Why desktop + local API?
No WebGPU requirement, easier model management, and stronger offline control. CV text is sent only to 127.0.0.1—never to a cloud model API.
Local TCV Desktop
Python local inference · browser extract / verify / rank / draft
Turn a conventional CV into evidence for a Canadian Tri-Agency narrative CV. This desktop build keeps extract, verify, ranking, and draft in the browser UI while a Python process on localhost runs the language model (Ollama or llama-cpp-python).
No WebGPU requirement, easier model management, and stronger offline control. CV text is sent only to 127.0.0.1—never to a cloud model API.
CV parsing, evidence cards, gap audit, discipline ranking UI, and draft editing remain the same constrained workflow as the web prototype.
Model load, chat completions, and streaming drafts via Ollama (preferred) or GGUF files through llama-cpp-python.
All three routes lead to the same evidence review. Choose the source that gives you the best factual starting point.
Choose an Ollama tag or a GGUF file managed by the desktop API. First Ollama use may pull weights; later runs reuse the local copy.
Prefer Ollama for easiest installs. GGUF files belong in the desktop models/ folder.
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Choose a text-based PDF, Word (.docx), or plain-text file. Scanned image PDFs are not supported in this version.
Try: “For the [opportunity], the project will address [problem] with/for [partners or users]. In my role as [role], I will be responsible for [specific responsibilities]. My prior [expertise or experience] is especially relevant to [criterion or project need].”
Review what the model found, then complete the evidence pathway from your contribution to its use and outcomes. Do not leave invented facts in place.
Choose your discipline so importance ranking and drafting use the right evidence signals. Confirm the top elements, then generate one section.
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