LLM Ecosystem: Open-Source Model/Data/Code (since ChatGPT)

생성일

2023/04/13 11:44

최종 편집 일시

2023/05/12 10:07

1. LLM Ecosystem 이란?

2. LLM Ecosystem: Open-Source Model/Data/Code (since ChatGPT)

Table

이름

출시일

모델 크기

Affiliation

Fine-tuning Method

Commercial

새롭게 제공된 Resource

데이터

Note

GPT-Neo

Open

2021-03

125M 350M 1.3B 2.7B

EleutherAI

- the Pile (영어 only; 825GB)

- 학습 및 테스트 코드 : https://github.com/EleutherAI/gpt-neo ◦ DDP 라이브러리 mesh-tensorflow 기반 학습 ◦ DL framework : TF

GPT-J

Open

2021-05

EleutherAI

Model

- the Pile (영어 only; 825GB)

- huggingface, chatbot

GPT-NeoX-20B

Open

2022-04

20B

EleutherAI

Model

- the Pile (영어 only; 825GB)

- paper - 학습 및 테스트 코드 : https://github.com/EleutherAI/gpt-neox/ from scratch, LLM 학습 및 분석 코드 ◦ NVdia Megatron https://github.com/NVIDIA/Megatron-LM & DDP 라이브러리 DeepSpeed 기반 학습 ◦ DL framework : PyTorch (이때부터 TF → pytorch로 옮김)

OPT

Open

2022-05

175B

MetaAI

Model

- RoBERTa Data (BookCorpus, Stories, CCNews v2) - the Pile (CommonCrawl, DM Mathematics, Project Gutenberg, HackerNews, OpenSubtitles, OpenWebText2, USPTO, Wikipedia) - PushShift.io Reddit (only longest chain of thread; 66%) → English text 위주로 추출 (CommonCrawl 내부에 non-English data 조금 있음) → filtered out by MinhashLSH (Jaccard similarity ≥ .95; Pile은 중복 문서가 많음) → GPT-2 BPE tokenizer → final data : 180B tokens

BLOOM

Open

2022-11

300M 580M 1.2B 3.7B 13B 560M 1.1B 1.7B 3B 7.1B 176B

EleutherAI

BigScience

Model

- 데이터 셋 : ROOTS ◦ 46개 자연어 (multi-lingual) : 한국어 없음 ◦ 13개 프로그래밍 언어

- data description, huggingface - code : megatron-LM & DeepSpeed를 수정하여 활용 . PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link) . apex (Github link) - model : Megatron-LM GPT2 architecture . Stable Embedding (Layer norm을 wor embeddings layer에 적용; code, paper) . ALiBI positional encoding (paper) . GeLU activation functions . BPE : The BLOOM tokenizer (link) - A simple pre-tokenization rule, no normalization - training : 2022년 3월 11일 ~ 2022년 7월 5일 (version 1.3) . 384 A100 80GB GPUs (48 nodes) . 32 A100 80GB GPUs (4 nodes) in reserve . 8 GPUs per node using NVLink 4 inter-gpu connects, 4 OmniPath links . GPU Memory : 640GB per node . CPU Memory : 512GB per node . NCCL-communications network . inter-node connect : Omni-Path Architecture (OPA) . Disc IO Network : shared network with other types of nodes . Training throughput : ~150 TFLOP per GPU per second . epoch : 1 epoch (95000 iterations) . total tokens : 366B tokens

BLOOMZ

Open

2022-12

300M (mt5 fine-tuning) 580M (mt5 fine-tuning) 1.2B (mt5 fine-tuning) 3.7B (mt5 fine-tuning) 13B (mt5 fine-tuning) 560M (bloom fine-tuning) 1.1B (bloom fine-tuning) 1.7B (bloom fine-tuning) 3B (bloom fine-tuning) 7.1B (bloom fine-tuning) 176B (bloom fine-tuning)

BigScience

EleutherAI

SFT

Model

- xP3 (multilingual task mixture; cross-lingual & cross-task paper)

- paper, huggingface . pre-training에서 보지 못한 언어에 대해 adapter를 활용한 language adaptation 후, zero-shot 실험 - BLOOMZ : BLOOM의 multi-task fine-tuned version . Adapter architecture : (IA)^3 (inner transformer block activation에 element-wise rescaling 하는 것), continued pretraining, bottleneck adapter, BitFit, LoRA, FishMask 등을 실험 . 결론 : BLOOM + MadX (bottleneck adapter) 조합으로 language adaptation 후 zero-shot 했을 때 성능이 제일 좋음 . 결론 : BLOOMZ (fine-tuned) 는 prompting capability를 잃어버려서, adaptation method가 오히려 성능을 낮춤

Polyglot-Ko

Open

2022-12

1.3B 3.8B 5.8B

EleutherAI

BigScience

Model

- TUNiB Korean Data (863GB, 원본 1.2TB)

- github, , huggingface, blog - Eleuther AI에서 공개한 GPT 구조 언어모델 (Multilingual; 특히 한국어용) - Model . 5.8B : 172B tokens, 320k steps, 256 A100 GPUs with the GPT-NeoX framework.

Pygmalion

Open

2023-01

350M (OPT-350M fine-tuning) 1.3B (OPT-1.3B fine-tuning) 2.7B (ConvoGPT fine-tuning) 6B (ConvoGPT fine-tuning)

PygmalionAI

SFT

Model

- 56MB Dialogue Data (from multiple sources; 일부분은 AI로 생성한 데이터; high-quality roleplaying data)

- docs, github, guideline, video - CharacterAI의 유저들이 모여서 만든 챗봇 용 모델 - 공개된 모델 크기 : 350M ~ 6B . 기반 모델 : OPT & ConvoGPT - 학습 pipeline : ColossalAI . 350M : 데이터의 7%도 사용하지 않고 수렴해버려서, 273 KB size로 학습 (6GB VRAM single GPU) . 1.3B : 11.4M tokens over 5440 steps on a single 24GB GPu, 21 hours . 2.7B : 48.5M tokens over ~5k steps on 4 NVIDIA A40s using DeepSpeed . 6B : 48.5M tokens over ~5k steps on 4 NVIDIA A40s using DeepSpeed - Serving : Gradio UI

LLaMA

Open

2023-02

6.7B (1.0T tokens) 13.0B (1.0T tokens) 32.5B (1.4T tokens) 65.2B (1.4T tokens)

MetaAI

SFT

Model

- English CommonCrawl (67%) : CCNet pipeline quality filtering + filtering model 추가 활용 - C4 (15%) : CCNet pipeline - Github (4.5%) : Apache, BSD, MIT licenses - Wikipedia (4.5%) : bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk - Gutenberg and Books3 (4.5%) - ArXiv (2.5%) : latex files - Stack Exchange (2%) : High quality QA data

- paper - Architecture : Pre-Norm, SwiGLU, Rotary PE (GPTNeo & GPT-J에서 쓰이기 시작하면서 LLM에 활용됨) - Efficient Implementation : attention 계산 (xformer library) & backward (Flashattention for self-att), autograd 대신 backward function 직접 구현 (activations 값 미리 저장), model & sequence parallelism (memory 감소) - Resource for 65.2B : 380 tokens/seg/GPU on 2048 A100 GPU with 80 GB of RAM & 1.4 tokens 학습시 21일 소요

TRLX

Open

2023-02-23

CarperAI

RLHF

Training/Inference Pipeline

github code • NeMo ILQL (link) • T5 ILQL & PPO • LLaMa & Alpaca PPO/SFT support • HF Transformers integration for ILQL • Data integration ◦ AnthropicAI HH

Alpaca

Open

2023-03

Stanford

SFT

Model

InstructData

- Alpaca Instruct Data (link) : 52K instruction-following demonstrations from GPT-3.5 responses (175 self-instruct seed tasks & augmentation)

- blog, github - model input size 512 - supervised learning (RLHF 3단계 중 1단계만 적용한 것) - 학습 비용 : A100 x 8 for 3 hours at the cost of $100, augmented data generation at the cost of $500 - self-instruct 방식으로 데이터 생성 (modified self-instruct) . instruction 생성은 한번에 20개씩 . 평가단계는 생략 (기존에는 task identification을 통해, input-first or output-first w/ task class를 선택함) . response 생성은 1 instruction 당 한개씩

koalpaca

Open

2023-03

5.8B (Polyglot-ko fine-tuning) 7B (LLAMA fine-tuning) 13B (LLAMA LoRA) 30B (LLAMA LoRA) 65B (LLAMA LoRA)

Seoul National Univ.

SFT

Model

InstructData

- Koalpaca Instruct Data . Alpaca data 중 input만 한국어로 번역 . GPT 3.5 (ChatGPT) 로 한국어 답변 생성 (self-instruct 방식) . n=10으로 생성하는 것이 가장 안정적

- github - finetuning base models : LLAMA LoRA (13B, 30B, 65B), Polyglot-ko fine-tuning (5.8B), LLAMA fine-tuning (7B) - Alpaca와 동일한 modified self-instruct

Dolly 1.0

Open

2023-03

6B (GPT-J Fine-tuning)

Databricks

SFT

Model

- alpaca dataset (link) : text-davinci-003으로 생성한 52,000개 instructions and demonstrations dataset

- blog - HuggingFace model card (dolly-v1-6b) : https://huggingface.co/databricks/dolly-v1-6b - Databricks ML Platform & Deepspeed ZeRO 3 - 초기 version : 1 epoch 학습 in 30 mins - dolly-v1-6b : 10 epochs 학습

Vicuna

Open

2023-03

13B (LLaMA fine-tuning)

UC Berkeley

CMU

Stanford

UC San Diego

SFT

Model

InstructData

- ShareGPT : 53k cleaned English data obtained by ChatGPT (from ~100k original data) → 실제 학습에는 70k 데이터 활용했다고 함. . multi-turn . 데이터 전처리 : HTML → Markdown / filter out low-quality samples / divide lengthy conversations into smaller segments that fit the model’s maximum context length

- blog, github, demo, huggingface(GPTQ 4bit), Data & Cleansing & Finetuning . data link : ShareGPT (chatgpt 대화 내용 공유를 extension을 통해 진행; website, data), alpaca instruct data, shareGPT filtered data - Model (학습 코드는 alpaca를 수정해서 진행) . input size 2048 . CPU 60GB RAM, GPU 28GB VRAM 이상에서 작동 (하지만, 3090으로 작동 확인) . 체크포인트는 LLaMA 13B 체크포인트에 delta weight를 더해서 업데이트 하는 방식으로 제공 . 체크포인트는 huggingface transformers로 작동 . Finetuning은 pytorch FSDP & A100 8개로 하루동안 진행, Skypilot(링크)이라는 것을 이용하여 학습 비용은 $1000 -> $300으로 최적화 . gradient checkpointing & flash attention - Tools . 학습 : SkyPilot (managed spot jobs) . Serving : FastChat (gradio) . Evaluation : GPT-4 - comparison : https://vicuna.lmsys.org/eval/ . GPT-4를 이용한 평가에서, ChatGPT ~ Bard > Vicuna > Alpaca > LLaMA

KoChatGPT

Open

2023-03

RLHF

InstructData

- kochatgpt data (link) 1. 아래의 4가지 데이터에서 질문셋 167,577 수집 → 12000개 질문 랜덤 선택 . ChatbotData : 11824 문장 . AI허브_한국어 대화 : 49711 문장 . AI허브_일반상식 : 100268 문장 . KorQuad : 5774 문장 2. ChatGPT를 이용한 답변 생성 3. ChatGPT를 이용하여, 대화 데이터 생성 (ChatGPT가 사람vs.챗봇 대화를 둘 모두 만들도록 함) 4. RM을 위한 Ranking 데이터 : 10220개 . 동일한 prompt에 대해 “ChatGPT, GPT3.5 davinci-003, GPT3 ada-001” 생성 . ChatGPT > GPT3.5-davinci > GPT3-ada 순으로 ranking 자동 레이블링 . RM 모델 학습을 위한 데이터는 이중 두개씩을 묶어서, chosen & rejected 로 만듦 5. PPO dataset : 초기 12000개의 질문만을 활용.

- github, 강의 영상 - ColossalChat 코드를 통한 RLHF 학습 코드 & 한국어 데이터 공개

OIG (Open Instruction Generalist) Dataset

Open

2023-03-10

LAION-AI

InstructData

[OIG dataset] : link ~43M instructions으로 구성되는 대부분 synthetic data로 이루어진 대규모의 데이터 • OIG-small-chip2 (200K) - Done and released. See small_instruction_set sub-directory. • OIG-40M - Done - Done and released. See 40M sub-directory • OIG-100M - In progress, to be released expected April 30, 2023

blog, github, huggingface dataset

ColossalChat (Coati)

Open

2023-03-29

7B (LLaMa RLHF)

HPC-AI tech

RLHF

SFT

Training/Inference Pipeline

InstructData

- InstructWild (link): 52k instructions for English (24M tokens) & 52K instructions for Chinese (30M tokens) . 생성 과정 : 700 noisy instructions 수집 from Twitter & filter out noisy ones → 429 clean instruction (alpaca와 다르게 제한없이 instruction 얻음) → ChatGPT → 5개의 prompts를 예시로 두고 새로운 instruction들을 생성하도록 함 → ChatGPT → 각각의 instruction에 대해 response 수집 . 영어와 중국어 각각 별도로 수집 . 데이터 모으는데 총 $880 소모

- Homepage, blog, ColossalAI Github, ColossalChat github - 거대 GPT 모델 학습을 위한 pipeline 제공 - 10x speedup, 47x cost savings, 175B 이상의 parameters - the first to open-source a complete RLHF pipeline : from training to deployment - 학습/추론 pipeline 제공 : Pytorch 대비 추론 1.4배, 학습 7.7배, 10.3배 큰 모델 학습 가능 (alpaca code 대비 학습 속도 3배 빠름) - automatic parallelism, memory management, dynamic scheduling, “data / pipeline / sequence / tensor parallelism” - chip and cloud agnostic : GPUs, TPUs, FPGAs, CPUs - ColossalAI Talking Intelligence (Coati models) : RLHF Pipeline 제공 • Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms • Supervised datasets collection • Supervised instructions fine-tuning • Training reward model • Reinforcement learning with human feedback • Quantization inference • Fast model deploying • Perfectly integrated with the Hugging Face ecosystem, a high degree of model customization - Limitations of LLaMA-finetuned models and dataset . limitation of LLaMa fine-tuning models : missing knowledge by LLaMa / Lack of counting ability / Lack of Logics (reasoning and calculation) / Tend to repeat the last sentence / poor multilingual results . limitation of InstructWild dataset : Lack of summarization ability / multi-turn chat and role-playing / self-recognition / Safety - Quantization 지원 & Deployment 지원 . 8-bit quantization (RTN), 4-bit quantization (GPTQ), and FP16 inference.

Koala

Open

2023-04-03

13B

UC Berkeley

SFT

InstructData

Model

Training/Inference Pipeline

[ChatGPT DIstillation Data] - ShareGPT : 30k cleaned English data obtained by ChatGPT (from ~60k original data) . multi-turn - HC3 (paper) : 24k questions 에 대해 사라므이 대답 60k & ChatGPT 대답 27k (총 87k QA examples) . single-turn [Open Source Data] - OIG : Open Instruction Generalist , LAION에 의해 만들어진 “garde-school-math-instructions, poetry-to-songs, plot-screenplay-books-dialogue” 30k examples . single-turn - Alpaca dataset (link) . single-turn - Anthropic HH : ~160k Human-rated examples (harmfulness & helpfulness 기준, response pair 중에 더 선호되는 것) - OpenAI WebGPT : ~20K comparisons (a question, a pair of model answers, and metadata, comparision human rates with a preference score) - OpenAI Summarization : ~93K examples . comparison part : 2개의 summaries 중 best를 고르는 것 . axis part : likert scale로 quality 평가하는 것 (overall, accuracy, coverage, coherence, compatible) [test set] - testset (link) : 180 queries (자체 데이터 + Alpaca testset)

- homepage, demo - github code - 웹에서 얻은 대화 데이터를 기반으로 LLaMA를 fine-tuning 한 모델 - JAX/Flax를 활용한 EasyLM을 활용하여, pre-train, fine-tune, serve, evaluate 진행 - Training : 8 A100 GPUs (a single Nvidia DGX) & 6 hours for 2 epochs & <$100 - evaluation : Alpaca보다 좋고, 반정도의 데이터에서 ChatGPT와 비슷한 성능 - result : ChatGPT > Koala ~ Alpaca . testset (link) : 180 queries & human evaluation - comparison data를 학습시에는, “a helpful answer” for positive, “an unhelpful answer” for negative를 conditioning 한 뒤 정답 생성. . human feedback이 없는 데이터는 “a helpful answer”를 붙임. . evaluation 시에도 “a helpful answer” 붙임.

OpenAssistant

Open

2023-04-06

LAION-AI

InstructData

[oasst1] • New Data : human-generated, human-annotated assistant-style conversation corpus 161,443 ◦ 66,497 conversation trees ◦ 35개의 서로 다른 언어 ◦ 461,292 quality ratings ◦ 13,500 volunteers • Ready for export : Spam & Deleted data 제외 (초기 prompt로만 구성 : prompt_lottery_waiting; 낮은 퀄리티 : aborted_low_grade; 중단 : halted_by_moderator) ◦ 10,364 conversation trees ◦ 88,838 messages

paper, code, New Data, blog, website for chat, roadmap, Data Structures, models • 2022년 12월 Project launch • 2023년 4월 6일, Models & Training Data & Code 공개 Other data 참고 • oasst-mix : sft를 위한 추가 데이터 ◦ vicuna (non-commercial) ◦ code_alpaca (non-commercial) ◦ dolly15k (commercial) ◦ grade_school_math_instructions • RM Data : rm 학습을 위한 추가 데이터 ◦ Anthropic HH : ~160k Human-rated examples (harmfulness & helpfulness 기준, response pair 중에 더 선호되는 것) ◦ SHP : ~385K Stanford Human Preferences dataset ◦ hellaswag ◦ webgpt ◦ hf_summary_pairs • RM Others ◦ summarize_from_feedback ◦ synthetic-instruct-gptj-pairwise

Pythia

Open

2023-04-09

70M 160M 410M 1.0B 1.4B 2.8B 6.9B 12B

EleutherAI

SFT

Model

- the Pile (영어 only; 825GB)

- Paper, code - LLM의 학습 패턴 분석 & scaling laws를 조사하기 위한 project . research에서 “memorization / term frequency / gender bias” 등을 분석할 수 있도록 공개하는 것이 목적 - Models . 16개 LLMs (orginal & deduped) 와 154개 checkpoints를 재현가능하도록 공개

Dolly 2.0

Open

2023-04-12

12B (pythia-12b Fine-tuning)

Databricks

SFT

Model

InstructData

- dolly15k : Instruction/response fine-tuning 15k Data for the first instruction-following LLM - 기존 instruction following 모델인 Alpaca, Koala, GPT5All, Vicuna 모두 ChatGPT 결과로부터 만들어진 데이터를 활용했기에 상업적 활용이 불가했음. 상업적 활용이 가능하도록 새롭게 데이터를 만들었고, 이를 통해 학습된 모델 & 코드 & 데이터 공개함.

- blog - HuggingFace model card (dolly-v2-12b) : https://huggingface.co/databricks/dolly-v2-12b - training model : pythia-12b model family - Data description and download . InstructGPT의 가이드를 따라서 7개의 기존 category에 하나(open-ended free form)를 추가하여, 총 8개의 prompt / response pair를 만듦 . 5000명 이상의 Databricks 직원들이 InstructGPT의 방식으로 데이터를 생성함 . Tasks : brainstorming, classification, closed QA, creative writing, information extraction, open QA, and summarization. . Wikipedia 소스를 제외한 나머지 웹 소스는 활용하지 않도록 함 . generative AI의 결과를 이용하지 않도록 함 . 전체 데이터 작업 중에, 반은 질문을 생성하도록 하고, 나머지 반은 본래 질문을 rephrase + response를 만들도록 함. . 별도의 context 필드에는 활용된 reference wikipedia의 내용이 포함되어 있고, 이들은 citation number [42] 가 포함되어 있어서, downstream application에서는 이를 제거할 것을 추천함. - 활용법 : instructions examples을 생성하기 위한 few-shot examples & data augmentation (restate & translate)

Deepspeed Chat

Open

2023-04-12

Microsoft

RLHF

Training/Inference Pipeline

- DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales . Doc, code example - Features . Easy-Breezy Training & Inference API : A complete E2E RLHF training (from Huggingface model + an inference API) . High performance system : Hybrid Engine achieves 15x training speedup over SOTA RLHF systems (memory management & data movement across different phases of RLHF) . Accessible Large Model Support : a single or multi-GPUs through ZeRO and LoRA . An Universal Acceleration Backend for RLHF : Support InstructGPT pipeline (+ data abstraction & blending capabilities for multiple data sources) - Hybrid Engine for Optimization • Inference : a light-weight memory management system (KV-cache & intermediate results) ◦ highly-optimized inference-adapted kernels ◦ tensor parallelism implementation (model partitioning) • Training : memory optimization techniques ◦ ZeRO family (sharding) (model partitioning) & LoRA - Performance . OPT-13B (9 hours; $300 Azure cloud) & OPT-30B (18 hours; $600 Azure cloud) . 13B (1.25 hours; 64 GPU cluster) & 175B (1 day; 64 GPU cluster) [training speed; throughput] . single GPU : 10x for RLHF . multiple GPUs : 6-19x speedup over Colossal-AI & 1.4-10.5x over HuggingFace DDP . example . Actor (OPT-66B) & Reward (OPT-350M) : Step1 (82m) - Step2 (5m) - Step3 (7.5h) . Actor (OPT-1.3B) & Reward (OPT-350M) : Step1 (2900s) - Step2 (670s) - Step (1.2h) [model scalability] . DeepSpeed-HE : 6.5B (a single GPU) & 50B (a single A100 40G node) . Colossal-AI : 1.3B (a single GPU) & 6.7B (a single A100 40G node)

StableLM

Open

2023-04-20

3B 7B

Stability AI

SFT

Model

The Pile Alpaca GPT4All (400k) : GPT-3.5 Turbo로 생성한 437,605개의 (prompts-responses) dataset Anthropic HH : ~160k Human-rated examples (harmfulness & helpfulness 기준, response pair 중에 더 선호되는 것) DataBricks Dolly ShareGPT

code • StableLM-alpha (2023-04-20) ◦ 3B & 7B (이후에 15B & 65B release 예정) ◦ Data The Pile ◦ CC BY-SA-4.0 : Commercial • StableLM-tuned-alpha ◦ 3B & 7B ◦ StableLM-alpha의 추가 SFT version ◦ Data Alpaca (52k) GPT4All (400k) : GPT-3.5 Turbo로 생성한 437,605개의 (prompts-responses) dataset Anthropic HH : ~160k Human-rated examples (harmfulness & helpfulness 기준, response pair 중에 더 선호되는 것) DataBricks Dolly (15k) ShareGPT English Subset ◦ StableLM-alpha →SFT ◦ CC BY-NC-SA-4.0 : Non-commercial

StableVicuna

Open

2023-04-28

13B

Stability AI

RLHF

Model

[Training Data] oasst1 GPT4All (400k) : GPT-3.5 Turbo로 생성한 437,605개의 (prompts-responses) dataset Alpaca [Data for RM] oasst1 Anthropic HH : ~160k Human-rated examples (harmfulness & helpfulness 기준, response pair 중에 더 선호되는 것) SHP : ~385K Stanford Human Preferences dataset (348,718 datasets. 요리에서 철학에 이르는 서로 다른 18개 영역에 대한 questions/instructions datasets)

[Stability AI releases StableVicuna, the AI World’s First Open Source RLHF LLM Chatbot] code • StableVicuna-13B (2023-04-28) ◦ LLaMA-13B →SFT Vicuna-13B v0 →RLHF ◦ Training data oasst1 GPT4All (400k) : GPT-3.5 Turbo로 생성한 437,605개의 (prompts-responses) dataset Alpaca (52k) ◦ Data for RM oasst1 Anthropic HH : ~160k Human-rated examples (harmfulness & helpfulness 기준, response pair 중에 더 선호되는 것) SHP : ~385K Stanford Human Preferences dataset (348,718 datasets. 요리에서 철학에 이르는 서로 다른 18개 영역에 대한 questions/instructions datasets) ◦ CC BY-NC-SA-4.0 : Non-commercial

h2oGPT

Open

2023-05-03

6.9B (Pythia) 12B (Pythia) 20B (GPT-NeoX)

H2O

SFT

Model

oasst1 OIG

code, web demo, blog • code ◦ LoRA & 8-bit quantization 제공 ◦ chatbot server code via GPU & Python client API ◦ evaluation code • model list ◦ oasst1-512-20b : gpt-neox-20b로부터 openassistant data (oasst1-46283 & oasst1-48307)로 SFT 모델 ◦ oasst1-512-12b : pythia-12b로부터 openassistant data (oasst1-46283 & oasst1-48307)로 SFT 모델 ◦ oig-oasst1-512-6.9b : pythia-6.9b로부터 oig & oasst1 data & oasst1-48307로 SFT 모델 ◦ oig-oasst1-256-20b : gpt-neox-20b로부터 oig & oasst1 data 로 SFT 모델 ◦ oig-oasst1-256-12b : pythia-12b-deduped로부터 oig & oasst1 data 로 SFT 모델

3. Training & Inference Pipeline (RLHF)

TRLX (20230223)

ColossalChat (202303)

DeepSpeed-Chat (20230412)

4. Commercially Open-sourced Model/Data/Code

5. Tooling Layer Open-source Codes

•

llama_index

•

Auto-GPT

6. Open-source List (Not yet analyzed; to-be)

•

OpenChatKit : Commercial (Create specialized and general purpose chatbots)

•

Cerebras-GPT (from 111M to 13B) : Commercial (GPT-3 family)

•

Flan-UL2 : Commercial (Google’s T5 family)

•

GeoV (9B) : Commercial (RoPER; Rotary Positional Embeddings with Relative distances)

•

Dalai : Non-commercial (The simplest way to run LLaMA on your local machine)

•

Baize (from 7B to 30B) : Non-commercial (trained with LoRA using 100k dialogs)

•

AgentGPT : AgentGPT

◦

Tooling Layer Code + Agent App

◦

not-commercial : GPL-3.0 license

LLM Ecosystem: Open-Source Model/Data/Code (since ChatGPT)

1. LLM Ecosystem 이란?

2. LLM Ecosystem: Open-Source Model/Data/Code (since ChatGPT)

3. Training & Inference Pipeline (RLHF)

4. Commercially Open-sourced Model/Data/Code

5. Tooling Layer Open-source Codes

6. Open-source List (Not yet analyzed; to-be)

References