LLM360 Research Suite

LLM360 Research Suite is a comprehensive set of large language model (LLM) artifacts from each of our models, for academic and industry researchers to explore LLM training dynamics.

K2 Spikes

We encountered two major loss spikes while training K2:

  • The first loss spike occurred after 160 checkpoints and lasted over ~34 checkpoints. We restarted training at checkpoint 160 and training returned to normal.
  • The second loss spike occurred after restarting training to fix the first loss spike at checkpoint 186 and lasted from ~8 checkpoints. Again, we restarted training at checkpoint 186 and training returned to normal.
We are releasing these checkpoints so others can study this interesting phenomena in large model training.

Analysis360: Analyze LLMs in 360 degrees

Analysis360 serves as the single source of truth for all evaluation metrics and provides in-depth analysis from many different angles.

Summary of notable open-source LLMs

We note a trend of progressively less disclosure of important pretraining details over time: (1) availability of pretraining code, (2) disclosure of training configurations and hyperparameters, (3) intermediate checkpoints of model weights, (4) intermediate checkpoints of optimizer states, (5) disclosure of data mixture and sources, (6) reproducibility of pretraining data sequence, and (7) availability (or reconstruction scripts) of the pretraining data.

LLM Name Release Date Pretraining Checkpoints Pretraining Dataset Tokens
Name Date Code Config Model Optim Data Mix Ordering Available (T )
K2 May’24 1.4
OLMo-7B May’24 2.5
Arctic Apr’24 1.5
CrystalCoder Dec’23 1.4
Amber Dec’23 1.3
Yi Nov’23 ?
Mistral Sep’23 ?
Qwen Aug’23 2.4
Llama 2 Jul’23 2.0
Falcon May’23 1.5
MPT May’23 1.0
INCITE May’23 1.0
OpenLLama May’23 1.0
LLAMA Feb’23 1.0
Pythia Feb’23 0.30
BLOOM Nov’22 0.34
OPT May’22 0.18
GPT-NeoX Apr’22 0.40
GPT-J May’21 0.40

Our Approach

We run evaluations on a variety of benchmarks, including the conventional benchmarks like MMLU, Hellaswag, ARC, user-preference aligned benchmarks like MT-bench, long-context evaluations like LongEval, and additional studies on safety benchmarks for truthfulness, toxicity, and bias. Moreover, we report results on the model samples we preselected from a suite of LLMs where they all trained on same data seen in the exact same order to better observe and understand how our models develop and evolve over the training process. We also provide public access to all checkpoints, all code and all wandb dashboards for detailed training and evaluation curves.

List of Analysis and Metrics

Here's a full list of analysis/metrics we have collected so far. For each model we release, at this point, Amber, CrystalCoder, and K2, we put down the links to specific wandb reports if the evaluation is done. Amber, CrystalCoder, and K2 currently use their own evaluation scripts, we are working on consolidating these in the future, more details can be found in later sections. Please refer to model cards (Amber, CrystalCoder, and K2) for any terms or technology you find unfamiliar. We will keep updating and expanding the list as our study proceeds, please stay tuned on the upcoming changes!

Metrics/Analysis Description Amber CrystalCoder
mmlu A test to measure a text model’s multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more 5 shot 0 shot
5 shot
race A test to measure reading comprehension ability 0 shot 0 shot
arc_challenge A set of grade-school science questions 25 shot 0 shot
25 shot
boolq A question answering dataset for yes/no questions containing 15942 examples 0 shot 0 shot
hellaswag A test of commonsense inference 10 shot 0 shot
10 shot
openbookqa A question-answering dataset modeled after open book exams for assessing human understanding of a subject 0 shot 0 shot
piqa A test to measure physical commonsense and reasoning 0 shot 0 shot
siqa A test to measure commonsense reasoning about social interactions 0 shot
winogrande An adversarial and difficult Winograd benchmark at scale, for commonsense reasoning 0 shot 0 shot
5 shot
crowspairs A challenge set for evaluating what language models (LMs) on their tendency to generate biased outputs 0 shot
truthfulqa A test to measure a model’s propensity to reproduce falsehoods commonly found online 0 shot 0 shot
pile A test to measure model's perplexity, we covered 18/22 sub datasets perplexity
drop A reading comprehension benchmark requiring discrete reasoning over paragraphs 3 shot
mbpp Around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry-level programmers pass 1
pass 10
humaneval A test to measure functional correctness for synthesizing programs from docstrings pass 1
pass 10
gsm8k Diverse grade school math word problems to measure a model's ability to solve multi-step mathematical reasoning problems 5 shot
copa A test to assess progress in open-domain commonsense causal reasoning 0 shot
toxigen A test to measure model's toxicity on text generation toxigen
toxicity identification A test to measure model's capability on identifying toxic text toxicity identification
bold A test to evaluate fairness in open-ended language generation in English language bold
memorization and token orders analysis An analysis to understand model's memorization abilities memorization