aha 0.2.5

aha model inference library, now supports Qwen(2.5VL/3/3VL/3.5/ASR/3Embedding/3Reranker), MiniCPM4, VoxCPM/1.5, DeepSeek-OCR/2, Hunyuan-OCR, PaddleOCR-VL/1.5, RMBG2.0, GLM(ASR-Nano-2512/OCR), Fun-ASR-Nano-2512, LFM(2/2.5/2VL/2.5VL)
Documentation 
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use std::{pin::pin, time::Instant};

use aha::{
    models::{GenerateModel, lfm2vl::generate::Lfm2VLGenerateModel},
    params::chat::ChatCompletionParameters,
};
use anyhow::Result;
use rocket::futures::StreamExt;
#[test]
fn lfm2vl_generate() -> Result<()> {
    // test with cuda: RUST_BACKTRACE=1 cargo test -F cuda --test test_lfm2vl lfm2vl_generate -r -- --nocapture

    let save_dir =
        aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    let model_path = format!("{}/LiquidAI/LFM2.5-VL-1.6B/", save_dir);
    // let model_path = format!("{}/LiquidAI/LFM2-VL-1.6B/", save_dir);
    let message = r#"
    {
        "model": "lfm2vl",
        "messages": [
            {
                "role": "user",
                "content": [ 
                    {
                        "type": "image",
                        "image_url": 
                        {
                            "url": "file://./assets/img/ocr_test1.png"
                        }
                    },             
                    {
                        "type": "text", 
                        "text": "图片里面是什么"
                    }
                ]
            }
        ]
    }
    "#;
    let mes: ChatCompletionParameters = serde_json::from_str(message)?;
    let i_start = Instant::now();
    let mut model = Lfm2VLGenerateModel::init(&model_path, None, None)?;
    let i_duration = i_start.elapsed();
    println!("Time elapsed in load model is: {:?}", i_duration);

    let res = model.generate(mes)?;
    println!("generate: \n {:?}", res);
    if let Some(usage) = &res.usage {
        println!("usage: \n {:?}", usage);
    }
    Ok(())
}

#[tokio::test]
async fn lfm2vl_stream() -> Result<()> {
    // test with cuda: RUST_BACKTRACE=1 cargo test -F cuda --test test_lfm2vl lfm2vl_stream -r -- --nocapture
    // test with cuda+flash-attn: RUST_BACKTRACE=1 cargo test -F cuda,flash-attn qwen3_0_6b_generate -r -- --nocapture

    let save_dir =
        aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    // let model_path = format!("{}/LiquidAI/LFM2-1.2B/", save_dir);
    let model_path = format!("{}/LiquidAI/LFM2.5-VL-1.6B/", save_dir);
    let message = r#"
    {
        "model": "lfm2vl",
        "messages": [
            {
                "role": "user",
                "content": [ 
                    {
                        "type": "image",
                        "image_url": 
                        {
                            "url": "file://./assets/img/ocr_test1.png"
                        }
                    },             
                    {
                        "type": "text", 
                        "text": "请分析图片并提取所有可见文本内容，按从左到右、从上到下的布局，返回纯文本"
                    }
                ]
            }
        ]
    }
    "#;
    let mes: ChatCompletionParameters = serde_json::from_str(message)?;
    let i_start = Instant::now();
    let mut model = Lfm2VLGenerateModel::init(&model_path, None, None)?;
    let i_duration = i_start.elapsed();
    println!("Time elapsed in load model is: {:?}", i_duration);

    let i_start = Instant::now();
    // let result = model.generate(mes)?;
    let mut stream = pin!(model.generate_stream(mes)?);
    let i_duration = i_start.elapsed();
    while let Some(token) = stream.next().await {
        println!("generate: \n {:?}", token);
    }
    println!("Time elapsed in generate is: {:?}", i_duration);

    Ok(())
}