```
feat(cli): add direct model inference via new run subcommand - Add `aha run` CLI subcommand for direct model inference without HTTP service - Support multiple models including Qwen series, OCR models, ASR models, and voice generation - Implement input/output handling with file path support and auto-generation - Add comprehensive documentation in CLI_USAGE.md with examples - Include performance timing for model loading and inference operations - Add macOS build target to Makefile with Metal support ```
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//! Qwen3VL-2B exec implementation for CLI `run` subcommand
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use crate::exec::ExecModel;
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use crate::models::{GenerateModel, qwen3vl::generate::Qwen3VLGenerateModel};
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use anyhow::{Ok, Result};
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use std::time::Instant;
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pub struct Qwen3vlExec;
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impl ExecModel for Qwen3vlExec {
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fn run(input: &str, output: Option<&str>, weight_path: &str) -> Result<()> {
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let target_text = if input.starts_with("file://") {
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let path = &input[7..];
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std::fs::read_to_string(path)?
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} else {
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input.to_string()
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};
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let i_start = Instant::now();
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let mut model = Qwen3VLGenerateModel::init(weight_path, None, None)?;
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let i_duration = i_start.elapsed();
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println!("Time elapsed in load model is: {:?}", i_duration);
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let message = format!(
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r#"{{
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"model": "qwen3vl",
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"messages": [
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{{
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"role": "user",
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"content": "{}"
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}}
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]
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}}"#,
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target_text.replace('"', "\\\"")
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);
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let mes = serde_json::from_str(&message)?;
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let i_start = Instant::now();
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let result = model.generate(mes)?;
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let i_duration = i_start.elapsed();
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println!("Time elapsed in generate is: {:?}", i_duration);
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println!("Result: {:?}", result);
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if let Some(out) = output {
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std::fs::write(out, format!("{:?}", result))?;
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println!("Output saved to: {}", out);
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}
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Ok(())
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}
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}
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