Create src/lib.rs

src/lib.rs

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+use pyo3::prelude::*;
+use pyo3::exceptions::PyRuntimeError;
+use candle_core::{Tensor, Device};
+use std::collections::HashMap;
+use std::fs::File;
+use std::path::Path;
+
+/// Helper function to perform raw tensor manipulation/quantization
+fn perform_quantization(input_path: &str, output_path: &str, precision: &str) -> Result<(), String> {
+    let device = Device::Cpu; // Can switch to Device::new_cuda(0) for GPU
+    
+    // 1. Read raw Llama 3.2 safetensors from the input directory
+    let content = std::fs::read(input_path)
+        .map_err(|e| format!("Failed to read input safetensor file: {}", e))?;
+    
+    let safetensor = safetensors::SafeTensors::deserialize(&content)
+        .map_err(|e| format!("Invalid Safetensor format: {}", e))?;
+
+    let mut new_tensors = HashMap::new();
+
+    // 2. Iterate through layers (Weights, Attention blocks, Tensors)
+    for (name, tensor_view) in safetensor.tensors() {
+        let candle_tensor = candle_core::safetensors::load_buffer(tensor_view, &device)
+            .map_err(|e| format!("Candle failed to parse tensor {}: {}", name, e))?;
+
+        // 3. Low-level optimization based on UI input
+        let optimized_tensor = match precision {
+            "F16" => candle_tensor.to_dtype(candle_core::DType::F16)
+                .map_err(|e| e.to_string())?,
+            "BF16" => candle_tensor.to_dtype(candle_core::DType::BF16)
+                .map_err(|e| e.to_string())?,
+            _ => candle_tensor // Keep original if layout matches
+        };
+
+        new_tensors.insert(name, optimized_tensor);
+    }
+
+    // 4. Safely serialize and stream the weights back to disk
+    let output_file = Path::new(output_path);
+    candle_core::safetensors::save(&new_tensors, output_file)
+        .map_err(|e| format!("Failed to write optimized Llama 3.2 model: {}", e))?;
+
+    Ok(())
+}
+
+/// This is the actual function exposed directly to Python
+#[pyfunction]
+fn compress_safetensors(repo_id: String, precision: String) -> PyResult<bool> {
+    // Fake directory path routing for local example layout
+    let input_file = format!("./models/{}/model.safetensors", repo_id);
+    let output_file = format!("./models/{}/model_{}.safetensors", repo_id, precision.to_lowercase());
+
+    println!("[Rust Engine] Quantizing Llama 3.2 weights for: {}", repo_id);
+
+    match perform_quantization(&input_file, &output_file, &precision) {
+        Ok(_) => {
+            println!("[Rust Engine] Success. Optimized file written to {}", output_file);
+            Ok(true)
+        },
+        Err(err_msg) => {
+            // Converts native Rust system errors into explicit Python exceptions
+            Err(PyRuntimeError::new_err(err_msg))
+        }
+    }
+}
+
+/// Defines the Python library entry point structures
+#[pymodule]
+fn llama32_rust_core(m: &Bound<'_, PyModule>) -> PyResult<()> {
+    m.add_function(wrap_pyfunction!(compress_safetensors, m)?)?;
+    Ok(())
+}