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---
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:

<table>
  <tr>
    <td style="text-align: center"><strong>Use case</strong></td>
    <td style="text-align: center"><strong>Prompt tokens</strong></td>
    <td style="text-align: center"><strong>Generated tokens</strong></td>
  </tr>
  <tr>
    <td>Code Completion</td>
    <td style="text-align: center">256</td>
    <td style="text-align: center">1024</td>
  </tr>
  <tr>
    <td>Docstring Generation</td>
    <td style="text-align: center">768</td>
    <td style="text-align: center">128</td>
  </tr>
  <tr>
    <td>Code Fixing</td>
    <td style="text-align: center">1024</td>
    <td style="text-align: center">1024</td>
  </tr>
  <tr>
    <td>RAG</td>
    <td style="text-align: center">1024</td>
    <td style="text-align: center">128</td>
  </tr>
  <tr>
    <td>Instruction Following</td>
    <td style="text-align: center">256</td>
    <td style="text-align: center">128</td>
  </tr>
  <tr>
    <td>Multi-turn chat</td>
    <td style="text-align: center">512</td>
    <td style="text-align: center">256</td>
  </tr>
  <tr>
    <td>Large Summarization</td>
    <td style="text-align: center">4096</td>
    <td style="text-align: center">512</td>
  </tr>
</table>

Benchmarking was conducted with [GuideLLM](https://github.com/neuralmagic/guidellm) using the following syntax:

```
guidellm --model <model name> --data-type emulated --data "prompt_tokens=<prompt tokens>,generated_tokens=<generated tokens>"

```