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Technical-Implementation.md

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Technical Implementation Plan

  1. High-Level Architecture

[Training (Python)] ──> [ONNX Exporter] ──> [Rust Inference Daemon] │ │ └── fine-tuning + quantization └── IPC (UNIX socket / HTTP) └── Editor/CLI plugin

  1. Component Breakdown a) Training & Export (Python)

    train.py

from transformers import GPT2LMHeadModel, Trainer, TrainingArguments

fine-tune on corpus, save checkpoint

export_model.py

import torch
model = GPT2LMHeadModel.from_pretrained('path/to/checkpoint').eval()
dummy = torch.randint(0, model.config.vocab_size, (1, ctx_len))
torch.onnx.export(model, (dummy,), 'model.onnx',
                  input_names=['input_ids'], output_names=['logits'],
                  dynamic_axes={'input_ids':[1], 'logits':[1]})
# optional: onnxruntime.quantization.quantize_dynamic(...)

b) Inference Daemon (Rust)

Cargo.toml

[dependencies] onnxruntime = { version = "0.18", features = ["cuda"] } tokio = { version = "1", features = ["full"] } serde = "1.0" toml = "0.5"

src/config.rs

#[derive(Deserialize)] pub struct Config { context_window: usize, beam_width: usize, quant: bool } pub fn load() -> Config { /* read config.toml */ }

src/model.rs

use onnxruntime::environment::Environment; pub struct Model { session: Session }; impl Model { pub fn new(path: &str) -> Self { /* init CUDA session / } pub fn predict(&self, input_ids: &[i64]) -> Vec { / run inference */ } }

src/server.rs

#[tokio::main]
async fn main() {
  let cfg = config::load();
  let model = model::Model::new(&cfg.model_path);
  // spawn UNIX socket listener + optional HTTP
  // on request: parse input, call model.predict, return logits/top-k tokens
}

c) Editor/CLI Plugin

Neovim (Lua)

local socket = require('socket.unix') function complete() local ctx = vim.api.nvim_get_current_line() local client = socket() client:connect('/tmp/ai_autocorrect.sock') client:send(vim.inspect({ text = ctx })) local resp = client:receive('*l') return vim.fn.json_decode(resp).suggestions end

Shell fallback

#!/usr/bin/env bash
echo -n "$1" | nc -U /tmp/ai_autocorrect.sock

d) Configuration Example (config.toml)

model_path = "/home/user/.models/gpt2.onnx" context_window = 128 beam_width = 5 quantization = true socket_path = "/tmp/ai_autocorrect.sock"

This PRD + technical spec gives you a robust context to kick off development, measure progress, and iterate. Let's build something that actually ships—no fluff, max impact.