Upload dataset

README.md

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+---
+pretty_name: ShareGPT Workload Cases
+language:
+- en
+license: other
+task_categories:
+- text-generation
+tags:
+- synthetic
+- conversations
+- scheduling
+- benchmarking
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - manifest.json
+    - cases/**/*
+---
+
+# ShareGPT Workload Cases
+
+This dataset packages workload-generation artifacts derived from `Aeala/ShareGPT_Vicuna_unfiltered`.
+It includes the manifest describing how cases were generated and per-category case files intended for scheduler and systems benchmarking.
+
+## Contents
+
+- `manifest.json`: dataset generation metadata and category definitions.
+- `cases/`: 176 generated case files across 22 workload categories.
+
+## Generation Metadata
+
+- Split: `train`
+- Cases per category: `8`
+- Tokenizer: `gpt-4` via `tiktoken`
+- Generation seed: `7`
+
+## Category Preview
+
+- `steady_short`: Steady Poisson arrivals dominated by short prompts. (32 requests per case)
+- `steady_mixed`: Steady mixed load with balanced request lengths. (64 requests per case)
+- `bursty_chat`: Bursty conversational traffic with mostly short and medium prompts. (96 requests per case)
+- `bursty_long`: Burst-heavy load with a larger share of long prompts. (72 requests per case)
+- `flash_crowd`: Large clustered traffic spikes for stress testing scheduler fairness and queueing. (192 requests per case)
+- `uniform_workload`: Uniform request sizes and steady arrivals for baseline throughput and scheduler overhead. (64 requests per case)
+- `low_load`: Low-arrival workload with little contention to approximate ideal latency. (32 requests per case)
+- `short_long_mix`: Majority short requests with a minority of long requests to expose head-of-line blocking. (80 requests per case)
+- ...and 14 more categories.
+
+## Notes
+
+- Each category folder also contains a `category.json` file with category-level metadata.
+- Source records are intentionally excluded from this Hub upload.
+- The uploaded layout mirrors the local directory structure for reproducibility.

cases/adversarial_long_burst/category.json

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+{
+  "category": "adversarial_long_burst",
+  "description": "Many long requests arrive nearly simultaneously to stress worst-case latency and memory pressure.",
+  "arrival_mode": "synchronized",
+  "metadata": {
+    "requests_per_case": 48,
+    "stress_focus": "memory_pressure"
+  }
+}

cases/bursty_chat/category.json

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+{
+  "category": "bursty_chat",
+  "description": "Bursty conversational traffic with mostly short and medium prompts.",
+  "arrival_mode": "burst",
+  "metadata": {
+    "requests_per_case": 96
+  }
+}

cases/bursty_long/category.json

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+{
+  "category": "bursty_long",
+  "description": "Burst-heavy load with a larger share of long prompts.",
+  "arrival_mode": "burst",
+  "metadata": {
+    "requests_per_case": 72
+  }
+}

cases/bursty_traffic/category.json

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+{
+  "category": "bursty_traffic",
+  "description": "Bursty arrivals with queue buildup followed by recovery periods.",
+  "arrival_mode": "burst",
+  "metadata": {
+    "requests_per_case": 96
+  }
+}

cases/fairness_test/category.json

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+{
+  "category": "fairness_test",
+  "description": "Heterogeneous request sizes for slowdown and fairness evaluation.",
+  "arrival_mode": "poisson",
+  "metadata": {
+    "requests_per_case": 96,
+    "evaluation_focus": "fairness"
+  }
+}

cases/flash_crowd/category.json

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+{
+  "category": "flash_crowd",
+  "description": "Large clustered traffic spikes for stress testing scheduler fairness and queueing.",
+  "arrival_mode": "burst",
+  "metadata": {
+    "requests_per_case": 192
+  }
+}