nebius/gpt-oss-120b-Infinity-Instruct-0625
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下载链接：
https://hf-mirror.com/datasets/nebius/gpt-oss-120b-Infinity-Instruct-0625
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资源简介：
---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: generated_message
struct:
- name: annotations
dtype: 'null'
- name: audio
dtype: 'null'
- name: content
dtype: string
- name: function_call
dtype: 'null'
- name: reasoning_content
dtype: string
- name: refusal
dtype: 'null'
- name: role
dtype: string
- name: tool_calls
sequence: 'null'
- name: finish_reason
dtype: string
splits:
- name: train
num_bytes: 6061060422
num_examples: 659358
download_size: 3572636672
dataset_size: 6061060422
---
# gpt-oss-120b-Infinity-Instruct-0625
## Dataset Description
This dataset is part of the LK-Speculators collection for speculative decoding research. It contains 660K prompt-response pairs designed for training draft models that are used alongside gpt-oss-120b as the target model. The dataset was created by generating responses to the prompts from [Infinity-Instruct-0625](https://huggingface.co/datasets/BAAI/Infinity-Instruct) with [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) at temperature=1.
For more details on the training methodology and results, see our paper: [LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding](https://arxiv.org/abs/2602.23881).
## Dataset Structure
- **Format**: parquet
- **Rows**: 659,358
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("nebius/gpt-oss-120b-Infinity-Instruct-0625")
```
## License
The dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
## Citation
```
@misc{samarin2026lklosses,
title = {LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding},
author = {Alexander Samarin and Sergei Krutikov and Anton Shevtsov and Sergei Skvortsov and Filipp Fisin and Alexander Golubev},
year = {2026},
eprint = {2602.23881},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2602.23881}
}
```
提供机构：
nebius
