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A collection of AWESOME things about mixture-of-experts
Reaching LLaMA2 Performance with 0.1M Dollars
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method ; GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model
An incremental parsing system for programming tools
Notebooks for the O'Reilly book "Learning Ray"
Agentic components of the Llama Stack APIs
A repository for research on medium sized language models.
Faker is a Python package that generates fake data for you.
Scalable toolkit for efficient model alignment
DataComp: In search of the next generation of multimodal datasets
Provides end-to-end model development pipelines for LLMs and Multimodal models that can be launched on-prem or cloud-native.
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
Easily embed, cluster and semantically label text datasets
Finetune Llama 3.1, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory
SimPO: Simple Preference Optimization with a Reference-Free Reward
Code examples and resources for DBRX, a large language model developed by Databricks
Official inference library for Mistral models
⛷️ LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
Manage scalable open LLM inference endpoints in Slurm clusters
A massively parallel, high-level programming language
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supportin…
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
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