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QuickCleverModel

Local inference wrapper with native Apple Metal and CUDA Docker paths, coding-agent client setup and API contract tests.

[DATA][FULL]github.com/KSEGIT/QuickCleverModel ↗

python · shell · metal · cuda · docker · openai-compatible api

Problem

Running a local model is only one part of using it for development. Model files, runtime settings, API clients and coding-agent configuration also need to agree.

Approach

QuickCleverModel packages setup and operations around a local inference runtime. It provides a native Apple Metal path on macOS and a CUDA Docker path on Linux, with model downloads, a browser interface and an OpenAI-compatible API.

Agent setup guides and scripts connect clients such as Codex and Claude Code. Tests cover client configuration, model discovery, startup scripts and API contracts. Runtime validation records keep successful, failed and skipped cases visible.

 model files → local inference runtime → API → coding-agent clients
                    │
             Metal / CUDA Docker
                    │
          setup scripts · contract tests

Tradeoffs

  • Native Metal and CUDA containers need separate setup paths.
  • API compatibility still requires client-specific tests, especially for tool calls and model discovery.
  • Model size, context length and hardware affect what fits and how it responds. Script and contract tests do not establish GPU throughput.

Outcome

A public MIT-licensed wrapper with reproducible setup, agent client integration and validation tooling. The inference engine and model weights remain upstream components with their own licenses and requirements.