From 1ec5818945670b19e4782070d73cff6937755e6b Mon Sep 17 00:00:00 2001 From: Daniel Smilkov Date: Fri, 26 Jan 2024 09:37:31 -0500 Subject: [PATCH] Update readme.md (#1133) Also render dynamic number of cards based on screen width --- README.md | 119 ++++++++++-------- docs/_static/dataset/dataset_cluster_view.png | Bin 294615 -> 281408 bytes docs/getting_started/installation.md | 38 +++++- .../datasetView/DatasetPivotResult.svelte | 5 +- .../datasetView/DatasetPivotViewer.svelte | 26 ++-- 5 files changed, 122 insertions(+), 66 deletions(-) diff --git a/README.md b/README.md index 52103dd9e..0b864a6af 100644 --- a/README.md +++ b/README.md @@ -23,21 +23,37 @@

-Lilac helps you **curate data** for LLMs, from RAGs to fine-tuning datasets. +Lilac is a tool for exploration, curation and quality control of datasets for training, fine-tuning +and monitoring LLMs. -Lilac runs **on-device** using open-source LLMs with a UI and Python API for: +Lilac is used by companies like [Cohere](https://cohere.com/) and +[Databricks](https://www.databricks.com/) to visualize, quantify and improve the quality of +pre-training and fine-tuning data. -- **Exploring** datasets with natural language (documents) -- **Annotating & structuring** data (e.g. PII detection, profanity, text statistics) -- **Semantic search** to find similar results to a query -- **Conceptual search** to find and tag results that match a fuzzy concept (e.g. low command of - English language) -- **Clustering** data semantically for understanding & deduplication -- **Labeling** and **Bulk Labeling** to curate data +Lilac runs **on-device** using open-source LLMs with a UI and Python API. -### 3 minute walkthrough +## 🆒 New -[![Lilac: 3 minute walkthrough](https://i.ytimg.com/vi/RrcvVC3VYzQ/maxresdefault.jpg)](https://www.youtube.com/watch?v=RrcvVC3VYzQ) +- [Lilac Garden](https://www.lilacml.com/#Garden) is our hosted platform for blazing fast + dataset-level computations. [Sign up](https://forms.gle/Gz9cpeKJccNar5Lq8) to join the pilot. +- Cluster & title millions of documents with the power of LLMs. + [Explore and search](https://lilacai-lilac.hf.space/datasets#lilac/OpenOrca&query=%7B%7D&viewPivot=true&pivot=%7B%22outerPath%22%3A%5B%22question__cluster%22%2C%22category_title%22%5D%2C%22innerPath%22%3A%5B%22question__cluster%22%2C%22cluster_title%22%5D%7D) + over 36,000 clusters of 4.3M documents in OpenOrca + +## Why use Lilac? + +- Explore your data interactively with LLM-powered search, filter, clustering and annotation. +- Curate AI data, applying best practices like removing duplicates, PII and obscure content to + reduce dataset size and lower training cost and time. +- Inspect and collaborate with your team on a single, centralized dataset to improve data quality. +- Understand how data changes over time. + +Lilac can offload expensive computations to [Lilac Garden](https://www.lilacml.com/#Garden), our +hosted platform for blazing fast dataset-level computations. + +image + +> See our [3min walkthrough video](https://www.youtube.com/watch?v=RrcvVC3VYzQ) ## 🔥 Getting started @@ -47,13 +63,16 @@ Lilac runs **on-device** using open-source LLMs with a UI and Python API for: pip install lilac[all] ``` -If you prefer no local installation, you can duplicate the -[HuggingFace Spaces demo](https://lilacai-lilac.hf.space/). Documentation +If you prefer no local installation, you can duplicate our +[Spaces demo](https://lilacai-lilac.hf.space/) by following documentation [here](https://docs.lilacml.com/deployment/huggingface_spaces.html). +For more detailed instructions, see our +[installation guide](https://docs.lilacml.com/getting_started/installation.html). + ### 🌐 Start a webserver -Start a Lilac webserver from the CLI: +Start a Lilac webserver with our `lilac` CLI: ```sh lilac start ~/my_project @@ -70,34 +89,19 @@ ll.start_server(project_dir='~/my_project') This will open start a webserver at http://localhost:5432/ where you can now load datasets and explore them. -### Run via Docker +### Lilac Garden -We publish images for `linux/amd64` and `linux/arm64` on Docker Hub under -[lilacai](https://hub.docker.com/u/lilacai). +Lilac Garden is our hosted platform for running dataset-level computations. We utilize powerful GPUs +to accelerate expensive signals like Clustering, Embedding, and PII. +[Sign up](https://forms.gle/Gz9cpeKJccNar5Lq8) to join the pilot. -The container runs on the virtual port `80`, this command maps it to the host machine port `5432`. - -If you have an existing lilac project, mount it and set the `LILAC_PROJECT_DIR` environment -variable: - -```sh -docker run -it \ - -p 5432:80 \ - --volume /host/path/to/data:/data \ - -e LILAC_PROJECT_DIR="/data" \ - --gpus all \ # Remove if you don't have a GPU, or on MacOS. - lilacai/lilac -``` - -To build your own custom image run the following command, otherwise skip to the next step. - -```sh -docker build -t lilac . -``` +- Cluster and title **a million** data points in **20 mins** +- Embed your dataset at **half a billion** tokens per min +- Run your own signal ### 📊 Load data -Datasets can be loaded directly from HuggingFace, CSV, JSON, +Datasets can be loaded directly from HuggingFace, Parquet, CSV, JSON, [LangSmith from LangChain](https://www.langchain.com/langsmith), SQLite, [LLamaHub](https://llamahub.ai/), Pandas, Parquet, and more. More documentation [here](https://docs.lilacml.com/datasets/dataset_load.html). @@ -106,13 +110,7 @@ Datasets can be loaded directly from HuggingFace, CSV, JSON, import lilac as ll ll.set_project_dir('~/my_project') - -config = ll.DatasetConfig( - namespace='local', - name='imdb', - source=ll.HuggingFaceSource(dataset_name='imdb')) - -dataset = ll.create_dataset(config) +dataset = ll.from_huggingface('imdb') ``` If you prefer, you can load datasets directly from the UI without writing any Python: @@ -121,26 +119,41 @@ If you prefer, you can load datasets directly from the UI without writing any Py ### 🔎 Explore -> [🔗 Try OpenOrca before installing!](https://lilacai-lilac.hf.space/datasets#lilac/OpenOrca) + +> [!NOTE] +> 🔗 Explore [OpenOrca](https://lilacai-lilac.hf.space/datasets#lilac/OpenOrca) and +> [its clusters](https://lilacai-lilac.hf.space/datasets#lilac/OpenOrca&query=%7B%7D&viewPivot=true&pivot=%7B%22outerPath%22%3A%5B%22question__cluster%22%2C%22category_title%22%5D%2C%22innerPath%22%3A%5B%22question__cluster%22%2C%22cluster_title%22%5D%7D) +> before installing! Once we've loaded a dataset, we can explore it from the UI and get a sense for what's in the data. More documentation [here](https://docs.lilacml.com/datasets/dataset_explore.html). image -### ⚡ Annotate with Signals (PII, Text Statistics, Language Detection, Neardup, etc) +### ✨ Clustering -Annotating data with signals will produce another column in your data. +Cluster any text column to get automated dataset insights: ```python -import lilac as ll +dataset = ll.get_dataset('local', 'imdb') +dataset.cluster('text') # add `use_garden=True` to offload to Lilac Garden +``` -ll.set_project_dir('~/my_project') + +> [!TIP] +> Clustering on device can be slow or impractical, especially on machines without a powerful GPU or +> large memory. Offloading the compute to [Lilac Garden](https://www.lilacml.com/#Garden), our +hosted data processing platform, can speedup clustering by more than 100x. -dataset = ll.get_dataset('local', 'imdb') +image + +### ⚡ Annotate with Signals (PII, Text Statistics, Language Detection, Neardup, etc) -# [Language detection] Detect the language of each document. -dataset.compute_signal(ll.LangDetectionSignal(), 'text') +Annotating data with signals will produce another column in your data. + +```python +dataset = ll.get_dataset('local', 'imdb') +dataset.compute_signal(ll.LangDetectionSignal(), 'text') # Detect language of each doc. # [PII] Find emails, phone numbers, ip addresses, and secrets. dataset.compute_signal(ll.PIISignal(), 'text') @@ -157,7 +170,7 @@ print(dataset.manifest()) We can also compute signals from the UI: -image +image ### 🔎 Search diff --git a/docs/_static/dataset/dataset_cluster_view.png b/docs/_static/dataset/dataset_cluster_view.png index 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{@const innerGroup = castToInnerPivot(item)} {@const groupPercentage = getPercentage(innerGroup.count, group.count)} diff --git a/web/blueprint/src/lib/components/datasetView/DatasetPivotViewer.svelte b/web/blueprint/src/lib/components/datasetView/DatasetPivotViewer.svelte index f466493bc..9bb15c4fa 100644 --- a/web/blueprint/src/lib/components/datasetView/DatasetPivotViewer.svelte +++ b/web/blueprint/src/lib/components/datasetView/DatasetPivotViewer.svelte @@ -162,6 +162,13 @@ inputSearchText = undefined; search(); } + + const WIDTH_PER_ITEM_PX = 256; + const DEFAULT_ITEMS_PER_PAGE = 4; + let carouselWidth: number | undefined = undefined; + $: itemsPerPage = carouselWidth + ? Math.round(carouselWidth / WIDTH_PER_ITEM_PX) + : DEFAULT_ITEMS_PER_PAGE;
@@ -281,14 +288,17 @@ >
{#if outerLeafPath && innerLeafPath && numRowsInQuery} - +
+ +
{/if}
{:else}