---
configs:
- config_name: multi-turn_chat
data_files:
- split: test
path: multi-turn_chat.parquet
- config_name: code_completion
data_files:
- split: test
path: code_completion.parquet
- config_name: instruction_tuning
data_files:
- split: test
path: instruction_tuning.parquet
- config_name: code_fixing
data_files:
- split: test
path: code_fixing.parquet
- config_name: rag
data_files:
- split: test
path: rag.parquet
- config_name: large_summarization
data_files:
- split: test
path: large_summarization.parquet
- config_name: docstring
data_files:
- split: test
path: docstring.parquet
---
This dataset contains inference performance benchmarking obtained with vllm version 0.6.1.post2 on different use-case scenarios. The scenarios are defined as bellow:
| Use case |
Prompt tokens |
Generated tokens |
| Code Completion |
256 |
1024 |
| Docstring Generation |
768 |
128 |
| Code Fixing |
1024 |
1024 |
| RAG |
1024 |
128 |
| Instruction Following |
256 |
128 |
| Multi-turn chat |
512 |
256 |
| Large Summarization |
4096 |
512 |
Benchmarking was conducted with [GuideLLM](https://github.com/neuralmagic/guidellm) using the following syntax:
```
guidellm --model --data-type emulated --data "prompt_tokens=,generated_tokens="
```