Clarify usage, outcomes and implementation scope

README.md

@@ -1,40 +1,26 @@
-# Devino
+# devino
 
-An independently maintained experimental toolkit for model conversion and local inference environments. Validated features can be adopted individually by consuming products. Environment advice is supplied by [intel-gpu-wsl-advisor](https://github.com/zixcel/intel-gpu-wsl-advisor).
+Experiment with model conversion and local inference setup before adopting a verified feature in another tool.
 
-# Project Overview
+## What you can do
 
-This repository provides an environment for leveraging OpenVINO and OneAPI to enable LLM inference on Intel devices. It includes scripts for setting up an Ubuntu-based environment, converting models to OpenVINO IR format, and deploying an OpenVINO model server.
+- Review conversion and environment scripts.
+- Compare explicitly configured local inference experiments.
 
-## Repository Structure
+## Current scope
 
-- [setup](./setup): Provides scripts for setting up a Pytorch-XPU environment using Ubuntu 22 and Poetry.
-installation.
-- Model Directories: Named according to Hugging Face model IDs, each containing conversion and inference scripts based on the setup environment.
-- [playground](./playground/): Contains sample scripts tested in an OpenVINO 2025 and Ubuntu 24 environment.
+This is an experimental toolset. Hardware, model files and wheel sources are registered inputs; a script or documentation example is not production compatibility evidence.
 
-## Key Features
+Package distribution is not activated by this documentation. Use the checked-in source and the declared dependency versions; published availability must be verified separately.
 
-- **OpenVINO IR Conversion**: Converts Hugging Face models to OpenVINO IR format for optimized inference.
-- **OpenVINO Model Server (OVMC)**: Implements an OpenVINO model server for running converted models.
-- **GPU Acceleration**: Provides performance improvements for inference using Intel GPUs.
+## Getting started
 
-## Getting Started
+Start with the implementation and examples linked below. Review registered configuration and prerequisites before running a command that writes state or contacts a service.
 
-1. **Verify Ubuntu Compatibility**: Check the appropriate WSL Ubuntu version using [intel-gpu-wsl-advisor](https://github.com/zixcel/intel-gpu-wsl-advisor).
-  - The advisor is optional; callers may verify the Windows driver, WSL kernel and runtime requirements directly.
-2. **Setup Environment**: Use the scripts in `setup/` to install dependencies and configure Pytorch-XPU.
-3. **Convert Models**: Run the provided conversion scripts to transform models into OpenVINO IR format.
-4. **Deploy Model Server**: Install OpenVINO GenAI's OVMC server and execute converted models. by [Makefile](./Makefile)
+## Documentation and source
 
-## References
+[Interface reference](docs/interface-reference.md)
 
-- [OpenVINO Documentation (2025)](https://docs.openvino.ai/2025/index.html)
-- [Hugging Face Optimum-Intel](https://huggingface.co/blog/deploy-with-openvino)
+[Usage guide](docs/getting-started.md)
 
-This repository is under active development, integrating new features for optimized inference and deployment on Intel hardware.
-
-
-## Registered local inputs
-
-Some historical setup recipes use a local PyTorch wheel. Supply the selected, verified wheel at `registration/torch.whl` before running such a recipe; registration data and downloaded model weights are excluded from Git. External model/runtime licenses and hardware compatibility must be verified for the selected experiment. The migration validates source and configuration without downloading models, starting servers, or changing the host.
+[Contributing](CONTRIBUTING.md) · [Security reporting](SECURITY.md) · [License](LICENSE) · [Attribution notices](NOTICE)

docs/getting-started.md

@@ -0,0 +1,22 @@
+# Using devino
+
+Experiment with model conversion and local inference setup before adopting a verified feature in another tool.
+
+## Before you start
+
+This is an experimental toolset. Hardware, model files and wheel sources are registered inputs; a script or documentation example is not production compatibility evidence.
+
+## First steps
+
+Read the declared configuration and implementation before enabling an operation. Choose an explicitly registered target and review any write or external effect.
+
+## How to assess the result
+
+- Review conversion and environment scripts.
+- Compare explicitly configured local inference experiments.
+
+A passing source-level check establishes only what that check observes. Keep missing configuration, unavailable services and unverified deployment paths visible.
+
+## Continue reading
+
+[Repository overview](../README.md)

docs/interface-reference.md

@@ -0,0 +1,35 @@
+# devino interface reference
+
+Use the [usage guide](getting-started.md) for the first steps. This reference preserves the current interface details and operational limits. Run command examples from the repository root, after preparing the exact declared dependencies and registered configuration.
+
+## Repository Structure
+
+- [setup](../setup): Provides scripts for setting up a Pytorch-XPU environment using Ubuntu 22 and Poetry.
+installation.
+- Model Directories: Named according to Hugging Face model IDs, each containing conversion and inference scripts based on the setup environment.
+- [playground](../playground): Contains sample scripts tested in an OpenVINO 2025 and Ubuntu 24 environment.
+
+## Key Features
+
+- **OpenVINO IR Conversion**: Converts Hugging Face models to OpenVINO IR format for optimized inference.
+- **OpenVINO Model Server (OVMC)**: Implements an OpenVINO model server for running converted models.
+- **GPU Acceleration**: Provides performance improvements for inference using Intel GPUs.
+
+## Getting Started
+
+1. **Verify Ubuntu Compatibility**: Check the appropriate WSL Ubuntu version using [intel-gpu-wsl-advisor](https://github.com/zixcel/intel-gpu-wsl-advisor).
+  - The advisor is optional; callers may verify the Windows driver, WSL kernel and runtime requirements directly.
+2. **Setup Environment**: Use the scripts in `setup/` to install dependencies and configure Pytorch-XPU.
+3. **Convert Models**: Run the provided conversion scripts to transform models into OpenVINO IR format.
+4. **Deploy Model Server**: Install OpenVINO GenAI's OVMC server and execute converted models. using the checked-in experiment scripts
+
+## References
+
+- [OpenVINO Documentation (2025)](https://docs.openvino.ai/2025/index.html)
+- [Hugging Face Optimum-Intel](https://huggingface.co/blog/deploy-with-openvino)
+
+This repository is under active development, integrating new features for optimized inference and deployment on Intel hardware.
+
+## Registered local inputs
+
+Some historical setup recipes use a local PyTorch wheel. Supply the selected, verified wheel at `registration/torch.whl` before running such a recipe; registration data and downloaded model weights are excluded from Git. External model/runtime licenses and hardware compatibility must be verified for the selected experiment. The migration validates source and configuration without downloading models, starting servers, or changing the host.