| --- |
| license: cc0-1.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| pretty_name: NVForge Synthetic GPU Instruct 480 |
| size_categories: |
| - n<1K |
| --- |
| |
| # NVForge Synthetic GPU Instruct 480 |
|
|
| **Unofficial, independently generated synthetic instruction dataset** about NVIDIA-related GPU computing topics, including CUDA, inference, VRAM, multi-GPU communication, optimization, data science and model serving. **Not created, sponsored, reviewed, or endorsed by NVIDIA.** |
|
|
| ## Dataset contents |
| - 480 entirely synthetic instruction-response conversations, all in English. |
| - 432 training rows and 48 validation rows. |
| - 24 topics, five instruction styles and four paraphrased prompts per style and topic. |
| - Files: `train.jsonl` and `validation.jsonl`. |
| - Every row includes `id`, `topic`, `category`, `messages`, `synthetic`, `affiliation`, `language`. |
|
|
| ## Example |
|
|
| ```json |
| {"id":"nvforge-0001","topic":"CUDA","category":"concept_explanation","messages":[{"role":"user","content":"Explain CUDA to a beginner learning about NVIDIA GPU computing."},{"role":"assistant","content":"CUDA is NVIDIA’s parallel computing platform and programming model. It lets developers run suitable computations on NVIDIA GPUs. A CUDA program typically distinguishes CPU host code from GPU device kernels."}],"synthetic":true,"affiliation":"unofficial","language":"en"} |
| ``` |
|
|
| ## Intended use |
| - Demo pipelines, schema testing, tutorials and synthetic instruction-tuning experiments. |
| - General introductory technical explanations only; verify details against official documentation before relying on them for engineering decisions. |
|
|
| ## Limitations |
| - Highly templated, intentionally repetitive dataset. Not an independent quality benchmark, not hardware telemetry, not measured GPU performance, and not a dataset of real NVIDIA user requests. |
| - NVIDIA is a trademark of its respective owner. References are informational only. |
| - No proprietary content, scraped transcripts, or real measured benchmarks were included. |
|
|
| ## Generation method |
| The examples were constructed locally from 24 authored informational topic descriptions, five question formats, and four variants per format, with a fixed shuffle seed (42). The 90/10 train/validation split is random, so similar examples may appear in both splits; this is **not** suitable for generalization evaluations. |
|
|
| ## License |
| CC0-1.0 for the original synthetic data provided here. Trademarks belong to their respective owners. |
|
|
