| # Full pipeline diagram (text) |
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| ``` |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β STEP 1: DATA (raw Zindi CSVs β AfriE5 retrieval) β |
| β β |
| β Train.csv Val.csv Test.csv β |
| β β β β β |
| β βββββββββ΄βββββββββ β |
| β β β |
| β AfriE5 retrieval (McGill-NLP/AfriE5-Large-instruct) β |
| β build_afrie5_k5.py --k=3,5,7 β |
| β β β |
| β ββββββββββΌβββββββββ β |
| β βΌ βΌ βΌ β |
| β TV_k3.json TV_k5.json TV_k7.json β |
| β (train+test, top-K passages per query) β |
| β β |
| β Fewshot demo prep (top-K similar Q,A from Train+Val) β |
| β build_fewshot_train_k{3,4,5,7}.py + test counterparts β |
| β β β |
| β ββββββββββΌβββββββββ β |
| β βΌ βΌ βΌ β |
| β k3_fewshot k5_fewshot k7_fewshot β |
| β β β |
| β v8 prompt swap (replace v1 instruction with v8) β |
| β build_v8_k5_fewshot.py β |
| β β β |
| β βΌ β |
| β k5_fewshot_v8.json β |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β |
| βΌ |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β STEP 2: TRAIN (13 LoRA adapters) β OPTIONAL for reviewer β |
| β β |
| β β Reviewer can SKIP this step β all 19 LoRA adapters β |
| β are PRE-TRAINED and shipped in checkpoints/<name>/ β |
| β (~35 GB total, 1.8-2.1 GB per adapter). β |
| β β |
| β Training recipes (LlamaFactory + DeepSpeed ZeRO-3): β |
| β Q3.5-27B base Γ K=3 v1 (3ep) β ck-1200, ck-1100 β |
| β Q3.5-27B base Γ K=3 v1 (5ep) β ck-1200 β |
| β Q3.5-27B base Γ K=4 v1 (3ep) β ck-1600 β |
| β Q3.5-27B base Γ K=5 V8 (3ep) β ck-1500 β |
| β Q3.5-27B base Γ K=7 v1 (3ep) β ck-1600, ck-1200 β |
| β Q3.6-27B base Γ K=3 v1 (3ep RecA) β ck-1600 β |
| β Q3.6-27B base Γ K=4 v1 (3ep) β ck-1400 β |
| β Q3.6-27B base Γ K=5 v1 (3ep) β ck-1200, ck-1000 β |
| β Q3.6-27B base Γ K=7 V8 (3ep) β β ck-1600 β |
| β Q3-32B base Γ K=3 v1 (3ep) β ck-1400 β |
| β Q3-32B base Γ K=5 v1 (3ep) β ck-1700 β |
| β Q3-32B base Γ K=7 v1 (3ep) β ck-1600, ck-1200 β |
| β + 3 NoFewshots SFT runs (same LoRA recipe, no demos): β |
| β Q3.5-27B (5ep ck-2800) qwen35_27b_nofewshots_5ep...yaml β |
| β Q3.6-27B (5ep ck-2600) qwen36_27b_nofewshots_5ep...yaml β |
| β Q3-32B (4ep ck-6500) qwen3_32b_nofewshots_4ep...yaml β |
| β β |
| β LoRA: r=128, Ξ±=256, dropout=0.05, target=all β |
| β LR=2e-4 cosine warmup 5%, bf16, gc=true β |
| β eff_bs=64 (per_dev=2, ga=4, GPUs=8) β |
| β Wall time: 13 Γ (~8-14h on 8Γ H100) sequential β |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β |
| βΌ |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β STEP 3: PREDICT (19 CSVs from 19 provided LoRA adapters) β |
| β β |
| β bash scripts/launch_all_predicts.sh β |
| β (orchestrator runs all 19 in parallel/sequential) β |
| β β |
| β For each adapter in checkpoints/<descriptive_name>/: β |
| β vllm_predict_extra.py β |
| β --base <Qwen base model> β |
| β --adapter checkpoints/<descriptive_name> β |
| β --rag_test data/msrh_rag_test_k3_AfriE5_TV*.json β |
| β --max_lora_rank 128 --max_new 512 β |
| β --max_model_len 6144/8192/10240 (per K) --no_think β |
| β --temperature 0.0 --best_of 1 β |
| β β β |
| β jsonl_rowidx_to_csv.py β candidate_csvs/<name>.csv β |
| β β |
| β Produces 19 prediction CSVs in candidate_csvs/ β |
| β (the folder is empty in the shipped package β they β |
| β are GENERATED here from provided checkpoints) β |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β |
| βΌ |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β STEP 4: ENSEMBLE (V2 medoid_ngram on 19 cands) β |
| β β |
| β build_ensemble.py β |
| β β |
| β For each Test row: β |
| β 1. Collect 19 candidate answer texts β |
| β 2. Compute pairwise ROUGE-1.F + ROUGE-2.F (171 pairs) β |
| β 3. For each cand, sum similarity to all others β |
| β 4. Pick candidate with HIGHEST sum (medoid) β |
| β 5. Use its answer text β |
| β β β |
| β βΌ β |
| β go.csv β |
| β (2618 rows Γ 4 target columns, all = same medoid pick) β |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β |
| βΌ |
| ββββββββββββββββββββββ |
| β Submit to Zindi β |
| β Public LB 0.7388 β |
| β Private LB Top 1πβ |
| ββββββββββββββββββββββ |
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