Projcect Seshu

SLMs by the public, for the public

Introduction

chatGPT caught the public imagination and for the first time non-tech people could experience Generative AI. This led to a surge in interest to develop safe applications of LLMs as well as developing domain specific or open source alternatives to chatGPT. Notable among them is LLaMA 2 - an LLM open sourced by Meta. This release catalyzed the development of tasks, tools, and assets ranging from datasets to new models to new applictions. An SLM called Phi 2 released by Microsoft also showed that small models (reletively that is) can also compete with large models, which can be trained and served at substantially lower costs. However, there are some challenges.

Majority, if not all the LLMs, we see today are based on proven Transformer based architectures. And Transfomres have quadratic (in inputs tokens) complexity - therefore slow to train and infer. As a result, new memory and compute efficient attention mechanisms have sprungup, along with Engineering hacks. But, at the end of the day, they are still based on Transformer-based architectures.

Further, majority, with the exception of some Chinese LLMs, are English-centric and other languages have a token representation (no pun intended). Often, LLMs have a particulalr tokenizer -- which makes extension to other languages/ domains hard. Vocabulary size and Transfomers Computational Efficiency have an uneasy relationship. Developing SLMs or LLMs is still a compute heavy problem. Therefore, only large consortia with deep pockets, massive talent concentration and GPU farms can afford to build such models.

In this hackathon, we like to address the above challenges.

Proposal:

Objectives

• Develop a multilingual Mambaa(S4)-based SLM on multi-lingual indic dataset
• Decentralise the training and development of SLMs/LLMs via simple federated learning framework w.r.t cost, talent and compute.
• Provide an experimental platform to develop the skills to develop SLMs/LLMs, particulalry for the student community, and foster research & innovation

User Stories

Client (donor user)

Pre-reqs

• has GPU, registers on HuggingFace/mlsquare for write access
• familair with HuggingFace ecosystem (transfomers, peft, datasets, hub)
• [optional] can donate time or data or both

Actions:

Runs client side sscript which

• downloads data, pretrains model
• SFTs via LoRA
• pushes the adapter to HuggingFace model hub

Server (who manages the federated learning)

Pre-reqs

• has (big) GPU(s)
• is familair with HuggingFace ecosystem (transfomers, peft, datasets, hub), databases, ML Enginneering in general
• [optional] can donate time or data or both

Actions:

• Pretrains a multi-lingual Mamba model, publushes a checkpoint
• Evaluated the community contributed adapters in a single-blind fashion, and merges them into the pretrained model
• Does continous pretrainning, and releases checkpoints periodically

Academic Interests

• experiment and identity good federating learning policies
• figure out effective training configurations to PT, CPT, SFT, FedT SLMs and LLMs
• develop new task specific adapters
• contribute your local, vernacular data
• curate datasets

Roadmap

Week 0

• Make Mamba compatiable with Transformer class
• Test LoRA adapters (adding, training, merging)
• Pretrain an SLM, SFT on LoRA, Merge, Push Outcome: A working end-to-end Pretraining and SFT-ing pipeline

Week 1

• Develop client-side code
• On multi-lingual indic dataset such as samantar, pretrain a model
• Release a checkpoint

Week 2

• Drive SFT via community (at least two users)
• Run Federated SFT-ing

Week 4 and onwards

• Benchmark and eval on test set (against other OSS LLMS)
• Perplexity vs Epochs (and how Seshu is maturing)

References:

Architectures and Tokenizers

• Mamba: Linear-Time Sequence Modeling with Selective State Spaces, paper, 1st, Dec, 2023
• MambaByte: Token-free Selective State Space Model paper, 24th, Jan, 2024
• BlackMamba - Mixture of Experts of State Space Models paper, code
• ByT5: Towards a token-free future with pre-trained byte-to-byte models paper, 28th, May, 2023

Indic LLMs

• RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models models via Romanization paper, 25th, Jan, 2024
• Open Hathi - blog from sarvam.ai, 12th Dec, 2023
• MaLA-500: Massive Language Adaptation of Large Language Models paper

Model Merging

• Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time paper
• Editing Models with Task Arithmetic paper
• TIES-Merging: Resolving Interference When Merging Models paper
• Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch paper
• Rethinking Alignment via In-Context Learning (implments token distribution shift) blog
• PhatGoose: The Challenge of Recycling PEFT Modules for Zero-Shot Generalization blog
• LoRA Hub: Efficient Cross-Task Generalization via Dynamic LoRA Composition (https://arxiv.org/abs/2307.13269)

Datasets

• samantar here

Code & Tools for Models, and Distributed Training

• Mamba-HF: Mamba model compatible with HuggingFace transformers here
• Mamba pretrained model collection here
• Mamba-minimal: a minimal implementaiton of Mamba architecture here
• Mamba: original implementation by Mama authors here
• OLMo: A truly open LLM blog
• Petals: decentralized inference and finetuning of large language models blog, paper, git repo
• position blog on Petals: a shift in training LLMs with Petals network techcrunch blog
• Shepherd: A Platform Supporting Federated Instruction Tuning here
• FATE-LM is a framework to support federated learning for large language models(LLMs) here
• FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale here
• mergekit for model merign to implement multiple model merging techiques here

Note:

The views expressed or approach being taken - is of the individuals, and they do not represent any organization explicitly or implicitly. Likewise, anyone who wants to contribute their time, compute or data must understand that, this is a community experiment to develop LLMs by the community, and may not result in any significant outcome. On the contrary, this may end up in total failure. The contributors must take this risk on their own.