| # Environment Requirements |
|
|
| ## Hardware |
| - 8× NVIDIA H100 80GB (or A100 80GB) per training run |
| - ~500 GB shared disk for model weights + checkpoints + train data |
| - ~200 GB system RAM recommended (DeepSpeed ZeRO-3 large weights gathering) |
|
|
| ## OS |
| - Ubuntu 22.04 LTS (kernel 5.15+) |
| - CUDA 12.8 drivers (12.4+ should also work) |
|
|
| ## Three conda environments |
|
|
| We use **THREE separate environments** to avoid dependency conflicts between |
| training (LlamaFactory + DeepSpeed) and inference (vLLM), plus a tiny CPU-only |
| env for the ensemble. Each phase has a pinned `requirements/<phase>.txt`. |
|
|
| ### Env 1: `llama-qa35` (TRAINING + Phase 1 data builders) |
| Used by data_builders/*.py and `llamafactory-cli train configs/*.yaml`. |
| ```bash |
| conda create -n llama-qa35 python=3.12 -y |
| conda activate llama-qa35 |
| pip install -r requirements/train.txt |
| # LlamaFactory installed in dev mode (git clone) |
| git clone https://github.com/hiyouga/LLaMA-Factory.git |
| cd LLaMA-Factory && pip install -e ".[deepspeed]" |
| ``` |
| |
| ### Env 2: `vllm` (INFERENCE — Phase 3) |
| Used by `scripts/launch_all_predicts.sh` → 19 LoRA cand CSVs. |
| ```bash |
| conda create -n vllm python=3.12 -y |
| conda activate vllm |
| pip install -r requirements/infer.txt |
| ``` |
| |
| ### Env 3: `rouge` (ENSEMBLE — Phase 4, CPU only, any python 3.10+) |
| Used by `scripts/build_ensemble.py` to write the regenerated submission CSV. |
| ```bash |
| conda create -n rouge python=3.12 -y # or use any base python |
| conda activate rouge |
| pip install -r requirements/ensemble.txt |
| ``` |
|
|
| ## Workspace layout |
|
|
| Every script, YAML, and builder in this package is hard-coded to the workspace root **`/mnt/msrh/Magic_submission/`**. Extract the archive (or symlink) so the tree lives at that exact location. Inside it you also need: |
| |
| ``` |
| /mnt/msrh/Magic_submission/ |
| ├── data/ # Zindi competition CSVs (Train/Val/Test/SampleSubmission) |
| ├── hub/ # base + retrieval HF snapshots (see below) |
| │ ├── Qwen3.5-27B/ |
| │ ├── Qwen3.6-27B/ |
| │ ├── Qwen3-32B/ |
| │ └── AfriE5-Large-instruct/ |
| ├── LF/data/ # generated train/test JSONLs (created by data_builders/) |
| ├── hf_cache/ # HF cache for retrieval encoder |
| ├── checkpoints/ # 19 LoRA adapters shipped with this package |
| ├── checkpoints_trained/ # output dir for any from-scratch retrains |
| └── candidate_csvs/ # generated per-cand prediction CSVs |
| ``` |
| |
| ## Base models (HuggingFace) |
| |
| Download into `/mnt/msrh/Magic_submission/hub/`: |
| ```bash |
| HUB=/mnt/msrh/Magic_submission/hub |
| |
| # Qwen3.5-27B-Base — main model |
| huggingface-cli download Qwen/Qwen3.5-27B --local-dir $HUB/Qwen3.5-27B |
| |
| # Qwen3.6-27B-Base |
| huggingface-cli download Qwen/Qwen3.6-27B --local-dir $HUB/Qwen3.6-27B |
| |
| # Qwen3-32B-Base |
| huggingface-cli download Qwen/Qwen3-32B --local-dir $HUB/Qwen3-32B |
| ``` |
| |
| ## Retrieval model |
| |
| ```bash |
| # Multilingual E5 instruct (AfriE5) — used by all data_builders/*.py |
| huggingface-cli download McGill-NLP/AfriE5-Large-instruct \ |
| --local-dir /mnt/msrh/Magic_submission/hub/AfriE5-Large-instruct |
| ``` |
| |
| ## Key packages cheatsheet |
| |
| | Package | Train env | Infer env | Notes | |
| |---|---|---|---| |
| | torch | 2.11.0+cu128 | 2.10.0+cu128 | DIFFERENT versions — keep envs separate | |
| | transformers | 5.2.0 | 4.57.6 | DIFFERENT — train needs newer for Qwen3.5/6 | |
| | vllm | — | 0.19.1 | Inference only | |
| | llamafactory | 0.9.5.dev0 | — | Train only (editable install) | |
| | peft | 0.18.1 | 0.19.1 | LoRA support | |
| | deepspeed | 0.18.8 | — | ZeRO-3 | |
| | rouge-score | — | — (CPU env) | 0.1.2 for ensemble medoid_ngram | |
| |
