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Update distilabel phrasing based on PR hugging face hub #821

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1490d9e
Update distilabel phrasing based on PR hugging face hub
davidberenstein1957 Jul 24, 2024
c2d7d99
Update README.md
davidberenstein1957 Jul 24, 2024
c7b1a82
Update index.md
davidberenstein1957 Jul 24, 2024
1bfbfac
Fix typos
davidberenstein1957 Jul 24, 2024
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16 changes: 8 additions & 8 deletions README.md
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Expand Up @@ -29,18 +29,18 @@
</p>


Distilabel is the **framework for synthetic data and AI feedback for AI engineers** that require **high-quality outputs, full data ownership, and overall efficiency**.
Distilabel is the framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

If you just want to get started, we recommend you check the [documentation](http://distilabel.argilla.io/). Curious, and want to know more? Keep reading!
<!-- ![overview](https://github.com/argilla-io/distilabel/assets/36760800/360110da-809d-4e24-a29b-1a1a8bc4f9b7) -->

## Why use Distilabel?
## Why use distilabel?

Whether you are working on **a predictive model** that computes semantic similarity or the next **generative model** that is going to beat the LLM benchmarks. Our framework ensures that the **hard data work pays off**. Distilabel is the missing piece that helps you **synthesize data** and provide **AI feedback**.
Distilabel can be used for generating synthetic data and AI feedback for a wide variety of projects including traditional predictive NLP (classification, extraction, etc.), or generative and large language model scenarios (instruction following, dialogue generation, judging etc.). Distilabel's programmatic approach allows you to build scalable pipelines data generation and AI feedback. The goal of distilabel is to accelerate your AI development by quickly generating high-quality, diverse datasets based on verified research methodologies for generating and judging with AI feedback.

### Improve your AI output quality through data quality

Compute is expensive and output quality is important. We help you **focus on data quality**, which tackles the root cause of both of these problems at once. Distilabel helps you to synthesize and judge data to let you spend your valuable time on **achieveing and keeping high-quality standards for your data**.
Compute is expensive and output quality is important. We help you **focus on data quality**, which tackles the root cause of both of these problems at once. Distilabel helps you to synthesize and judge data to let you spend your valuable time **achieving and keeping high-quality standards for your data**.

### Take control of your data and models

Expand All @@ -62,11 +62,11 @@ We are an open-source community-driven project and we love to hear from you. Her

## What do people build with Distilabel?

Distilabel is a tool that can be used to **synthesize data and provide AI feedback**. Our community uses Distilabel to create amazing [datasets](https://huggingface.co/datasets?other=distilabel) and [models](https://huggingface.co/models?other=distilabel), and **we love contributions to open-source** ourselves too.
The Argilla community uses distilabel to create amazing [datasets](https://huggingface.co/datasets?other=distilabel) and [models](https://huggingface.co/models?other=distilabel), and Argilla contributed to open-source too.
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not sure if I understand the phrasing


- The [1M OpenHermesPreference](https://huggingface.co/datasets/argilla/OpenHermesPreferences) is a dataset of ~1 million AI preferences derived from teknium/OpenHermes-2.5. It shows how we can use Distilabel to **synthesize data on an immense scale**.
- Our [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) and the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B),, show how we **improve model performance by filtering out 50%** of the original dataset through **AI feedback**.
- The [haiku DPO data](https://github.com/davanstrien/haiku-dpo) outlines how anyone can create a **dataset for a specific task** and **the latest research papers** to improve the quality of the dataset.
- The [1M OpenHermesPreference](https://huggingface.co/datasets/argilla/OpenHermesPreferences) is a dataset of ~1 million AI preferences that have been generated using the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) LLM. It is a great example on how you can use distilabel to scale and increase dataset development.
- [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) used to fine tune the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B). This dataset was built combining Argilla human curation with distilabel, leading to an improved version of the Intel Orca dataset and outperforming models fine tuned on the original dataset.
- The [haiku DPO data](https://github.com/davanstrien/haiku-dpo) is an example how anyone can create a synthetic dataset for a specific task, which after curation and evaluation can be used for fine-tuning custom LLMs.

## Installation

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20 changes: 10 additions & 10 deletions docs/index.md
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</a>
</p>

Distilabel is the **framework for synthetic data and AI feedback for AI engineers** that require **high-quality outputs, full data ownership, and overall efficiency**.
Distilabel is the framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

If you just want to get started, we recommend you check the [documentation](http://distilabel.argilla.io/). Curious, and want to know more? Keep reading!

## Why use Distilabel?
## Why use distilabel?

Whether you are working on **a predictive model** that computes semantic similarity or the next **generative model** that is going to beat the LLM benchmarks. Our framework ensures that the **hard data work pays off**. Distilabel is the missing piece that helps you **synthesize data** and provide **AI feedback**.
Distilabel can be used for generating synthetic data and AI feedback for a wide variety of projects including traditional predictive NLP (classification, extraction, etc.), or generative and large language model scenarios (instruction following, dialogue generation, judging etc.). Distilabel's programmatic approach allows you to build scalable pipelines data generation and AI feedback. The goal of distilabel is to accelerate your AI development by quickly generating high-quality, diverse datasets based on verified research methodologies for generating and judging with AI feedback.

### Improve your AI output quality through data quality

Compute is expensive and output quality is important. We help you **focus on data quality**, which tackles the root cause of both of these problems at once. Distilabel helps you to synthesize and judge data to let you spend your valuable time on **achieveing and keeping high-quality standards for your data**.
Compute is expensive and output quality is important. We help you **focus on data quality**, which tackles the root cause of both of these problems at once. Distilabel helps you to synthesize and judge data to let you spend your valuable time **achieving and keeping high-quality standards for your synthetic data**.

### Take control of your data and models

**Ownership of data for fine-tuning your own LLMs** is not easy but Distilabel can help you to get started. We integrate **AI feedback from any LLM provider out there** using one unified API.
**Ownership of data for fine-tuning your own LLMs** is not easy but distilabel can help you to get started. We integrate **AI feedback from any LLM provider out there** using one unified API.

### Improve efficiency by quickly iterating on the right research and LLMs

Synthesize and judge data with **latest research papers** while ensuring **flexibility, scalability and fault tolerance**. So you can focus on improving your data and training your models.

## What do people build with Distilabel?
## What do people build with distilabel?

Distilabel is a tool that can be used to **synthesize data and provide AI feedback**. Our community uses Distilabel to create amazing [datasets](https://huggingface.co/datasets?other=distilabel) and [models](https://huggingface.co/models?other=distilabel), and **we love contributions to open-source** ourselves too.
The Argilla community uses distilabel to create amazing [datasets](https://huggingface.co/datasets?other=distilabel) and [models](https://huggingface.co/models?other=distilabel), and Argilla contributed to open-source too.

- The [1M OpenHermesPreference](https://huggingface.co/datasets/argilla/OpenHermesPreferences) is a dataset of ~1 million AI preferences derived from teknium/OpenHermes-2.5. It shows how we can use Distilabel to **synthesize data on an immense scale**.
- Our [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) and the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B),, show how we **improve model performance by filtering out 50%** of the original dataset through **AI feedback**.
- The [haiku DPO data](https://github.com/davanstrien/haiku-dpo) outlines how anyone can create a **dataset for a specific task** and **the latest research papers** to improve the quality of the dataset.
- The [1M OpenHermesPreference](https://huggingface.co/datasets/argilla/OpenHermesPreferences) is a dataset of ~1 million AI preferences that have been generated using the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) LLM. It is a great example on how you can use distilabel to scale and increase dataset development.
- [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) used to fine tune the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B). This dataset was built combining Argilla human curation with distilabel, leading to an improved version of the Intel Orca dataset and outperforming models fine tuned on the original dataset.
- The [haiku DPO data](https://github.com/davanstrien/haiku-dpo) is an example how anyone can create a synthetic dataset for a specific task, which after curation and evaluation can be used for fine-tuning custom LLMs.
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