add server and cli mod

This commit is contained in:
jhqxxx
2026-03-31 21:09:29 +08:00
parent 083e319574
commit 2802038b2e
19 changed files with 717 additions and 785 deletions
+1
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@@ -114,6 +114,7 @@ curl http://localhost:10100/v1/chat/completions \
## Changelog
### 2026-03-31
- add server adn cli mod
- aha model name use modelscope id replace
- update WhichModel
- Usage add time info
+1
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@@ -112,6 +112,7 @@ curl http://localhost:10100/v1/chat/completions \
## 更新日志
### 2026-03-31
- 新增 server 和 cli 模块
- aha模型名称使用 modelscope id 替换
- 更新 WhichModel 枚举
- Usage 增加时间信息
+1
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@@ -6,6 +6,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
### 2026-03-31
- add server adn cli mod
- aha model name use modelscope id replace
- update WhichModel enum
- Usage add time info
+1
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@@ -6,6 +6,7 @@
本项目遵循 [语义化版本](https://semver.org/lang/zh-CN/spec/v2.0.0.html)。
### 2026-03-31
- 新增 server 和 cli 模块
- aha模型名称使用 modelscope id 替换
- 更新 WhichModel 枚举
- Usage 增加时间信息
+194
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@@ -0,0 +1,194 @@
use aha::models::common::model_mapping::WhichModel;
use clap::{Args, Parser, Subcommand};
#[derive(Parser, Debug)]
#[command(name = "aha")]
#[command(version, about, long_about = None)]
pub(crate) struct Cli {
/// Service listen address
#[arg(short, long, default_value = "127.0.0.1")]
pub address: Option<String>,
/// Service listen port
#[arg(short, long)]
pub port: Option<u16>,
/// Model type (required for backward compatibility)
#[arg(short, long)]
pub model: Option<WhichModel>,
/// Local model weight path
#[arg(long)]
pub weight_path: Option<String>,
/// Model download save directory
#[arg(long)]
pub save_dir: Option<String>,
/// Download retry count
#[arg(long)]
pub download_retries: Option<u32>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
pub gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
pub mmproj_path: Option<String>,
#[command(subcommand)]
pub command: Option<Commands>,
}
#[derive(Subcommand, Debug)]
pub(crate) enum Commands {
/// Download model and start service (default)
Cli(CliArgs),
/// Start service only (--weight-path is optional, defaults to ~/.aha/{model_id})
Serv(ServArgs),
/// List all running aha services
Ps(ServListArgs),
/// Delete a downloaded model from the default location (~/.aha/{model_id})
Delete(DeleteArgs),
/// Download model only
Download(DownloadArgs),
/// Run model inference directly
Run(RunArgs),
/// List all supported models
List(ListArgs),
}
/// Common/shared arguments for server operations
#[derive(Args, Debug)]
pub(crate) struct CommonArgs {
/// Service listen address
#[arg(short, long, default_value = "127.0.0.1")]
pub address: String,
/// Service listen port
#[arg(short, long, default_value_t = 10100)]
pub port: u16,
/// Model type (required)
#[arg(short, long)]
pub model: WhichModel,
/// Allow remote shutdown requests (default: local only, use with caution)
#[arg(long)]
pub allow_remote_shutdown: bool,
}
/// Arguments for the 'cli' subcommand (download + serve)
#[derive(Args, Debug)]
pub(crate) struct CliArgs {
#[command(flatten)]
pub common: CommonArgs,
/// Local model weight path (skip download if provided)
#[arg(long)]
pub weight_path: Option<String>,
/// Model download save directory
#[arg(long)]
pub save_dir: Option<String>,
/// Download retry count
#[arg(long)]
pub download_retries: Option<u32>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
pub gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
pub mmproj_path: Option<String>,
}
/// Arguments for the 'serv start' subcommand
#[derive(Args, Debug)]
pub(crate) struct ServArgs {
#[command(flatten)]
pub common: CommonArgs,
/// Local model weight path (defaults to ~/.aha/{model_id} if not specified)
#[arg(long)]
pub weight_path: Option<String>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
pub gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
pub mmproj_path: Option<String>,
}
/// Arguments for the 'serv list' subcommand
#[derive(Args, Debug)]
pub(crate) struct ServListArgs {
/// Compact output format
#[arg(short, long)]
pub compact: bool,
}
/// Arguments for the 'download' subcommand (download only)
#[derive(Args, Debug)]
pub(crate) struct DownloadArgs {
/// Model type (required)
#[arg(short, long)]
pub model: WhichModel,
/// Model download save directory
#[arg(short, long)]
pub save_dir: Option<String>,
/// Download retry count
#[arg(long)]
pub download_retries: Option<u32>,
}
/// Arguments for the 'run' subcommand (direct inference)
#[derive(Args, Debug)]
pub(crate) struct RunArgs {
/// Model type (required)
#[arg(short, long)]
pub model: WhichModel,
/// Input text or file path
#[arg(short, long, num_args = 1..=2, value_delimiter = ' ')]
pub input: Vec<String>,
/// Output file path (optional)
#[arg(short, long)]
pub output: Option<String>,
/// Local model weight path (defaults to ~/.aha/{model_id} if not specified)
#[arg(long)]
pub weight_path: Option<String>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
pub gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
pub mmproj_path: Option<String>,
}
/// Arguments for the 'delete' subcommand (delete model from default location)
#[derive(Args, Debug)]
pub(crate) struct DeleteArgs {
/// Model type (required)
#[arg(short, long)]
pub model: WhichModel,
}
/// Arguments for the 'list' subcommand (list all supported models)
#[derive(Args, Debug)]
pub(crate) struct ListArgs {
/// Output models in JSON format (includes name, model_id, and type fields)
#[arg(short, long)]
pub json: bool,
}
+360
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@@ -0,0 +1,360 @@
use crate::{
cli::args::{CliArgs, DeleteArgs, DownloadArgs, ListArgs, RunArgs, ServArgs, ServListArgs},
server::{
api::init,
process::{ServiceStatus, find_aha_services},
start_http_server,
},
};
use aha::exec::*;
use aha::{
models::common::model_mapping::WhichModel,
utils::{
bytes_to_human, dir_size, download_model, get_default_save_dir, get_default_weight_path,
is_model_downloaded,
},
};
use anyhow::anyhow;
use serde::Serialize;
pub mod args;
/// Model information for JSON output
#[derive(Serialize)]
struct ModelInfo {
model_id: String,
owner: String,
#[serde(rename = "type")]
model_type: String,
downloaded: bool,
}
/// List all supported models
pub(crate) fn run_list(args: ListArgs) -> anyhow::Result<()> {
let models = WhichModel::model_list();
if args.json {
// JSON output
let model_infos: Vec<ModelInfo> = models
.iter()
.map(|model| ModelInfo {
model_id: model.as_string(),
owner: model.model_owner(),
model_type: model.model_type().to_string(),
downloaded: is_model_downloaded(*model),
})
.collect();
println!("{}", serde_json::to_string_pretty(&model_infos)?);
} else {
// Table output (default)
println!("Available models:");
println!();
println!(
"{:<40} {:<20} {:<10} {:<10}",
"Model ID", "Owner", "type", "Download"
);
println!("{}", "-".repeat(80));
for model in models {
let model_id = model.as_string();
let owner = model.model_owner();
let model_type = model.model_type();
let download_status = if is_model_downloaded(model) {
""
} else {
""
};
println!(
"{:<40} {:<20} {:<10} {:<10}",
model_id, owner, model_type, download_status
);
}
}
Ok(())
}
/// Run the 'cli' subcommand: download model (if needed) and start service
pub(crate) async fn run_cli(args: CliArgs) -> anyhow::Result<()> {
let CliArgs {
common,
weight_path,
save_dir,
download_retries,
gguf_path,
mmproj_path,
} = args;
let model_id = common.model.as_string();
let (model_path, gguf, mmproj) = if model_id.contains("gguf") {
if gguf_path.is_none() {
return Err(anyhow!("gguf model path is required"));
}
("GGUF".to_string(), gguf_path, mmproj_path)
} else {
let model_path = match weight_path {
Some(path) => path,
None => {
let save_dir = match save_dir {
Some(dir) => dir,
None => get_default_save_dir().expect("Failed to get home directory"),
};
let max_retries = download_retries.unwrap_or(3);
download_model(&model_id, &save_dir, max_retries).await?;
save_dir + "/" + &model_id
}
};
(model_path, None, None)
};
init(common.model, model_path, gguf, mmproj)?;
start_http_server(common.address, common.port, common.allow_remote_shutdown).await?;
Ok(())
}
/// Run the 'serv' subcommand: start service only (no download)
pub(crate) async fn run_serv(args: ServArgs) -> anyhow::Result<()> {
let ServArgs {
common,
weight_path,
gguf_path,
mmproj_path,
} = args;
let model_id = common.model.as_string();
let (model_path, gguf, mmproj) = if model_id.contains("gguf") {
if gguf_path.is_none() {
return Err(anyhow!("gguf model path is required"));
}
("GGUF".to_string(), gguf_path, mmproj_path)
} else {
let model_path = match weight_path {
Some(path) => path,
None => get_default_weight_path(common.model),
};
(model_path, None, None)
};
init(common.model, model_path, gguf, mmproj)?;
start_http_server(common.address, common.port, common.allow_remote_shutdown).await?;
Ok(())
}
/// Run the 'ps' subcommand: list running AHA services
pub(crate) fn run_ps(args: ServListArgs) -> anyhow::Result<()> {
let services = find_aha_services()?;
if services.is_empty() {
println!("No aha services found running.");
return Ok(());
}
if args.compact {
// Compact format: one service per line
for svc in services {
println!("{}", svc.service_id);
}
} else {
// Table format
println!(
"{:<20} {:<10} {:<20} {:<10} {:<15} {:<10}",
"Service ID", "PID", "Model", "Port", "Address", "Status"
);
println!("{}", "-".repeat(85));
for svc in services {
let model = svc.model.as_deref().unwrap_or("N/A");
let status = match svc.status {
ServiceStatus::Running => "Running",
ServiceStatus::Stopping => "Stopping",
ServiceStatus::Unknown => "Unknown",
};
println!(
"{:<20} {:<10} {:<20} {:<10} {:<15} {:<10}",
svc.service_id, svc.pid, model, svc.port, svc.address, status,
);
}
}
Ok(())
}
/// Run the 'download' subcommand: download model only (no server)
pub(crate) async fn run_download(args: DownloadArgs) -> anyhow::Result<()> {
let DownloadArgs {
model,
save_dir,
download_retries,
} = args;
let model_id = model.as_string();
let save_dir = match save_dir {
Some(dir) => dir,
None => get_default_save_dir().expect("Failed to get home directory"),
};
let max_retries = download_retries.unwrap_or(3);
download_model(&model_id, &save_dir, max_retries).await?;
Ok(())
}
/// Run the 'run' subcommand: direct model inference
pub(crate) fn run_run(args: RunArgs) -> anyhow::Result<()> {
let RunArgs {
model,
input,
output,
weight_path,
gguf_path,
mmproj_path,
} = args;
// Use default weight path if not specified
let weight_path = match weight_path {
Some(path) => path,
None => get_default_weight_path(model),
};
match model {
WhichModel::MiniCPM4_0_5B => {
minicpm4::MiniCPM4Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2_1_2B => {
lfm2::Lfm2Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2_5_1_2BInstruct => {
lfm2::Lfm2Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2_5VL1_6B => {
lfm2vl::Lfm2VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2VL1_6B => {
lfm2vl::Lfm2VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen2_5VL3B => {
qwen2_5vl::Qwen2_5VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen2_5VL7B => {
qwen2_5vl::Qwen2_5VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_0_6B => {
qwen3::Qwen3Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_0_8B => {
qwen3_5::Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_2B => {
qwen3_5::Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_4B => {
qwen3_5::Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_9B => {
qwen3_5::Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5Gguf => {
qwen3_5::Qwen3_5Exec::run_gguf(&input, output.as_deref(), gguf_path, mmproj_path)?;
}
WhichModel::Qwen3ASR0_6B => {
qwen3_asr::Qwen3ASRExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3ASR1_7B => {
qwen3_asr::Qwen3ASRExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL2B => {
qwen3vl::Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL4B => {
qwen3vl::Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL8B => {
qwen3vl::Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL32B => {
qwen3vl::Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::DeepSeekOCR => {
deepseek_ocr::DeepSeekORExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::DeepSeekOCR2 => {
deepseek_ocr::DeepSeekORExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::HunyuanOCR => {
hunyuan_ocr::HunyuanORExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::PaddleOCRVL => {
paddleocr_vl::PaddleOVLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::PaddleOCRVL1_5 => {
paddleocr_vl::PaddleOVLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::RMBG2_0 => {
rmbg2_0::RMBG2_0Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::VoxCPM => {
voxcpm::VoxCPMExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::VoxCPM1_5 => {
voxcpm::VoxCPMExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::GlmASRNano2512 => {
glm_asr_nano::GlmASRNanoExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::FunASRNano2512 => {
fun_asr_nano::FunASRNanoExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::GlmOCR => {
glm_ocr::GlmOcrExec::run(&input, output.as_deref(), &weight_path)?;
}
}
Ok(())
}
/// Run the 'delete' subcommand: delete model from default location
pub(crate) fn run_delete(args: DeleteArgs) -> anyhow::Result<()> {
let DeleteArgs { model } = args;
let model_id = model.as_string();
let save_dir = get_default_save_dir().expect("Failed to get home directory");
let model_path = format!("{}/{}", save_dir, model_id);
let path = std::path::Path::new(&model_path);
if !path.exists() {
println!("Model not found: {} does not exist", model_path);
return Ok(());
}
// Show model info
println!("Model ID: {}", model_id);
println!("Location: {}", model_path);
// Calculate size if possible
if let Ok(metadata) = std::fs::metadata(path)
&& metadata.is_dir()
&& let Ok(total_size) = dir_size(path)
{
println!("Size: {}", bytes_to_human(total_size));
}
// Confirm deletion
print!("Are you sure you want to delete this model? (y/N): ");
use std::io::Write;
std::io::stdout().flush()?;
let mut input = String::new();
std::io::stdin().read_line(&mut input)?;
let input = input.trim().to_lowercase();
if input != "y" && input != "yes" {
println!("Deletion cancelled.");
return Ok(());
}
// Delete the directory
std::fs::remove_dir_all(path)?;
println!("Model deleted successfully: {}", model_path);
Ok(())
}
-1
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@@ -19,7 +19,6 @@ pub mod qwen3_asr;
pub mod qwen3vl;
pub mod rmbg2_0;
pub mod voxcpm;
pub mod voxcpm1_5;
use anyhow::Result;
+4 -2
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@@ -28,8 +28,10 @@ impl ExecModel for VoxCPMExec {
let i_start = Instant::now();
let audio = voxcpm_generate.inference(
target_text,
Some("啥子小师叔,打狗还要看主人,你再要继续,我就是你的对手".to_string()), // todo args
Some("file://./assets/audio/voice_01.wav".to_string()), // todo args
// Some("啥子小师叔,打狗还要看主人,你再要继续,我就是你的对手".to_string()), // todo args
// Some("file://./assets/audio/voice_01.wav".to_string()), // todo args
None,
None,
2,
100, // max_len (voxcpm uses 100 vs OpenBMB/VoxCPM1.5's 4096)
10,
-63
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@@ -1,63 +0,0 @@
//! VoxCPM1.5 exec implementation for CLI `run` subcommand
//!
//! This module handles VoxCPM1.5 model inference for direct CLI execution.
//! Input/output parameter interpretation is handled here as per the design:
//! - Input can be text content or a file path (with `file://` prefix)
//! - Output can be a file path or will be auto-generated if not specified
use std::time::Instant;
use anyhow::{Ok, Result};
use crate::models::voxcpm::generate::VoxCPMGenerate;
use crate::{exec::ExecModel, utils::get_file_path};
pub struct VoxCPM1_5Exec;
impl ExecModel for VoxCPM1_5Exec {
fn run(input: &[String], output: Option<&str>, weight_path: &str) -> Result<()> {
let input_text = &input[0];
let target_text = if input_text.starts_with("file://") {
// let path = &input[7..];
let path = get_file_path(input_text)?;
std::fs::read_to_string(path)?
} else {
input_text.clone()
};
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerate::init(weight_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
let i_start = Instant::now();
let audio = voxcpm_generate.inference(
target_text,
Some("啥子小师叔,打狗还要看主人,你再要继续,我就是你的对手".to_string()), // todo args
Some("file://./assets/audio/voice_01.wav".to_string()), // todo args
2,
4096,
10,
2.0,
6.0,
)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in generate is: {:?}", i_duration);
let output_path = if let Some(out) = output {
out.to_string()
} else {
let timestamp = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)?
.as_secs();
format!("voxcpm1_5_{}.wav", timestamp)
};
let sample_rate = voxcpm_generate.sample_rate();
crate::utils::audio_utils::save_wav(&audio, &output_path, sample_rate as u32)?;
println!("Output saved to: {}", output_path);
Ok(())
}
}
-1
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@@ -3,6 +3,5 @@ pub mod exec;
pub mod models;
pub mod params;
pub mod position_embed;
pub mod process;
pub mod tokenizer;
pub mod utils;
+6 -704
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@@ -1,647 +1,12 @@
use std::sync::atomic::{AtomicBool, Ordering};
use std::{net::IpAddr, str::FromStr, sync::Arc};
use clap::Parser;
use aha::{
models::common::model_mapping::WhichModel,
process::{cleanup_pid_file, create_pid_file},
utils::{download_model, get_default_save_dir},
use crate::cli::{
args::{Cli, CliArgs, Commands, CommonArgs},
run_cli, run_delete, run_download, run_list, run_ps, run_run, run_serv,
};
use anyhow::anyhow;
use clap::{Args, Parser, Subcommand};
use rocket::{
Config,
data::{ByteUnit, Limits},
routes,
};
use serde::Serialize;
use crate::api::{init, set_server_port};
mod api;
#[derive(Parser, Debug)]
#[command(name = "aha")]
#[command(version, about, long_about = None)]
struct Cli {
/// Service listen address
#[arg(short, long, default_value = "127.0.0.1")]
address: Option<String>,
/// Service listen port
#[arg(short, long)]
port: Option<u16>,
/// Model type (required for backward compatibility)
#[arg(short, long)]
model: Option<WhichModel>,
/// Local model weight path
#[arg(long)]
weight_path: Option<String>,
/// Model download save directory
#[arg(long)]
save_dir: Option<String>,
/// Download retry count
#[arg(long)]
download_retries: Option<u32>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
mmproj_path: Option<String>,
#[command(subcommand)]
command: Option<Commands>,
}
#[derive(Subcommand, Debug)]
enum Commands {
/// Download model and start service (default)
Cli(CliArgs),
/// Start service only (--weight-path is optional, defaults to ~/.aha/{model_id})
Serv(ServArgs),
/// List all running aha services
Ps(ServListArgs),
/// Delete a downloaded model from the default location (~/.aha/{model_id})
Delete(DeleteArgs),
/// Download model only
Download(DownloadArgs),
/// Run model inference directly
Run(RunArgs),
/// List all supported models
List(ListArgs),
}
/// Common/shared arguments for server operations
#[derive(Args, Debug)]
struct CommonArgs {
/// Service listen address
#[arg(short, long, default_value = "127.0.0.1")]
address: String,
/// Service listen port
#[arg(short, long, default_value_t = 10100)]
port: u16,
/// Model type (required)
#[arg(short, long)]
model: WhichModel,
/// Allow remote shutdown requests (default: local only, use with caution)
#[arg(long)]
allow_remote_shutdown: bool,
}
/// Arguments for the 'cli' subcommand (download + serve)
#[derive(Args, Debug)]
struct CliArgs {
#[command(flatten)]
common: CommonArgs,
/// Local model weight path (skip download if provided)
#[arg(long)]
weight_path: Option<String>,
/// Model download save directory
#[arg(long)]
save_dir: Option<String>,
/// Download retry count
#[arg(long)]
download_retries: Option<u32>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
mmproj_path: Option<String>,
}
/// Arguments for the 'serv start' subcommand
#[derive(Args, Debug)]
struct ServArgs {
#[command(flatten)]
common: CommonArgs,
/// Local model weight path (defaults to ~/.aha/{model_id} if not specified)
#[arg(long)]
weight_path: Option<String>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
mmproj_path: Option<String>,
}
/// Arguments for the 'serv list' subcommand
#[derive(Args, Debug)]
struct ServListArgs {
/// Compact output format
#[arg(short, long)]
compact: bool,
}
/// Arguments for the 'download' subcommand (download only)
#[derive(Args, Debug)]
struct DownloadArgs {
/// Model type (required)
#[arg(short, long)]
model: WhichModel,
/// Model download save directory
#[arg(short, long)]
save_dir: Option<String>,
/// Download retry count
#[arg(long)]
download_retries: Option<u32>,
}
/// Arguments for the 'run' subcommand (direct inference)
#[derive(Args, Debug)]
struct RunArgs {
/// Model type (required)
#[arg(short, long)]
model: WhichModel,
/// Input text or file path
#[arg(short, long, num_args = 1..=2, value_delimiter = ' ')]
input: Vec<String>,
/// Output file path (optional)
#[arg(short, long)]
output: Option<String>,
/// Local model weight path (defaults to ~/.aha/{model_id} if not specified)
#[arg(long)]
weight_path: Option<String>,
/// Local GGUF model weight path (required for loading models with GGUF).
#[arg(long)]
gguf_path: Option<String>,
/// Local path for mmproj GGUF model weights (required for loading with multimodel GGUF)
#[arg(long)]
mmproj_path: Option<String>,
}
/// Arguments for the 'delete' subcommand (delete model from default location)
#[derive(Args, Debug)]
struct DeleteArgs {
/// Model type (required)
#[arg(short, long)]
model: WhichModel,
}
/// Arguments for the 'list' subcommand (list all supported models)
#[derive(Args, Debug)]
struct ListArgs {
/// Output models in JSON format (includes name, model_id, and type fields)
#[arg(short, long)]
json: bool,
}
/// Get the default weight path for a given model
/// Returns ~/.aha/{model_id} e.g., ~/.aha/OpenBMB/VoxCPM1.5
fn get_default_weight_path(model: WhichModel) -> String {
let model_id = model.as_string();
let save_dir = get_default_save_dir().expect("Failed to get home directory");
format!("{}/{}", save_dir, model_id)
}
/// Check if a model is downloaded by verifying the model directory exists
/// Returns true if ~/.aha/{model_id} directory exists, false otherwise
fn is_model_downloaded(model: WhichModel) -> bool {
let model_id = model.as_string();
let save_dir = match get_default_save_dir() {
Some(dir) => dir,
None => return false,
};
let model_path = format!("{}/{}", save_dir, model_id);
std::path::Path::new(&model_path).exists()
}
/// Model information for JSON output
#[derive(Serialize)]
struct ModelInfo {
model_id: String,
owner: String,
#[serde(rename = "type")]
model_type: String,
downloaded: bool,
}
/// List all supported models
fn run_list(args: ListArgs) -> anyhow::Result<()> {
let models = WhichModel::model_list();
if args.json {
// JSON output
let model_infos: Vec<ModelInfo> = models
.iter()
.map(|model| ModelInfo {
model_id: model.as_string(),
owner: model.model_owner(),
model_type: model.model_type().to_string(),
downloaded: is_model_downloaded(*model),
})
.collect();
println!("{}", serde_json::to_string_pretty(&model_infos)?);
} else {
// Table output (default)
println!("Available models:");
println!();
println!(
"{:<40} {:<20} {:<10} {:<10}",
"Model ID", "Owner", "type", "Download"
);
println!("{}", "-".repeat(80));
for model in models {
let model_id = model.as_string();
let owner = model.model_owner();
let model_type = model.model_type();
let download_status = if is_model_downloaded(model) {
""
} else {
""
};
println!(
"{:<40} {:<20} {:<10} {:<10}",
model_id, owner, model_type, download_status
);
}
}
Ok(())
}
/// Run the 'cli' subcommand: download model (if needed) and start service
async fn run_cli(args: CliArgs) -> anyhow::Result<()> {
let CliArgs {
common,
weight_path,
save_dir,
download_retries,
gguf_path,
mmproj_path,
} = args;
let model_id = common.model.as_string();
let (model_path, gguf, mmproj) = if model_id.contains("gguf") {
if gguf_path.is_none() {
return Err(anyhow!("gguf model path is required"));
}
("GGUF".to_string(), gguf_path, mmproj_path)
} else {
let model_path = match weight_path {
Some(path) => path,
None => {
let save_dir = match save_dir {
Some(dir) => dir,
None => get_default_save_dir().expect("Failed to get home directory"),
};
let max_retries = download_retries.unwrap_or(3);
download_model(&model_id, &save_dir, max_retries).await?;
save_dir + "/" + &model_id
}
};
(model_path, None, None)
};
init(common.model, model_path, gguf, mmproj)?;
start_http_server(common.address, common.port, common.allow_remote_shutdown).await?;
Ok(())
}
/// Run the 'serv' subcommand: start service only (no download)
async fn run_serv(args: ServArgs) -> anyhow::Result<()> {
let ServArgs {
common,
weight_path,
gguf_path,
mmproj_path,
} = args;
let model_id = common.model.as_string();
let (model_path, gguf, mmproj) = if model_id.contains("gguf") {
if gguf_path.is_none() {
return Err(anyhow!("gguf model path is required"));
}
("GGUF".to_string(), gguf_path, mmproj_path)
} else {
let model_path = match weight_path {
Some(path) => path,
None => get_default_weight_path(common.model),
};
(model_path, None, None)
};
init(common.model, model_path, gguf, mmproj)?;
start_http_server(common.address, common.port, common.allow_remote_shutdown).await?;
Ok(())
}
/// Run the 'ps' subcommand: list running AHA services
fn run_ps(args: ServListArgs) -> anyhow::Result<()> {
use aha::process::find_aha_services;
let services = find_aha_services()?;
if services.is_empty() {
println!("No aha services found running.");
return Ok(());
}
if args.compact {
// Compact format: one service per line
for svc in services {
println!("{}", svc.service_id);
}
} else {
// Table format
println!(
"{:<20} {:<10} {:<20} {:<10} {:<15} {:<10}",
"Service ID", "PID", "Model", "Port", "Address", "Status"
);
println!("{}", "-".repeat(85));
for svc in services {
let model = svc.model.as_deref().unwrap_or("N/A");
let status = match svc.status {
aha::process::ServiceStatus::Running => "Running",
aha::process::ServiceStatus::Stopping => "Stopping",
aha::process::ServiceStatus::Unknown => "Unknown",
};
println!(
"{:<20} {:<10} {:<20} {:<10} {:<15} {:<10}",
svc.service_id, svc.pid, model, svc.port, svc.address, status,
);
}
}
Ok(())
}
/// Run the 'download' subcommand: download model only (no server)
async fn run_download(args: DownloadArgs) -> anyhow::Result<()> {
let DownloadArgs {
model,
save_dir,
download_retries,
} = args;
let model_id = model.as_string();
let save_dir = match save_dir {
Some(dir) => dir,
None => get_default_save_dir().expect("Failed to get home directory"),
};
let max_retries = download_retries.unwrap_or(3);
download_model(&model_id, &save_dir, max_retries).await?;
Ok(())
}
/// Run the 'run' subcommand: direct model inference
fn run_run(args: RunArgs) -> anyhow::Result<()> {
use aha::exec::ExecModel;
let RunArgs {
model,
input,
output,
weight_path,
gguf_path,
mmproj_path,
} = args;
// Use default weight path if not specified
let weight_path = match weight_path {
Some(path) => path,
None => get_default_weight_path(model),
};
match model {
WhichModel::MiniCPM4_0_5B => {
use aha::exec::minicpm4::MiniCPM4Exec;
MiniCPM4Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2_1_2B => {
use aha::exec::lfm2::Lfm2Exec;
Lfm2Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2_5_1_2BInstruct => {
use aha::exec::lfm2::Lfm2Exec;
Lfm2Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2_5VL1_6B => {
use aha::exec::lfm2vl::Lfm2VLExec;
Lfm2VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::LFM2VL1_6B => {
use aha::exec::lfm2vl::Lfm2VLExec;
Lfm2VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen2_5VL3B => {
use aha::exec::qwen2_5vl::Qwen2_5VLExec;
Qwen2_5VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen2_5VL7B => {
use aha::exec::qwen2_5vl::Qwen2_5VLExec;
Qwen2_5VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_0_6B => {
use aha::exec::qwen3::Qwen3Exec;
Qwen3Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_0_8B => {
use aha::exec::qwen3_5::Qwen3_5Exec;
Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_2B => {
use aha::exec::qwen3_5::Qwen3_5Exec;
Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_4B => {
use aha::exec::qwen3_5::Qwen3_5Exec;
Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5_9B => {
use aha::exec::qwen3_5::Qwen3_5Exec;
Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3_5Gguf => {
use aha::exec::qwen3_5::Qwen3_5Exec;
Qwen3_5Exec::run_gguf(&input, output.as_deref(), gguf_path, mmproj_path)?;
}
WhichModel::Qwen3ASR0_6B => {
use aha::exec::qwen3_asr::Qwen3ASRExec;
Qwen3ASRExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3ASR1_7B => {
use aha::exec::qwen3_asr::Qwen3ASRExec;
Qwen3ASRExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL2B => {
use aha::exec::qwen3vl::Qwen3VLExec;
Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL4B => {
use aha::exec::qwen3vl::Qwen3VLExec;
Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL8B => {
use aha::exec::qwen3vl::Qwen3VLExec;
Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::Qwen3VL32B => {
use aha::exec::qwen3vl::Qwen3VLExec;
Qwen3VLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::DeepSeekOCR => {
use aha::exec::deepseek_ocr::DeepSeekORExec;
DeepSeekORExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::DeepSeekOCR2 => {
use aha::exec::deepseek_ocr::DeepSeekORExec;
DeepSeekORExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::HunyuanOCR => {
use aha::exec::hunyuan_ocr::HunyuanORExec;
HunyuanORExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::PaddleOCRVL => {
use aha::exec::paddleocr_vl::PaddleOVLExec;
PaddleOVLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::PaddleOCRVL1_5 => {
use aha::exec::paddleocr_vl::PaddleOVLExec;
PaddleOVLExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::RMBG2_0 => {
use aha::exec::rmbg2_0::RMBG2_0Exec;
RMBG2_0Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::VoxCPM => {
use aha::exec::voxcpm::VoxCPMExec;
VoxCPMExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::VoxCPM1_5 => {
use aha::exec::voxcpm1_5::VoxCPM1_5Exec;
VoxCPM1_5Exec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::GlmASRNano2512 => {
use aha::exec::glm_asr_nano::GlmASRNanoExec;
GlmASRNanoExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::FunASRNano2512 => {
use aha::exec::fun_asr_nano::FunASRNanoExec;
FunASRNanoExec::run(&input, output.as_deref(), &weight_path)?;
}
WhichModel::GlmOCR => {
use aha::exec::glm_ocr::GlmOcrExec;
GlmOcrExec::run(&input, output.as_deref(), &weight_path)?;
}
}
Ok(())
}
/// Run the 'delete' subcommand: delete model from default location
fn run_delete(args: DeleteArgs) -> anyhow::Result<()> {
let DeleteArgs { model } = args;
let model_id = model.as_string();
let save_dir = get_default_save_dir().expect("Failed to get home directory");
let model_path = format!("{}/{}", save_dir, model_id);
let path = std::path::Path::new(&model_path);
if !path.exists() {
println!("Model not found: {} does not exist", model_path);
return Ok(());
}
// Show model info
println!("Model ID: {}", model_id);
println!("Location: {}", model_path);
// Calculate size if possible
if let Ok(metadata) = std::fs::metadata(path)
&& metadata.is_dir()
&& let Ok(total_size) = dir_size(path)
{
println!("Size: {}", bytes_to_human(total_size));
}
// Confirm deletion
print!("Are you sure you want to delete this model? (y/N): ");
use std::io::Write;
std::io::stdout().flush()?;
let mut input = String::new();
std::io::stdin().read_line(&mut input)?;
let input = input.trim().to_lowercase();
if input != "y" && input != "yes" {
println!("Deletion cancelled.");
return Ok(());
}
// Delete the directory
std::fs::remove_dir_all(path)?;
println!("Model deleted successfully: {}", model_path);
Ok(())
}
/// Calculate total size of a directory recursively
fn dir_size(path: &std::path::Path) -> anyhow::Result<u64> {
let mut total = 0;
if path.is_dir() {
for entry in std::fs::read_dir(path)? {
let entry = entry?;
let entry_path = entry.path();
if entry_path.is_dir() {
total += dir_size(&entry_path)?;
} else {
total += entry.metadata()?.len();
}
}
} else {
total = std::fs::metadata(path)?.len();
}
Ok(total)
}
/// Convert bytes to human readable format
fn bytes_to_human(bytes: u64) -> String {
const KB: u64 = 1024;
const MB: u64 = KB * 1024;
const GB: u64 = MB * 1024;
const TB: u64 = GB * 1024;
if bytes >= TB {
format!("{:.2} TB", bytes as f64 / TB as f64)
} else if bytes >= GB {
format!("{:.2} GB", bytes as f64 / GB as f64)
} else if bytes >= MB {
format!("{:.2} MB", bytes as f64 / MB as f64)
} else if bytes >= KB {
format!("{:.2} KB", bytes as f64 / KB as f64)
} else {
format!("{} B", bytes)
}
}
mod cli;
mod server;
#[tokio::main]
async fn main() -> anyhow::Result<()> {
@@ -675,66 +40,3 @@ async fn main() -> anyhow::Result<()> {
}
}
}
pub(crate) async fn start_http_server(
address: String,
port: u16,
allow_remote_shutdown: bool,
) -> anyhow::Result<()> {
// Set server port for shutdown endpoint
set_server_port(port, allow_remote_shutdown);
// Create PID file for service tracking
let pid = std::process::id();
create_pid_file(pid, port)?;
// Set up shutdown flag
let shutdown_flag = Arc::new(AtomicBool::new(false));
let shutdown_flag_clone = shutdown_flag.clone();
// Configure Ctrl+C handler for graceful shutdown
let port_for_cleanup = port;
let shutdown_handler = tokio::spawn(async move {
tokio::signal::ctrl_c().await.ok();
println!("Received shutdown signal, gracefully shutting down...");
shutdown_flag_clone.store(true, Ordering::SeqCst);
// Give time for existing requests to complete
tokio::time::sleep(tokio::time::Duration::from_secs(1)).await;
// Cleanup PID file
let _ = cleanup_pid_file(port_for_cleanup);
std::process::exit(0);
});
let mut builder = rocket::build().configure(Config {
address: IpAddr::from_str(&address)?,
port,
limits: Limits::default()
.limit("string", ByteUnit::Mebibyte(5))
.limit("json", ByteUnit::Mebibyte(5))
.limit("data-form", ByteUnit::Mebibyte(100))
.limit("file", ByteUnit::Mebibyte(100)),
..Config::default()
});
builder = builder.mount("/v1/chat", routes![api::chat]);
builder = builder.mount("/chat", routes![api::chat]);
// /images/remove_background
builder = builder.mount("/images", routes![api::remove_background]);
// /audio/speech and /audio/transcriptions (ASR transcription endpoint)
builder = builder.mount("/audio", routes![api::speech, api::transcriptions]);
// /v1/audio/transcriptions (OpenAI standard ASR transcription endpoint)
builder = builder.mount("/v1/audio", routes![api::transcriptions]);
// Health check and model info endpoints
builder = builder.mount("/", routes![api::health, api::models]);
// Shutdown endpoint
builder = builder.manage(shutdown_flag);
builder = builder.mount("/", routes![api::shutdown]);
let _rocket = builder.launch().await?;
// Cleanup PID file when server exits
cleanup_pid_file(port)?;
shutdown_handler.abort();
Ok(())
}
+3 -9
View File
@@ -4,7 +4,6 @@ use std::sync::{Arc, OnceLock};
use aha::models::{GenerateModel, ModelInstance, common::model_mapping::WhichModel, load_model};
use aha::params::chat::ChatCompletionParameters;
use aha::process::cleanup_pid_file;
use aha::utils::string_to_static_str;
use rocket::futures::StreamExt;
use rocket::serde::{Serialize, json::Json};
@@ -18,17 +17,12 @@ use rocket::{
};
use tokio::sync::RwLock;
// ASR (Automatic Speech Recognition) API module
pub(crate) mod asr;
pub(crate) mod asr_types;
// Re-export ASR routes
pub(crate) use asr::transcriptions;
use crate::server::process::cleanup_pid_file;
/// Wrapper to store model type together with the model instance
pub(crate) struct StoredModel {
which_model: WhichModel,
instance: ModelInstance<'static>,
pub which_model: WhichModel,
pub instance: ModelInstance<'static>,
}
// Export MODEL for use in ASR module
+4 -2
View File
@@ -11,8 +11,10 @@ use rocket::http::Status;
use rocket::serde::json::Json;
use rocket::{form::Form, post};
use super::MODEL;
use super::asr_types::{ErrorDetail, ErrorResponse, TranscriptionRequest, TranscriptionResponse};
use crate::server::api::MODEL;
use crate::server::asr_types::{
ErrorDetail, ErrorResponse, TranscriptionRequest, TranscriptionResponse,
};
/// Handle audio transcription requests
///
+77
View File
@@ -0,0 +1,77 @@
use crate::server::api::set_server_port;
use crate::server::process::{cleanup_pid_file, create_pid_file};
use rocket::data::{ByteUnit, Limits};
use rocket::{Config, routes};
use std::net::IpAddr;
use std::str::FromStr;
use std::sync::Arc;
use std::sync::atomic::{AtomicBool, Ordering};
// ASR (Automatic Speech Recognition) API module
pub(crate) mod api;
pub(crate) mod asr;
pub(crate) mod asr_types;
pub(crate) mod process;
pub(crate) async fn start_http_server(
address: String,
port: u16,
allow_remote_shutdown: bool,
) -> anyhow::Result<()> {
// Set server port for shutdown endpoint
set_server_port(port, allow_remote_shutdown);
// Create PID file for service tracking
let pid = std::process::id();
create_pid_file(pid, port)?;
// Set up shutdown flag
let shutdown_flag = Arc::new(AtomicBool::new(false));
let shutdown_flag_clone = shutdown_flag.clone();
// Configure Ctrl+C handler for graceful shutdown
let port_for_cleanup = port;
let shutdown_handler = tokio::spawn(async move {
tokio::signal::ctrl_c().await.ok();
println!("Received shutdown signal, gracefully shutting down...");
shutdown_flag_clone.store(true, Ordering::SeqCst);
// Give time for existing requests to complete
tokio::time::sleep(tokio::time::Duration::from_secs(1)).await;
// Cleanup PID file
let _ = cleanup_pid_file(port_for_cleanup);
std::process::exit(0);
});
let mut builder = rocket::build().configure(Config {
address: IpAddr::from_str(&address)?,
port,
limits: Limits::default()
.limit("string", ByteUnit::Mebibyte(5))
.limit("json", ByteUnit::Mebibyte(5))
.limit("data-form", ByteUnit::Mebibyte(100))
.limit("file", ByteUnit::Mebibyte(100)),
..Config::default()
});
builder = builder.mount("/v1/chat", routes![api::chat]);
builder = builder.mount("/chat", routes![api::chat]);
// /images/remove_background
builder = builder.mount("/images", routes![api::remove_background]);
// /audio/speech and /audio/transcriptions (ASR transcription endpoint)
builder = builder.mount("/audio", routes![api::speech, asr::transcriptions]);
// /v1/audio/transcriptions (OpenAI standard ASR transcription endpoint)
builder = builder.mount("/v1/audio", routes![asr::transcriptions]);
// Health check and model info endpoints
builder = builder.mount("/", routes![api::health, api::models]);
// Shutdown endpoint
builder = builder.manage(shutdown_flag);
builder = builder.mount("/", routes![api::shutdown]);
let _rocket = builder.launch().await?;
// Cleanup PID file when server exits
cleanup_pid_file(port)?;
shutdown_handler.abort();
Ok(())
}
+3 -1
View File
@@ -30,6 +30,7 @@ pub struct ServiceInfo {
/// Service status
#[derive(Debug, Clone, PartialEq)]
#[allow(unused)]
pub enum ServiceStatus {
Running,
Stopping,
@@ -102,7 +103,8 @@ pub fn cleanup_pid_file(port: u16) -> Result<()> {
///
/// # Arguments
/// * `port` - Listen port
pub fn get_pid_from_file(port: u16) -> Option<u32> {
#[allow(unused)]
fn get_pid_from_file(port: u16) -> Option<u32> {
let pid_dir = get_pid_dir().ok()?;
let pid_file = pid_dir.join(format!("{}.pid", port));
+60
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@@ -9,6 +9,7 @@ use std::io::{Cursor, Read};
use std::time::{SystemTime, UNIX_EPOCH};
use std::{collections::HashMap, fs, path::PathBuf, process::Command, time::Duration};
use crate::models::common::model_mapping::WhichModel;
use crate::params::{
chat::{
AudioUrlType, ChatCompletionChoice, ChatCompletionChunkChoice, ChatCompletionChunkResponse,
@@ -975,6 +976,65 @@ pub fn clean_asr_response(raw: &str) -> String {
}
}
/// Get the default weight path for a given model
/// Returns ~/.aha/{model_id} e.g., ~/.aha/OpenBMB/VoxCPM1.5
pub fn get_default_weight_path(model: WhichModel) -> String {
let model_id = model.as_string();
let save_dir = get_default_save_dir().expect("Failed to get home directory");
format!("{}/{}", save_dir, model_id)
}
/// Check if a model is downloaded by verifying the model directory exists
/// Returns true if ~/.aha/{model_id} directory exists, false otherwise
pub fn is_model_downloaded(model: WhichModel) -> bool {
let model_id = model.as_string();
let save_dir = match get_default_save_dir() {
Some(dir) => dir,
None => return false,
};
let model_path = format!("{}/{}", save_dir, model_id);
std::path::Path::new(&model_path).exists()
}
/// Calculate total size of a directory recursively
pub fn dir_size(path: &std::path::Path) -> anyhow::Result<u64> {
let mut total = 0;
if path.is_dir() {
for entry in std::fs::read_dir(path)? {
let entry = entry?;
let entry_path = entry.path();
if entry_path.is_dir() {
total += dir_size(&entry_path)?;
} else {
total += entry.metadata()?.len();
}
}
} else {
total = std::fs::metadata(path)?.len();
}
Ok(total)
}
/// Convert bytes to human readable format
pub fn bytes_to_human(bytes: u64) -> String {
const KB: u64 = 1024;
const MB: u64 = KB * 1024;
const GB: u64 = MB * 1024;
const TB: u64 = GB * 1024;
if bytes >= TB {
format!("{:.2} TB", bytes as f64 / TB as f64)
} else if bytes >= GB {
format!("{:.2} GB", bytes as f64 / GB as f64)
} else if bytes >= MB {
format!("{:.2} MB", bytes as f64 / MB as f64)
} else if bytes >= KB {
format!("{:.2} KB", bytes as f64 / KB as f64)
} else {
format!("{} B", bytes)
}
}
#[cfg(test)]
mod tests {
use super::*;
+1 -1
View File
@@ -19,7 +19,7 @@ fn qwen3_0_6b_generate() -> Result<()> {
"messages": [
{
"role": "user",
"content": "你吃饭了没"
"content": "你好啊,你是谁"
}
]
}
+1 -1
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@@ -12,7 +12,7 @@ use anyhow::{Ok, Result};
#[test]
fn voxcpm1_5_use_message_generate() -> Result<()> {
// RUST_BACKTRACE=1 cargo test -F cuda voxcpm1_5_use_message_generate -r -- --nocapture
// RUST_BACKTRACE=1 cargo test -F cuda --test test_voxcpm1_5 voxcpm1_5_use_message_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!("{}/OpenBMB/VoxCPM1.5/", save_dir);