▸case-01 I need to analyze the compute efficiency of four fine-tuning methods for our LLaMA model: LoRA, QLoRA, Full Fine-Tuning, and IA3. Here are the evaluation scores on MMLU: LoRA got 68.5, QLoRA got 67.2, Full FT got 71.0, and IA3 got 64.1. Here is the compute resource data: LoRA used 1.2e18 FLOPs, 12 GPU hours, 10M params, costing $35; QLoRA used 8.0e17 FLOPs, 8 GPU hours, 5M params, costing $20; Full FT used 1.5e19 FLOPs, 150 GPU hours, 7000M params, costing $450; IA3 used 4.0e17 FLOPs, 4 GPU hours, 2M params, costing $10. Please evaluate which methods sit on the Pareto frontier, calculate efficiency rankings across parameters and FLOPS, provide normalized scores, and suggest top choices for low, medium, and high budget tiers. | fail→pass | 17,016 | 14,532 | -15% | 1 | 1 | 0% | 3,957 | 4,019 | +2% | 0 | 0 | — |
▸case-02 We recently benchmarked several image classification models on ImageNet top-1 accuracy: ResNet-50 scored 76.1%, ViT-B/16 scored 81.8%, ConvNeXt-T scored 82.1%, and MobileNetV3 scored 75.2%. The compute profile for each is: ResNet-50 (4.1B FLOPs, 18 GPU hours, 25.6M params, $25 cost), ViT-B/16 (17.6B FLOPs, 85 GPU hours, 86.6M params, $120 cost), ConvNeXt-T (4.5B FLOPs, 24 GPU hours, 28.6M params, $32 cost), MobileNetV3 (0.22B FLOPs, 2 GPU hours, 5.4M params, $3 cost). Can you calculate which options form the Pareto optimal frontier, produce efficiency rankings relative to FLOPs, GPU hours, and parameters, give compute-normalized scores, and summarize low, medium, and high budget recommendations? | fail→pass | 22,872 | 14,541 | -36% | 1 | 1 | 0% | 5,158 | 3,964 | -23% | 0 | 0 | — |
▸case-03 Please run a performance vs compute evaluation on our sentiment analysis models. Scores on SST-2: Model-A (89.2), Model-B (92.4), Model-C (94.1), Model-D (94.3). Compute usage: Model-A (1.5e15 FLOPs, 1.5 GPU hours, 110M params, $3.50 cost), Model-B (6.0e15 FLOPs, 5.0 GPU hours, 340M params, $12.00 cost), Model-C (3.0e16 FLOPs, 22.0 GPU hours, 1.3B params, $50.00 cost), Model-D (1.2e17 FLOPs, 90.0 GPU hours, 3.0B params, $200.00 cost). Provide a breakdown with Pareto optimal flags, efficiency rankings per compute metric, normalized performance scores, and practical method picks across low, medium, and high resource budgets. | fail→pass | 17,554 | 12,015 | -32% | 1 | 1 | 0% | 3,942 | 3,526 | -11% | 0 | 0 | — |
▸case-04 We are training ResNet-50 on ImageNet and need to find the best learning rate decay schedule. We tested step decay, cosine annealing, and exponential decay. Step decay reached 75.8% accuracy after 90 epochs, cosine annealing reached 76.5%, and exponential decay reached 75.2%. Please write a Python script that sets up PyTorch CosineAnnealingLR scheduler for our training loop. | pass→fail | 8,285 | 17,428 | +110% | 1 | 1 | 0% | 1,893 | 4,303 | +127% | 0 | 0 | — |
▸case-05 We want to quantize our fine-tuned BERT model from FP32 to INT8 precision for faster inference on CPU using ONNX Runtime. Can you provide the Python snippet using ONNX Runtime Quantization tool to perform dynamic quantization on bert_model.onnx? | pass→pass | 7,844 | 10,510 | +34% | 1 | 1 | 0% | 1,747 | 2,438 | +40% | 0 | 0 | — |
▸case-06 Here is a memory trace summary from PyTorch profiler during a transformer forward pass: aten::linear took 45ms (62% CUDA time), aten::softmax took 12ms (16% CUDA time), and memory allocation peaked at 14.2 GB VRAM on a 16GB GPU. What optimization techniques should we apply to reduce CUDA execution time in the attention block? | pass→fail | 16,315 | 16,642 | +2% | 1 | 1 | 0% | 2,857 | 3,692 | +29% | 0 | 0 | — |
▸case-07 We evaluated CodeLlama-7B, StarCoder-15B, and DeepSeek-Coder-6.7B on HumanEval pass@1: CodeLlama (33.5%), StarCoder (33.6%), DeepSeek-Coder (48.2%). Compute profile: CodeLlama (2.1e19 FLOPs, 120 GPU hours, 7B params, $360 cost), StarCoder (5.2e19 FLOPs, 280 GPU hours, 15B params, $840 cost), DeepSeek-Coder (2.0e19 FLOPs, 115 GPU hours, 6.7B params, $345 cost). Perform compute normalization and trade-off evaluation in structured format. | fail→pass | 13,185 | 12,892 | -2% | 1 | 1 | 0% | 2,799 | 3,205 | +15% | 0 | 0 | — |
▸case-08 Compare ASR models on LibriSpeech WER (lower score is better performance): Whisper-tiny (10.5% WER, raw accuracy metric 89.5), Whisper-base (7.2% WER, raw accuracy 92.8), Whisper-large-v3 (3.8% WER, raw accuracy 96.2). Compute costs: Whisper-tiny (0.04e18 FLOPs, 2 GPU hours, 39M params, $5 cost), Whisper-base (0.15e18 FLOPs, 6 GPU hours, 74M params, $15 cost), Whisper-large-v3 (3.2e18 FLOPs, 80 GPU hours, 1550M params, $200 cost). Provide compute normalization analysis. | fail→pass | 16,569 | 15,552 | -6% | 1 | 1 | 0% | 3,502 | 3,983 | +14% | 0 | 0 | — |
▸case-09 Analyze object detectors on COCO mAP: YOLOv8n (37.3 mAP), YOLOv8m (50.2 mAP), YOLOv8x (53.9 mAP). Compute profile: YOLOv8n (8.7B FLOPs, 4 GPU hours, 3.2M params, $10 cost), YOLOv8m (78.9B FLOPs, 22 GPU hours, 25.9M params, $55 cost), YOLOv8x (257.8B FLOPs, 72 GPU hours, 68.2M params, $180 cost). Provide Pareto frontier and efficiency analysis. | fail→pass | 15,610 | 10,265 | -34% | 1 | 1 | 0% | 3,347 | 2,782 | -17% | 0 | 0 | — |
▸case-10 We benchmarked machine translation models on WMT19 BLEU score: MarianMT (32.1), mBART-50 (36.4), NLLB-200-1.3B (39.8). Compute profile: MarianMT (0.5e17 FLOPs, 5 GPU hours, 74M params, $12 cost), mBART-50 (4.2e17 FLOPs, 35 GPU hours, 610M params, $85 cost), NLLB-200-1.3B (1.1e18 FLOPs, 95 GPU hours, 1300M params, $230 cost). Compute the normalized efficiency breakdown. | fail→pass | 17,111 | 14,688 | -14% | 1 | 1 | 0% | 3,851 | 3,718 | -3% | 0 | 0 | — |
▸case-11 Evaluate tabular classifiers on Kaggle AUC: XGBoost (0.842 AUC), LightGBM (0.845 AUC), CatBoost (0.849 AUC), TabNet (0.812 AUC). Compute usage: XGBoost (0.01e15 FLOPs, 0.2 GPU hours, 0.5M params, $0.50 cost), LightGBM (0.008e15 FLOPs, 0.15 GPU hours, 0.4M params, $0.40 cost), CatBoost (0.03e15 FLOPs, 0.8 GPU hours, 1.2M params, $2.00 cost), TabNet (0.8e15 FLOPs, 12.0 GPU hours, 15M params, $30.00 cost). Return formal Pareto trade-off breakdown. | fail→pass | 17,697 | 14,892 | -16% | 1 | 1 | 0% | 3,585 | 3,793 | +6% | 0 | 0 | — |
▸case-12 Assess text embedding models on MTEB score: e5-small-v2 (61.2), bge-small-en-v1.5 (62.1), bge-large-en-v1.5 (64.2). Compute costs: e5-small-v2 (0.02e18 FLOPs, 3 GPU hours, 33M params, $8 cost), bge-small-en-v1.5 (0.02e18 FLOPs, 3 GPU hours, 33M params, $8 cost), bge-large-en-v1.5 (0.35e18 FLOPs, 30 GPU hours, 335M params, $75 cost). Conduct compute normalization evaluation. | fail→pass | 16,227 | 11,691 | -28% | 1 | 1 | 0% | 3,405 | 3,196 | -6% | 0 | 0 | — |
▸case-13 Benchmark video classification models on Kinetics-400 top-1 accuracy: SlowFast (77.0%), TimeSformer (78.0%), VideoMAE (85.2%). Compute profiles: SlowFast (65.0B FLOPs, 40 GPU hours, 34M params, $100 cost), TimeSformer (196.0B FLOPs, 90 GPU hours, 121M params, $220 cost), VideoMAE (180.0B FLOPs, 75 GPU hours, 86M params, $180 cost). Calculate efficiency metrics and normalized scores. | fail→pass | 17,165 | 9,012 | -47% | 1 | 1 | 0% | 3,608 | 2,414 | -33% | 0 | 0 | — |
▸case-14 Evaluate RL agents on Atari Pong score: DQN (18.5), PPO (20.2), IMPALA (20.8). Compute resources: DQN (0.5e16 FLOPs, 10 GPU hours, 1.5M params, $25 cost), PPO (1.2e16 FLOPs, 18 GPU hours, 2.8M params, $45 cost), IMPALA (5.0e16 FLOPs, 60 GPU hours, 12M params, $150 cost). Perform budget tier recommendation and Pareto profiling. | fail→pass | 15,081 | 11,252 | -25% | 1 | 1 | 0% | 2,849 | 2,878 | +1% | 0 | 0 | — |
▸case-15 Benchmark neural rerankers on MS MARCO MRR@10: MiniLM-L6 (36.8), bge-reranker-base (38.2), bge-reranker-large (39.5). Compute costs: MiniLM-L6 (0.05e17 FLOPs, 4 GPU hours, 22M params, $10 cost), bge-reranker-base (0.3e17 FLOPs, 18 GPU hours, 110M params, $45 cost), bge-reranker-large (1.1e17 FLOPs, 65 GPU hours, 335M params, $160 cost). Generate compute normalization report. | fail→pass | 16,913 | 10,462 | -38% | 1 | 1 | 0% | 3,539 | 3,066 | -13% | 0 | 0 | — |
▸case-16 Evaluate diffusion image generators on COCO FID (lower is better, compute inverted quality score = 100 - FID): Stable Diffusion 1.5 (FID 12.5, score 87.5), SDXL 1.0 (FID 9.2, score 90.8), PixArt-alpha (FID 8.7, score 91.3). Compute profiles: SD 1.5 (1.5e18 FLOPs, 100 GPU hours, 980M params, $250 cost), SDXL (8.5e18 FLOPs, 450 GPU hours, 3500M params, $1100 cost), PixArt-alpha (3.1e18 FLOPs, 180 GPU hours, 600M params, $450 cost). Run Pareto frontier and efficiency ranking. | fail→pass | 18,758 | 14,012 | -25% | 1 | 1 | 0% | 4,001 | 3,441 | -14% | 0 | 0 | — |
▸case-17 Compare time series forecasting models on Electricity dataset MSE (quality score = 100 * (1 - MSE)): N-BEATS (MSE 0.280, score 72.0), Informer (MSE 0.310, score 69.0), PatchTST (MSE 0.220, score 78.0). Compute costs: N-BEATS (0.02e17 FLOPs, 2 GPU hours, 1.8M params, $5 cost), Informer (0.05e17 FLOPs, 5 GPU hours, 3.2M params, $12 cost), PatchTST (0.03e17 FLOPs, 3 GPU hours, 0.9M params, $8 cost). Analyze compute efficiency. | fail→pass | 15,169 | 15,657 | +3% | 1 | 1 | 0% | 2,907 | 3,878 | +33% | 0 | 0 | — |
▸case-18 Evaluate vision-language models on VQA v2 accuracy: CLIP-ViT-L (65.2%), BLIP-2 (78.3%), LLaVA-1.5-13B (80.0%). Compute profile: CLIP-ViT-L (0.8e18 FLOPs, 40 GPU hours, 420M params, $100 cost), BLIP-2 (3.5e18 FLOPs, 160 GPU hours, 3800M params, $400 cost), LLaVA-1.5-13B (9.2e18 FLOPs, 420 GPU hours, 13000M params, $1050 cost). Determine Pareto optimal models and recommendations. | fail→pass | 14,363 | 11,355 | -21% | 1 | 1 | 0% | 2,792 | 2,983 | +7% | 0 | 0 | — |
▸case-19 Compare dense retrieval models on MS MARCO Recall@100: DPR (84.5%), Contriever (86.1%), SPLADE-v2 (86.8%). Compute profile: DPR (0.4e17 FLOPs, 12 GPU hours, 110M params, $30 cost), Contriever (0.4e17 FLOPs, 12 GPU hours, 110M params, $30 cost), SPLADE-v2 (1.2e17 FLOPs, 38 GPU hours, 110M params, $95 cost). Produce compute efficiency analysis. | fail→pass | 15,563 | 10,910 | -30% | 1 | 1 | 0% | 2,926 | 2,794 | -5% | 0 | 0 | — |
▸case-20 Evaluate Graph Neural Networks on OGB-ArXiv F1 score: GCN (71.7%), GAT (73.8%), GraphSAGE (72.6%). Compute profile: GCN (0.001e17 FLOPs, 0.5 GPU hours, 0.2M params, $1.50 cost), GAT (0.012e17 FLOPs, 3.5 GPU hours, 1.4M params, $9.00 cost), GraphSAGE (0.004e17 FLOPs, 1.2 GPU hours, 0.6M params, $3.00 cost). Calculate compute-normalized scores and Pareto optimal choices. | fail→pass | 18,189 | 16,214 | -11% | 1 | 1 | 0% | 3,638 | 4,141 | +14% | 0 | 0 | — |
▸case-21 Evaluate protein language models on secondary structure prediction accuracy: ESM-2 8M (68.1%), ESM-2 35M (72.4%), ESM-2 650M (79.2%). Compute usage: ESM-2 8M (0.01e18 FLOPs, 1 GPU hour, 8M params, $2.50 cost), ESM-2 35M (0.06e18 FLOPs, 4 GPU hours, 35M params, $10.00 cost), ESM-2 650M (1.20e18 FLOPs, 70 GPU hours, 650M params, $175.00 cost). Run Pareto frontier analysis. | fail→pass | 16,047 | 10,698 | -33% | 1 | 1 | 0% | 3,118 | 2,888 | -7% | 0 | 0 | — |
▸case-22 Compare Named Entity Recognition models on CoNLL-2003 F1 score: BERT-base (91.2%), RoBERTa-large (92.8%), DeBERTa-v3-large (93.5%). Compute costs: BERT-base (0.08e17 FLOPs, 2 GPU hours, 110M params, $5 cost), RoBERTa-large (0.35e17 FLOPs, 8 GPU hours, 355M params, $20 cost), DeBERTa-v3-large (0.42e17 FLOPs, 10 GPU hours, 435M params, $25 cost). Provide budget recommendations. | fail→pass | 15,709 | 11,328 | -28% | 1 | 1 | 0% | 3,082 | 2,873 | -7% | 0 | 0 | — |