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# Description This PR introduces the primary entry point for the QUADTRIX engine in src/main.cpp. It establishes a unified workflow that handles model lifecycle management without relying on TorchScript, utilizing our custom internal headers for model architecture. # Key Features - Dual-Mode Execution: Integrated support for both a training loop and an interactive chat mode. - Infinite Generation: Implemented an unconstrained inference loop for continuous text generation. - C++ Architecture: Bypasses TorchScript to use custom-defined layers and headers, ensuring direct control over the execution graph. - Resource Management: only for CPU
# Description This PR synchronizes the model interaction logic across both the Python backend utilities and the web frontend. It establishes a consistent way to interface with the model weights and the C++ engine. ## Python Backend (inference.py) - Goal: Refactor the standalone inference script to support modern weight loading. - Weight Mapping: Updated to load and map .pt files directly using the refactored architecture. - Chat Mode: Implemented a robust interactive loop for rapid model testing and verification. ## Frontend Layer (frontend/src/api) - Goal: Establish the bridge between the UI and the Quadtrix engine. - Service Definition: Created the base API client to handle requests to the C++ backend. - Dual-Path Logic: Added handlers for both Training control and Inference/Chat endpoints. - Stream Support: Prepared the API layer to handle "generation" data chunks for real-time UI updates. ## other PR merge #7 #6 #5 #4 #3
## Summary <img width="2185" height="829" alt="run_20260430_192930" src="https://github.com/user-attachments/assets/420ebbb4-cadf-4408-bc69-fc32ad081c6f" /> ## Model Configuration | Parameter | Value | |---|---| | Layers | 6 | | Heads | 6 | | Embedding dim | 100 | | Block size | 190 | | Batch size | 64 | | Dropout | 0.2 | | Learning rate | 3e-4 | | Total parameters | **10,837,257** | ## Training Details | Field | Value | |---|---| | Steps | 8,000 | | Eval every | 200 steps | | Optimizer seed | 1337 | | Train tokens | 14,080,249 | | Val tokens | 1,564,473 | | Precision | bf16 | | MFU | 60.0% | ## Results | Metric | Value | |---|---| | Best val loss | **2.3918** | | Final train loss | 2.2825 | | Total loss drop | 8.57 | | Peak throughput | 19,602 tok/s | | Mean throughput | 18,756 tok/s | | Peak grad norm | 2.2504 | | Mean grad norm | 1.6894 | | Training time | **82m 43s** | | Checkpoint | `best_model.pt` |
…#30) ## Summary Publish GitHub Package using npm ## Checks - [ ] C++ build still works - [ ] Backend changes were smoke-tested locally - [ ] Frontend build still passes
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Summary
docs improvement with chat images
Checks
C++ build still works
Backend changes were smoke-tested locally
Frontend build still passes
Docs or screenshots were updated if needed