jhqxxx
102 lines
3.2 KiB
Rust
102 lines
3.2 KiB
Rust
use std::collections::HashMap;
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use aha::utils::{find_type_files, get_device};
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use anyhow::Result;
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use candle_core::{Device, pickle::read_all_with_key, safetensors};
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use candle_nn::VarBuilder;
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#[test]
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fn minicpm4_weight() -> Result<()> {
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let model_path = "/home/jhq/huggingface_model/OpenBMB/MiniCPM4-0.5B/";
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let model_list = find_type_files(model_path, "safetensors")?;
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let device = Device::Cpu;
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for m in model_list {
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let weights = safetensors::load(m, &device)?;
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for (key, tensor) in weights.iter() {
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println!("=== {} ===", key);
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println!("Shape: {:?}", tensor.shape());
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println!("DType: {:?}", tensor.dtype());
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}
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}
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Ok(())
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}
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#[test]
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fn voxcpm_weight() -> Result<()> {
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let model_path = "/home/jhq/huggingface_model/openbmb/VoxCPM-0.5B/";
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let model_list = find_type_files(model_path, "pth")?;
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println!("model_list: {:?}", model_list);
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let dev = get_device(None);
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let mut dict_to_hashmap = HashMap::new();
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let mut dtype = candle_core::DType::F16;
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for m in model_list {
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let dict = read_all_with_key(m, Some("state_dict"))?;
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dtype = dict[0].1.dtype();
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for (k, v) in dict {
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println!("key: {}, tensor shape: {:?}", k, v);
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dict_to_hashmap.insert(k, v);
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}
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}
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let vb = VarBuilder::from_tensors(dict_to_hashmap, dtype, &dev);
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let contain_key = vb.contains_tensor("encoder.block.4.block.2.block.3.weight_g");
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println!(
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"contain encoder.block.4.block.2.block.3.weight_g: {}",
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contain_key
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);
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Ok(())
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}
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#[test]
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fn qwen3vl_weight() -> Result<()> {
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let model_path = "/home/jhq/huggingface_model/Qwen/Qwen3-VL-4B-Instruct/";
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let model_list = find_type_files(model_path, "safetensors")?;
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let device = Device::Cpu;
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for m in &model_list {
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let weights = safetensors::load(m, &device)?;
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for (key, tensor) in weights.iter() {
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println!("=== {} === {:?}", key, tensor.shape());
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}
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}
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println!("model_list: {:?}", model_list);
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Ok(())
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}
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#[test]
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fn deepseekocr_weight() -> Result<()> {
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let model_path = "/home/jhq/huggingface_model/deepseek-ai/DeepSeek-OCR/";
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let model_list = find_type_files(model_path, "safetensors")?;
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let device = Device::Cpu;
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for m in &model_list {
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let weights = safetensors::load(m, &device)?;
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for (key, tensor) in weights.iter() {
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if key.contains("rel_pos_h") {
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println!("=== {} === {:?}", key, tensor.shape());
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}
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// println!("=== {} === {:?}", key, tensor.shape());
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}
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}
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println!("model_list: {:?}", model_list);
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Ok(())
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}
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#[test]
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fn hunyuanocr_weight() -> Result<()> {
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let model_path = "/home/jhq/huggingface_model/Tencent-Hunyuan/HunyuanOCR/";
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let model_list = find_type_files(model_path, "safetensors")?;
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let device = Device::Cpu;
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for m in &model_list {
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let weights = safetensors::load(m, &device)?;
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for (key, tensor) in weights.iter() {
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if key.contains(".image_") {
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println!("=== {} === {:?}", key, tensor.shape());
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}
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// println!("=== {} === {:?}", key, tensor.shape());
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}
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}
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println!("model_list: {:?}", model_list);
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Ok(())
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}
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