
Open-PerfectBlend is an open-source reproduction of the instruction dataset introduced in the paper "The Perfect Blend: Redefining RLHF with Mixture of Judges". It's a solid general-purpose instruction dataset with chat, math, code, and instruction-following data.

Here is the list of the datasets used in this mix:
| Dataset | # Samples |
|---|---|
| meta-math/MetaMathQA | 395,000 |
| openbmb/UltraInteract_sft | 288,579 |
| HuggingFaceH4/ultrachat_200k | 207,865 |
| microsoft/orca-math-word-problems-200k | 200,035 |
| HuggingFaceH4/ultrafeedback_binarized | 187,405 |
| theblackcat102/evol-codealpaca-v1 | 111,272 |
| Post-training-Data-Flywheel/AutoIF-instruct-61k | 61,492 |
| mlabonne/lmsys-arena-human-preference-55k-sharegpt | 57,362 |
The deduplication process removed 88.1k samples across all datasets. All of these datasets use either an Apache 2.0 or MIT license.
Thanks to OpenBMB, MetaMath, Hugging Face, Microsoft, theblackcat102, Post-training-Data-Flywheel, and LMSYS for the data!
Here is the extract from the paper with the dataset mixture:

There are two main differences with the dataset described in the paper:
7 commits

Open-PerfectBlend is an open-source reproduction of the instruction dataset introduced in the paper "The Perfect Blend: Redefining RLHF with Mixture of Judges". It's a solid general-purpose instruction dataset with chat, math, code, and instruction-following data.

Here is the list of the datasets used in this mix:
| Dataset | # Samples |
|---|---|
| meta-math/MetaMathQA | 395,000 |
| openbmb/UltraInteract_sft | 288,579 |
| HuggingFaceH4/ultrachat_200k | 207,865 |
| microsoft/orca-math-word-problems-200k | 200,035 |
| HuggingFaceH4/ultrafeedback_binarized | 187,405 |
| theblackcat102/evol-codealpaca-v1 | 111,272 |
| Post-training-Data-Flywheel/AutoIF-instruct-61k | 61,492 |
| mlabonne/lmsys-arena-human-preference-55k-sharegpt | 57,362 |
The deduplication process removed 88.1k samples across all datasets. All of these datasets use either an Apache 2.0 or MIT license.
Thanks to OpenBMB, MetaMath, Hugging Face, Microsoft, theblackcat102, Post-training-Data-Flywheel, and LMSYS for the data!
Here is the extract from the paper with the dataset mixture:

There are two main differences with the dataset described in the paper:
7 commits