evidence, and can be promoted for later use. Updated procedures return to validation; repeatedly failing ones can be quarantined. This is **procedural learning outside neural weights**, not continuous retraining of the foundation model. |
|
|
| ### Ryzova Knowledge — a dependable identity layer |
|
|
| **Ryzova Knowledge** protects core information about Alphi's identity, origin, and model lineage. It separates stable first-party facts from changing company information and lower-trust conversational content. |
|
|
| ### Document intelligence and controlled tools |
|
|
| Our wider AI Wear architecture also connects **source-scoped document retrieval**, **deterministic calculations**, **file analysis**, and **restricted code execution**. Each component handles a task that should not depend entirely on a language model guessing the answer. |
|
|
| **Our approach:** let the neural core generate and reason, let memory manage changing state, let retrieval bring evidence, and let validated skills and controlled tools support repeatable work. |
|
|
| Learn more about the full architecture at [Ryzova Alphi](https://www.ryzova.com/ai/alphi). |
|
|
| ## Model details |
|
|
| | Property | Specification | |
| |---|---| |
| | Model | **Ryzova Alphi v1** | |
| | Developed by | **Ryzova** | |
| | Foundation | [Google Gemma 4 E2B-it](https://huggingface.co/google/gemma-4-E2B-it) | |
| | Neural adaptation | **Ryzova Super-v8**, merged LoRA specialization | |
| | Architecture | Gemma 4; **35 language-model layers** (upstream configuration) | |
| | Parameters | Approximately **2.3B effective / 5.1B total**, including embeddings (upstream Gemma 4 E2B figures) | |
| | Context configuration | **128K tokens** in the upstream configuration; Alphi's practical long-context performance requires separate testing | |
| | Languages explored | **English** | |
| | Formats | FP16 Safetensors; F16, Q4_K_M, Q6_K, Q8_0 GGUF | |
| | Verified inference scope | Basic text generation and targeted smoke tests | |
| | System technologies | Matrix Memory, Growing Brain, Ryzova Knowledge, Document RAG and controlled tools — through separate AI Wear integration | |
| | Licensing | [Component-specific license information](LICENSING.md) | |
|
|
| The upstream architecture specifications describe the **Gemma 4 E2B-it foundation**. Our work is the Super-v8 adaptation and the system technologies we develop around the neural model. |
|
|
| ## Choose your model |
|
|
| We offer several formats to make experimentation easier. **Q4_K_M** is a practical first download for local text inference; higher-precision variants allow you to explore different quality and memory trade-offs. |
|
|
| | Download | Format | Weight / file size | Where to start | |
| |---|---|---:|---| |
| | [Alphi FP16](https://huggingface.co/RyzovaTech/Alphi-v1/tree/main) | 3 Safetensors shards | ~9.51 GiB weight shards | Transformers on a compatible GPU | |
| | [Alphi Q4_K_M](gguf/v1/Ryzova-Alphi-v1-Q4_K_M.gguf) | 4-bit GGUF | **3.182 GiB** | Smaller-footprint local inference | |
| | [Alphi Q6_K](gguf/v1/Ryzova-Alphi-v1-Q6_K.gguf) | 6-bit GGUF | **3.567 GiB** | Mid-range quantization | |
| | [Alphi Q8_0](gguf/v1/Ryzova-Alphi-v1-Q8_0.gguf) | 8-bit GGUF | **4.608 GiB** | Higher-precision quantization | |
| | [Alphi F16 GGUF](gguf/v1/Ryzova-Alphi-v1-F16.gguf) | FP16 GGUF | **8.637 GiB** | Full-precision GGUF comparison | |
|
|
| These are **artifact sizes**, not total runtime memory requirements. RAM/VRAM use depends on context length, KV cache, runtime, offloading, and hardware. |
|
|
| ## Quick start |
|
|
| ### Local GGUF inference with llama.cpp |
|
|
| Install a recent `llama.cpp` build supporting Gemma 4. The following is a text-only example using our Q4_K_M release: |
|
|
| ```bash |
| hf download RyzovaTech/Alphi-v1 \ |
| gguf/v1/Ryzova-Alphi-v1-Q4_K_M.gguf \ |
| --local-dir ./alphi |
| |
| llama-completion \ |
| -m ./alphi/gguf/v1/Ryzova-Alphi-v1-Q4_K_M.gguf \ |
| --jinja --reasoning off \ |
| -p 'What is 84 + 126? Answer with the number only.' \ |
| -n 128 |
| ``` |
|
|
| Our GGUF smoke tests used a `llama.cpp` build with `--jinja --reasoning off`. Available executable names and flags depend on your build. This example runs the **neural model**, not the complete AI Wear runtime. |
|
|
| ### FP16 with Transformers |
|
|
| Our merged FP16 model is also available for use with a compatible Gemma 4 Transformers environment. Start with the repository's model and processor assets: |
|
|
| ```python |
| import torch |
| from transformers import AutoProcessor, AutoModelForMultimodalLM |
| |
| model_id = "RyzovaTech/Alphi-v1" |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForMultimodalLM.from_pretrained( |
| model_id, |
| dtype=torch.float16, |
| device_map="auto", |
| ) |
| |
| messages = [{ |
| "role": "user", |
| "content": [{"type": "text", "text": "What is 84 + 126?"}], |
| }] |
| |
| inputs = processor.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_dict=True, |
| return_tensors="pt", |
| ) |
| inputs = {k: v.to(model.device) if hasattr(v, "to") else v |
| for k, v in inputs.items()} |
| |
| with torch.inference_mode(): |
| output = model.generate(**inputs, max_new_tokens=64, do_sample=False) |
| |
| answer = output[0, inputs["input_ids"].shape[-1]:] |
| print(processor.tokenizer.decode(answer, skip_special_tokens=True)) |
| ``` |
|
|
| This is an illustrative integration example, not a separately validated release test for every Transformers version. A compatible Transformers release and sufficient hardware memory are required. Our internal FP16 cold boot used an NVIDIA Tesla T4. |
|
|
| ## Our development path |
|
|
| Alphi did not begin with this single checkpoint. **Our early Alphi R&D included knowledge-distillation work with Qwen3.5 and Mistral 3.** Those experiments were part of the research path toward the Alphi system. |
|
|
| For the **v1 neural model we are releasing here**, we selected **Google Gemma 4 E2B-it** and our **Super-v8 LoRA adaptation**. We merged that adaptation into the FP16 model and prepared GGUF variants for local use. |
|
|
| The earlier distillation work describes **our development history**, not a claim that Qwen or Mistral weight tensors are merged into the downloadable Gemma-derived checkpoint. We are not attaching exact teacher checkpoints, dataset receipts, or teacher-run logs to this release, so we do not present numerical teacher-distillation results here. |
|
|
| Our technical paper documents the Super-v8 adapter configuration: |
|
|
| | Super-v8 detail | Recorded configuration | |
| |---|---:| |
| | Technique | LoRA | |
| | Rank | 4 | |
| | Alpha | 8 | |
| | Dropout | 0.05 | |
| | Trainable LoRA parameters | 1,339,392 | |
| | Language-model layers covered | 35 | |
|
|
| Read the [Alphi technical white paper](https://doi.org/10.5281/zenodo.22942971) for the architecture and archived engineering methodology. |
|
|
| ## What we've tested |
|
|
| We believe in showing what we tested — and keeping that separate from what we intend to build next. |
|
|
| | Test | Result | Scope | |
| |---|---|---| |
| | Frozen FP16 cold boot | **PASS** — 1,951 tensors, 3 shards | Recorded neural load and basic generation | |
| | GGUF simple arithmetic | **4/4 PASS** | All four formats answered `84 + 126 = 210` | |
| | GGUF Sinhala generation | **4/4 PASS** | Small text-generation smoke test | |
| | Q6_K coding prompt | **PASS** | A short Python string-reversal example | |
| | AI Wear Core Intelligence suite | **54/54** in archived internal tests | Separate integrated runtime, not GGUF weights | |
| | AI Wear final integration suite | **51/51** in archived internal tests | Separate system-level checks | |
| | Matrix Memory million-word test | **5/5** planted sentinels | Synthetic retrieval-scale experiment | |
| |
| Our earlier **Frozen-200 internal neural evaluation** recorded an overall score of **73.88** and **did not pass its formal release gate**. We preserve that result in the white paper rather than turning it into an external benchmark claim. |
| |
| We have **not published comparable external scores** for MMLU-Pro, GPQA, IFEval, SWE-bench, LiveCodeBench or BFCL for this checkpoint. The upstream 128K context configuration, multimodal features, and device-specific performance are not independently established as Alphi v1 results by these tests. |
| |
| ## What's included — and what connects separately |
| |
| **Included in this download:** merged neural weights, model configuration, tokenizer/processor assets, chat template, GGUF files, checksum manifest, model card, Alpha Mark, and license notices. |
| |
| **Connected through AI Wear:** Matrix Memory, Growing Brain, Ryzova Knowledge, Document RAG, deterministic utilities, file analysis, and the restricted Python sandbox. |
| |
| This separation is intentional. We can update memory records, evaluate skills, or change retrieved evidence **without pretending that the model weights are continuously retrained**. An app must explicitly integrate the relevant AI Wear components to use them. |
| |
| ## Responsible use |
| |
| Alphi v1 is a starting point for **local text-generation experiments, developer exploration, English/Sinhala interaction, drafting, coding assistance and research into memory-aware AI applications**. |
| |
| Generated text can be inaccurate or biased. Test code before running it; verify important facts; and keep human review for medical, legal, financial, security-sensitive or other consequential uses. Local model files do not automatically guarantee an application's privacy, security or tool permissions. We have not established broad autonomous-agent performance, 128K long-context reliability, or end-to-end image/audio GGUF support with this release. |
| |
| ## File integrity |
| |
| We verified the four GGUF files by remote SHA256 and matched the three FP16 shards against the frozen source. The [GGUF checksum manifest](gguf/v1/GGUF_SHA256SUMS.txt) contains the published hashes. |
| |
| <details> |
| <summary>Verified GGUF SHA256 values</summary> |
| |
| ```text |
| 3b7c50f30229ecb6bf221d2ee12238e08f4532724b46284490f3953bd2bce447 Ryzova-Alphi-v1-F16.gguf |
| a86073fe6e1c9b69a74f0727c06ad00de16547a121a793e46edc5f95ebacc8e4 Ryzova-Alphi-v1-Q4_K_M.gguf |
| e352a3acd38b51642e90e45c08738d9e7bed5990d8e0eca1def14b3e4ece46cf Ryzova-Alphi-v1-Q6_K.gguf |
| cf00612aaf32b16a226518aa3600e6aa24d50cb6eea37c9da159875cfca14bb7 Ryzova-Alphi-v1-Q8_0.gguf |
| ``` |
| </details> |
| |
| ## Licensing |
| |
| We license **Ryzova-owned source code** under **Mozilla Public License 2.0 (MPL-2.0)**. The **Google Gemma 4 E2B-it foundation** is distributed under **Apache License 2.0**, subject to Google's upstream terms and notices. Our merged weights are derived from that foundation and include Ryzova modifications. |
| |
| Because these components have different applicable rights and notices, this repository uses Hugging Face's `license: other` metadata and explains the distribution in [`LICENSING.md`](LICENSING.md), [`LICENSE`](LICENSE), and [`LICENSE-APACHE-2.0.txt`](LICENSE-APACHE-2.0.txt). The neural-model download does not include our complete AI Wear source-code runtime. |
| |
| ## Learn more |
| |
| - **Official Alphi page:** https://www.ryzova.com/ai/alphi |
| - **Ryzova AI:** https://www.ryzova.com/ai |
| - **Ryzova website:** https://www.ryzova.com/ |
| - **Technical white paper:** https://doi.org/10.5281/zenodo.22942971 |
| - **Upstream Gemma 4 E2B-it:** https://huggingface.co/google/gemma-4-E2B-it |
| |
| ### Cite Alphi |
| |
| ```bibtex |
| @misc{ryzova_alphi_v1_2026, |
| author = {{Ryzova}}, |
| title = {Ryzova Alphi v1: Architecture, Persistent Memory, Retrieval, Tool Isolation, and Continual Skill Learning in a Modular Local-First AI System}, |
| year = {2026}, |
| doi = {10.5281/zenodo.22942971}, |
| url = {https://doi.org/10.5281/zenodo.22942971} |
| } |
| ``` |
| |
| --- |
| |
| <p align="center"><strong>Built by Ryzova. Designed for what comes next.</strong></p> |
| |
