| # Environment Record and Known Reproducibility Limitations |
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|
| ## Run-Specific Evidence Available |
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|
| The Qwen2.5-Coder-7B run has a contemporaneous package freeze, command, environment and |
| GPU snapshot under `evidence/training_runs/qwen25_coder_7b_cpt/`. Key versions were: |
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|
| | Component | Version | |
| |---|---| |
| | ms-swift | 4.0.2 | |
| | PyTorch | 2.5.1+cu121 | |
| | Transformers | 5.3.0 | |
| | Accelerate | 1.13.0 | |
| | DeepSpeed | 0.18.8 | |
| | FlashAttention | 2.8.3, CUDA 12 / PyTorch 2.5 / CPython 3.11 wheel | |
| | GPUs | four NVIDIA H100 80 GB devices | |
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|
| The run completed 18,165/18,165 steps and three epochs with recorded train loss 0.5602 and |
| token accuracy 0.8744. These are training telemetry, not downstream benchmark scores. |
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|
| The GPT-2 and CodeGPT runs record the exact Docker image |
| `nvidia/cuda:12.2.2-devel-ubuntu22.04`, build command, effective trainer arguments, and the |
| clean llm.c Git commit. They do not include a Python package freeze because the trainer was |
| built and executed inside that CUDA development image. |
|
|
| ## Known Gaps |
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| The following May 2026 runs do not have their own complete package or system snapshots: |
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| - `models/pretrained/stage1_cpt` |
| - `models/sft/sft_f3_refined_instruct` |
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|
| Their checkpoint `args.json` files record effective ms-swift parameters, and their exact |
| datasets and historical launch scripts are included. A nearby April 2026 ms-swift snapshot |
| used the same key versions as the 7B run, but treating that as the exact May environment is |
| an inference. Bit-for-bit numerical retraining is not claimed. |
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|
| The shared 20 GB raw dataset used by CodeGPT, GPT-2 and Qwen2.5-Coder-7B is not duplicated |
| in this package. Its historical source path, size and record count are recorded in the main |
| README. Reproducing those runs requires authorised access to that exact source data. |
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|
| Raw llm.c model checkpoints and optimiser states are excluded after conversion to standard |
| Hugging Face checkpoints. Qwen optimiser, scheduler and RNG states are also excluded. The |
| delivered weights support inference and further training, but not exact mid-run resumption. |
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|
| ## Delivery Validation Environment |
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|
| Validation uses the locally installed Python, PyTorch, Transformers and Safetensors |
| versions recorded in `evidence/smoke_test_results.json`. The smoke test checks that each |
| checkpoint is structurally loadable and produces finite, non-degenerate logits; it is not |
| a scientific performance evaluation. |
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|
