| # ============================================================================== |
| # Requirements for v3 -> v4 NADRA fine-tuning on the HBL GPU server |
| # ============================================================================== |
| # |
| # HOW TO GET THESE ONTO THE SERVER (no internet on the GPU box): |
| # |
| # On your internet-connected laptop, with the SAME Python version and OS |
| # family (Windows) as the target server: |
| # |
| # pip download -r requirements.txt -d wheelhouse |
| # |
| # Then copy the entire `wheelhouse` folder to the GPU server and install |
| # fully offline with: |
| # |
| # pip install --no-index --find-links=wheelhouse -r requirements.txt |
| # |
| # IMPORTANT: run `pip download` on a machine with the same OS (Windows) |
| # and Python minor version (e.g. 3.10.x) as the server, or the downloaded |
| # wheels won't match and pip will silently try to hit the network on the |
| # server and fail. |
| # |
| # BITSANDBYTES / WINDOWS NOTE: |
| # Current stable bitsandbytes (0.45.x+) publishes official Windows CUDA |
| # wheels directly on PyPI -- a plain `pip download bitsandbytes` on the |
| # laptop should work, no need for third-party Windows forks. |
| # Compatibility matrix to double-check against your server's CUDA Toolkit |
| # before committing to a long run: |
| # - CUDA Toolkit 11.7-12.6 supported on Windows |
| # - Requires MSVC 19.38+ (VS2022) runtime present |
| # - NF4/4-bit quantization needs NVIDIA Maxwell or newer -- your A16 |
| # (Ampere, compute capability 8.6) is well within spec. |
| # Test 4-bit model loading in isolation on the server FIRST (small script, |
| # just load + quantize, no training) before starting an unattended run -- |
| # CUDA/driver mismatches surface as first-op runtime errors, not install |
| # errors, so a clean `pip install` does not guarantee it will actually work. |
| # |
| # The training script defaults to optim="adamw_torch" (no bitsandbytes |
| # optimizer kernels needed at all). Only bitsandbytes' 4-bit LOADING path |
| # is required by default. Set USE_BNB_PAGED_OPTIMIZER = True in the script |
| # only after separately confirming the 8-bit optimizer kernels work on this |
| # server -- it's a distinct risk surface from 4-bit quantized loading. |
| # |
| # ============================================================================== |
|
|
| torch>=2.1,<2.5 |
| transformers>=4.44,<4.50 |
| accelerate>=0.33,<0.35 |
| peft>=0.12,<0.14 |
| bitsandbytes>=0.45,<0.50 |
| datasets>=2.20,<2.22 |
| numpy>=1.24,<2.0 |
| pandas>=2.0,<2.3 |
| safetensors>=0.4.3 |
