dvclive
haystack
| dvclive | haystack | |
|---|---|---|
| 5 | 55 | |
| 153 | 13,883 | |
| 2.0% | 4.3% | |
| 8.9 | 9.9 | |
| 9 days ago | 6 days ago | |
| Python | Python | |
| 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.
dvclive
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
- Log and track ML metrics, parameters, models with Git and DVC
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[P] Extension for VS Code to track ML experiments
There is no designated way to dump metrics. In the case of data for plots, we have a simple logger that might help: https://github.com/iterative/dvclive
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Show HN: DVC Studio – Git-Based ML Experiments Management
DVC has metrics logger similar to other experiment management tool: https://github.com/iterative/dvclive/
Also, metrics & params section of the docs explains this (but yes, it is not perfect yet): https://dvc.org/doc/start/metrics-parameters-plots
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
phoenix - AI Observability & Evaluation
langchain - 🦜🔗 Build context-aware reasoning applications
pytest-visual - A visual testing framework for ML with automated change detection
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
dvc - 🦉 ML Experiments and Data Management with Git
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
OpenLLM - Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.
jina - ☁️ Build multimodal AI applications with cloud-native stack
BERT-pytorch - Google AI 2018 BERT pytorch implementation