llmware
spark-nlp-workshop
| llmware | spark-nlp-workshop | |
|---|---|---|
| 9 | 16 | |
| 3,173 | 1,003 | |
| 6.7% | 1.5% | |
| 9.8 | 9.6 | |
| 6 days ago | 2 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
llmware
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More Agents Is All You Need: LLMs performance scales with the number of agents
I couldn't agree more. You should check out LLMWare's SLIM agents (https://github.com/llmware-ai/llmware/tree/main/examples/SLI...). It's focusing on pretty much exactly this and chaining multiple local LLMs together.
A really good topic that ties in with this is the need for deterministic sampling (I may have the terminology a bit incorrect) depending on what the model is indended for. The LLMWare team did a good 2 part video on this here as well (https://www.youtube.com/watch?v=7oMTGhSKuNY)
I think dedicated miniture LLMs are the way forward.
Disclaimer - Not affiliated with them in any way, just think it's a really cool project.
- FLaNK Stack Weekly 19 Feb 2024
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Show HN: LLMWare – Small Specialized Function Calling 1B LLMs for Multi-Step RAG
I've been building upon the LLMWare project - https://github.com/llmware-ai/llmware - for the past 3 months. The ability to run these models locally on standard consumer CPUs, along with the abstraction provided to chop and change between models and different processes is really cool.
I think these SLIM models are the start of something powerful for automating internal business processes and enhancing the use case of LLMs. Still kinda blows my mind that this is all running on my 3900X and also runs on a bog standard Hetzner server with no GPU.
- Show HN: LLMWare – Integrated Solution for RAG in Finance and Legal
- Llmware.ai – AI Tools for Financial, Legal and Compliance
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Open Source Advent Fun Wraps Up!
16. LLMWare by Ai Bloks | Github | tutorial
- FLaNK Stack Weekly 16 October 2023
- Strategy for PDF data extraction and Display
spark-nlp-workshop
- FLaNK Stack Weekly 19 Feb 2024
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Spark-NLP 4.1.0 Released: Vision Transformer (ViT) is here! The very first Computer Vision pipeline for the state-of-the-art Image Classification task, AWS Graviton/ARM64 support, new EMR & Databricks support, 1000+ state-of-the-art models, and more!
You can visit Spark NLP Workshop for 100+ examples
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Spark-NLP 4.0.0 🚀: New modern extractive Question answering (QA) annotators for ALBERT, BERT, DistilBERT, DeBERTa, RoBERTa, Longformer, and XLM-RoBERTa, official support for Apple silicon M1, support oneDNN to improve CPU up to 97%, improved transformers on GPU up to +700%, 1000+ SOTA models
I submitted a pull request here: https://github.com/JohnSnowLabs/spark-nlp-workshop/pull/552 that I think addresses both of those.
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How AI is used for mental health therapy
In SnowLab’s implementation, for example, they wrote a search function called get_clinical_entities that finds all mentions of medications for 100 patients, as well as specifications, if any, about the quantity and frequency the medication is consumed. The location of the sentence in the overall piece is also recorded, to locate the information easier.
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John Snow Labs Spark-NLP 3.4.0: New OpenAI GPT-2, new ALBERT, XLNet, RoBERTa, XLM-RoBERTa, and Longformer for Sequence Classification, support for Spark 3.2, new distributed Word2Vec, extend support to more Databricks & EMR runtimes, new state-of-the-art transformer models, bug fixes, and lots more!
There are so many examples here for Python users (I would start from tutorials/Certificate_Trainings): https://github.com/JohnSnowLabs/spark-nlp-workshop
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John Snow Labs Spark-NLP 3.1.0: Over 2600+ new models and pipelines in 200+ languages, new DistilBERT, RoBERTa, and XLM-RoBERTa transformers, support for external Transformers, and lots more!
Spark NLP Workshop notebooks
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Release John Snow Labs Spark-NLP 2.7.0: New T5 and MarianMT seq2seq transformers, detect up to 375 languages, word segmentation, over 720+ models and pipelines, support for 192+ languages, and many more! · JohnSnowLabs/spark-nlp
Spark NLP training certification notebooks for Google Colab and Databricks
Spark NLP training certification notebooks for Google Colab and Databricks
Spark NLP training certification notebooks for Google Colab and Databricks
Spark NLP training certification notebooks for Google Colab and Databricks
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