Update README.md

README.md

@@ -1,5 +1,5 @@
 Since Huggingface has omitted to publish a standalone pytorch SmolLM2_360M_model.py to load and finetune and run inference of the released model weights and config at https://huggingface.co/HuggingFaceTB/SmolLM2-360M/
-I have attempted to construct a model.py that can load and at least do inference mode using the published weights and config.  One a functioning pytorch model.py is built, it may be possible to export a torchscript version of the SmolLM2 model that can be implemented on non-python hardware such as MPUs or Risc machines or Smartphones, in edge devices. The SmolLM2_360M_model.py runs but is unable to load the safetensors data.  Here is the encountered error:
+I have attempted to construct a pytorch model.py that can load and at least do inference mode using the published weights and config.  One a functioning pytorch model.py is built, it may be possible to export a torchscript version of the SmolLM2 model that can be implemented on non-python hardware such as MPUs or Risc machines or Smartphones, in edge devices. The SmolLM2_360M_model.py runs but is unable to load the safetensors data.  Here is the encountered error:
 
 C:\Users\User\OneDrive\Desktop\SmolLM2>python SmolLM2_360M_model_debugging.py
 Warning: SentencePiece not found, using rudimentary BPE tokenizer. Install SentencePiece for better performance.
@@ -32,6 +32,122 @@ Is there a python script for inspecting the safetensors file?
 Why does model.safetensors file "not contain tensor lm_head.weight"?
 
 
+# Help Needed: Building a Standalone PyTorch SmolLM2-360M Model
+
+The Hugging Face Hub hosts the SmolLM2-360M model ([HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M/)), but currently lacks a standalone PyTorch `model.py` file for loading, fine-tuning, and inference. This limits the model's usability outside the Hugging Face ecosystem.
+
+I've started creating a `SmolLM2_360M_model.py` file to address this gap, aiming for compatibility with all SmolLM2 models.  The initial goal is to enable inference using the published weights and config.  A successful PyTorch implementation would pave the way for exporting a TorchScript version, broadening accessibility to non-Python environments like microcontrollers, RISC-V machines, smartphones, and other edge devices.
+
+**The Challenge:**
+
+While my `SmolLM2_360M_model.py` runs, it encounters problems loading the `safetensors` data. I'm receiving the following error:
+
+```
+# Insert the full error message here, including traceback. This will help others diagnose the problem quickly.
+# For example:
+Traceback (most recent call last):
+  File "SmolLM2_360M_model.py", line 32, in <module>
+    model.load_state_dict(torch.load("pytorch_model.bin"))
+  File ".../python3.8/site-packages/torch/serialization.py", line 781, in load
+    with _open_file_like(f, 'rb') as opened_file:
+FileNotFoundError: [Errno 2] No such file or directory: 'pytorch_model.bin'
+
+```
+
+**Call to Action:**
+
+I'm seeking assistance from experienced PyTorch developers to debug the loading issue and complete the `SmolLM2_360M_model.py` implementation.  Your contributions will significantly expand the potential applications of SmolLM2.
+
+**Specific Areas Where Help is Needed:**
+
+* **Safetensors Loading:**  Resolving the error encountered when loading the model weights from the safetensors file.
+* **Model Architecture Verification:**  Confirming the correctness of the PyTorch model architecture based on the config file.
+* **Inference Implementation:**  Ensuring the model can perform inference correctly.
+* **Fine-tuning Support (Optional):**  Adding functionality for fine-tuning the model on downstream tasks.
+* **TorchScript Export (Optional):**  Enabling export to TorchScript for deployment on resource-constrained devices.
+
+**How to Contribute:**
+
+1. Fork the repository containing the `SmolLM2_360M_model.py` file.
+2. Debug the code and implement the missing functionality.
+3. Submit a pull request with your changes.
+
+By working together, we can make SmolLM2 more accessible and empower a wider range of users to leverage its capabilities.  Thank you for your time and expertise!
+
+
+P.S.  Here's a technical breakdown of the process for creating a TorchScript version of the model and deploying it to various platforms:
+
+**1. TorchScript Creation:**
+
+* **Trace or Script:** TorchScript offers two ways to convert your PyTorch model: tracing and scripting. Tracing records the operations performed on example inputs, creating a static graph. Scripting directly parses the model code, supporting control flow. Scripting is preferred if your model uses dynamic control flow.
+```python
+# Tracing Example
+example_input = torch.randn(1, 3, 224, 224)  # Example input
+traced_model = torch.jit.trace(model, example_input)
+
+# Scripting Example
+scripted_model = torch.jit.script(model) 
+```
+
+* **Optimization (Optional):**  TorchScript provides optimization passes to improve the performance of the exported model.
+```python
+optimized_model = torch.jit.optimize_for_inference(scripted_model)
+```
+
+* **Saving:** Save the TorchScript model to a file.
+```python
+torch.jit.save(optimized_model, "smolLM2_360m.pt")
+```
+
+**2. Deployment to Target Environments:**
+
+* **C++:**  LibTorch, the C++ API for PyTorch, can load and execute TorchScript models. Integrate `libTorch` into your C++ application for microcontroller, RISC-V, or other edge device deployments. This typically involves compiling your C++ code and linking against `libTorch`.
+
+* **Android/iOS:** Use the respective PyTorch Mobile libraries for these platforms. These libraries offer optimized runtime environments for executing TorchScript models within mobile applications.
+
+* **Other Edge Devices:** Depending on the device and its capabilities, explore options like using a custom runtime, or if available, a cross-compilation toolchain to target the device from your development environment.
+
+**Example C++ Deployment (Simplified):**
+
+```c++
+#include <torch/script.h>
+
+int main() {
+  // Load the TorchScript model
+  torch::jit::script::Module module = torch::jit::load("smolLM2_360m.pt");
+
+  // Prepare input tensor
+  // ... (Device-specific input tensor preparation) ...
+
+  // Run inference
+  std::vector<torch::jit::IValue> inputs;
+  inputs.push_back(input_tensor);  // Add input tensor(s)
+  auto output = module.forward(inputs);
+
+  // Process output
+  // ... (Handle output tensor on the device) ...
+
+  return 0;
+}
+```
+
+**Key Considerations:**
+
+* **Hardware Limitations:** Microcontrollers and other edge devices have limited resources. Model size and complexity may need adjustments (quantization, pruning) for optimal performance.
+
+* **Platform-Specific Tooling:**  Each target platform has its own build system and toolchain.  Familiarize yourself with these tools for successful deployment.
+
+* **Cross-Compilation:** If building directly on the target device isn't feasible, cross-compilation is necessary. This typically involves setting up a cross-compilation toolchain for the target architecture.
+
+* **Debugging:** Debugging on edge devices can be challenging.  Thoroughly testing the TorchScript model within a more accessible environment (e.g., your development machine) before deploying is essential.
+
+
+This expanded explanation provides a more complete roadmap for creating and deploying TorchScript versions of the SmolLM2 model.  Remember to consult the official PyTorch and LibTorch documentation for platform-specific instructions and best practices.
+
+
+
+
+
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 license: apache-2.0
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