Discover amazing ML apps made by the community
MTEB Leaderboard - a Hugging Face Space by mteb
Discover amazing ML apps made by the community
Quanto: a pytorch quantization toolkit
Quantization is a technique to reduce the computational and memory costs of evaluating Deep Learning Models by representing their weights and activations with low-precision data types like 8-bit integer (int8) instead of the usual 32-bit floating point (float32). Reducing the number of bits means the resulting model requires less memory storage, which is crucial for deploying Large Language Models
Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models
We find less than 4 contaminated samples for MMLU, OpenBookQA and WinoGrande. Training stack We trained a 1B LLM using Llama2 architecure on Cosmopedia to assess its quality: https://huggingface.co/HuggingFaceTB/cosmo-1b. We used datatrove library for data deduplication and tokenization, nanotron for model training, and lighteval for evaluation. The model performs better than TinyLlama 1.1B on ARC
🪆 Introduction to Matryoshka Embedding Models
In this blogpost, we will introduce you to the concept of Matryoshka Embeddings and explain why they are useful. We will discuss how these models are theoretically trained and how you can train them using Sentence Transformers. Additionally, we will provide practical guidance on how to use Matryoshka Embedding models and share a comparison between a Matryoshka embedding model and a regular embeddi
What's going on with the Open LLM Leaderboard?
Recently an interesting discussion arose on Twitter following the release of Falcon 🦅 and its addition to the Open LLM Leaderboard, a public leaderboard comparing open access large language models. The discussion centered around one of the four evaluations displayed on the leaderboard: a benchmark for measuring Massive Multitask Language Understanding (shortname: MMLU). The community was surprise
Zero-shot image-to-text generation with BLIP-2
This guide introduces BLIP-2 from Salesforce Research that enables a suite of state-of-the-art visual-language models that are now available in 🤗 Transformers. We'll show you how to use it for image captioning, prompted image captioning, visual question-answering, and chat-based prompting. Table of contents Introduction What's under the hood in BLIP-2? Using BLIP-2 with Hugging Face Transformers
Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate
To reproduce the benchmark results simply add --benchmark to any of these 3 scripts discussed below. Solutions First checkout the demo repository: git clone https://github.com/huggingface/transformers-bloom-inference cd transformers-bloom-inference In this article we are going to use 3 scripts located under bloom-inference-scripts/. The framework-specific solutions are presented in an alphabetical
A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes
The 3 models are BLOOM-176B, T5-11B and T5-3B. Hugging Face transformers integration nuances Next let's discuss the specifics of the Hugging Face transformers integration. Let's look at the usage and the common culprit you may encounter while trying to set things up. Usage The module responsible for the whole magic described in this blog post is called Linear8bitLt and you can easily import it fro
The Technology Behind BLOOM Training
Please note that both Megatron-LM and DeepSpeed have Pipeline Parallelism and BF16 Optimizer implementations, but we used the ones from DeepSpeed as they are integrated with ZeRO. Megatron-DeepSpeed implements 3D Parallelism to allow huge models to train in a very efficient way. Let’s briefly discuss the 3D components. DataParallel (DP) - the same setup is replicated multiple times, and each being
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amazon/chronos-t5-base · Hugging Face
Model Cards
@DmitryRyumin on Hugging Face: "🌟✨ Exciting Announcement: NVIDIA AI Foundation Models ✨🌟 🚀 Interact…"
Patch Time Series Transformer in Hugging Face
Paper page - ControlLLM: Augment Language Models with Tools by Searching on Graphs
Deploy LLMs with Hugging Face Inference Endpoints
Paper page - Benchmarking Large Language Model Capabilities for Conditional Generation
stabilityai (Stability AI)