LLM-Stack
Marker
Marker converts PDF, EPUB, and MOBI to markdown. It's 10x faster than nougat, more accurate on most documents, and has low hallucination risk.
Marker converts PDF, EPUB, and MOBI to markdown. It's 10x faster than nougat, more accurate on most documents, and has low hallucination risk.
- Support for a range of PDF documents (optimized for books and scientific papers)
- Removes headers/footers/other artifacts
- Converts most equations to latex
- Formats code blocks and tables
- Support for multiple
VikParuchuri • GitHub - VikParuchuri/marker: Convert PDF to markdown quickly with high accuracy
Data Extraction Stack
Higher performance and lower cost than any single LLM
We invented the first LLM router. By dynamically routing between multiple models, Martian can beat GPT-4 on performance, reduce costs by 20%-97%, and simplify the process of using AI
We invented the first LLM router. By dynamically routing between multiple models, Martian can beat GPT-4 on performance, reduce costs by 20%-97%, and simplify the process of using AI
Martian's Model Router: Optimize AI Performance and Reduce Costs
Introducing Prompts: LLM Monitoring
W&B Prompts – LLM Monitoring provides large language model usage monitoring and diagnostics. Start simply, then customize and evolve your monitoring analytics over time.
W&B Prompts – LLM Monitoring provides large language model usage monitoring and diagnostics. Start simply, then customize and evolve your monitoring analytics over time.
Monitoring
Monitoring Tools
ANY
LLM of your choice, statistical methods, or NLP models that runs
locally on your machine
:
- G-Eval
- Summarization
- Answer Relevancy
- Faithfulness
- Contextual Recall
- Contextual Precision
- RAGAS
- Hallucination
- Toxicity
- Bias
- etc.
GitHub - confident-ai/deepeval: The LLM Evaluation Framework
dstack is an open-source toolkit and orchestration engine for running GPU workloads. It's designed for development, training, and deployment of gen AI models on any cloud.
Supported providers: AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, and DataCrunch.
Latest news ✨
Supported providers: AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, and DataCrunch.
Latest news ✨
- [2024/01] dstack 0.14.0: OpenAI-compatible endpoints preview (Release)
- [2023/12] dst
dstackai • GitHub - dstackai/dstack: dstack is an open-source toolkit for running GPU workloads on any cloud. It works seamlessly with any cloud GPU providers. Discord: https://discord.gg/u8SmfwPpMd
They will start to support autoscaling in March. You can configure multiple clouds and they deploy to the cheapest one.
LanceDB
LanceDB is an open-source vector database for AI that's designed to store, manage, query and retrieve embeddings on large-scale multi-modal data. The core of LanceDB is written in Rust 🦀 and is built on top of Lance, an open-source columnar data format designed for performant ML workloads and fast random access.
Both the database and the un... See more
LanceDB is an open-source vector database for AI that's designed to store, manage, query and retrieve embeddings on large-scale multi-modal data. The core of LanceDB is written in Rust 🦀 and is built on top of Lance, an open-source columnar data format designed for performant ML workloads and fast random access.
Both the database and the un... See more
LanceDB - LanceDB
SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution.
SkyPilot abstracts away cloud infra burdens :
SkyPilot... See more
SkyPilot abstracts away cloud infra burdens :
- Launch jobs & clusters on any cloud
- Easy scale-out: queue and run many jobs, automatically managed
- Easy access to object stores (S3, GCS, R2)
SkyPilot... See more
skypilot-org • GitHub - skypilot-org/skypilot: SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
Boosting annotator efficiency with Large Language Models
In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:
Prodigy's... See more
In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:
- NER: ner.llm.correct, ner.llm.fetch
- Spancat: spans.llm.correct, spans.llm.fetch
- Textcat: textcat.llm.correct, textcat.llm.fetch
- Prompt engineering and terms: ab.llm.tournament terms.llm.fetch
Prodigy's... See more
Prodigy in 2023: LLMs, task routers, QA and plugins · Explosion
Easy data labelling for NLP tasks.
🐂 What is Oxen?
Oxen is a lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
The interface mirrors git, but shines in many areas that git or git-lfs fall short. Oxen is built from the ground up for data, and is optimized to handle large... See more
Oxen is a lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
The interface mirrors git, but shines in many areas that git or git-lfs fall short. Oxen is built from the ground up for data, and is optimized to handle large... See more
Oxen-AI • GitHub - Oxen-AI/oxen-release: Official repository for docs and releases of the Oxen CLI
Data Structuring & Storage