REVIEW 10 cited by
LLM360: Towards Fully Transparent Open-Source LLMs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs have only released partial artifacts, such as the final model weights or inference code, and technical reports increasingly limit their scope to high-level design choices and surface statistics. These choices hinder progress in the field by degrading transparency into the training of LLMs and forcing teams to rediscover many details in the training process. We present LLM360, an initiative to fully open-source LLMs, which advocates for all training code and data, model checkpoints, and intermediate results to be made available to the community. The goal of LLM360 is to support open and collaborative AI research by making the end-to-end LLM training process transparent and reproducible by everyone. As a first step of LLM360, we release two 7B parameter LLMs pre-trained from scratch, Amber and CrystalCoder, including their training code, data, intermediate checkpoints, and analyses (at https://www.llm360.ai). We are committed to continually pushing the boundaries of LLMs through this open-source effort. More large-scale and stronger models are underway and will be released in the future.
Forward citations
Cited by 10 Pith papers
-
Fluid Language Model Benchmarking
Fluid Benchmarking, combining IRT-based ability estimation with Fisher-information-based adaptive item selection, improves LM evaluation across efficiency, validity, variance, and saturation in pretraining settings.
-
Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability
Telescope Perplexity, the average negative log probability a reference LM assigns to each token immediately after seeing it, yields strong zero-shot LLM-text detection by probing an early-training aversion to repetition.
-
OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
An open 1M-hour English speech dataset plus Whisper-architecture models trained on it match Whisper's word error rates on short and long-form benchmarks.
-
CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion
CollabEval turns model evaluation into low-rank matrix completion and uses the imputations as control variates to cut CI width and MSE at fixed annotation budget while preserving unbiasedness.
-
ZAYA1-8B Technical Report
ZAYA1-8B is a reasoning MoE model with 700M active parameters that matches larger models on math and coding benchmarks and reaches 91.9% on AIME'25 via Markovian RSA test-time compute.
-
ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution
ZUNA1.1, an open-source 380M EEG diffusion autoencoder, reconstructs variable-length, flexibly masked EEG at least as well as its predecessor and far better than spherical spline interpolation.
-
Index SLM Technical Report
Index-1.9B-Base reaches 64.92 average benchmark score via WSD training with late curated data plus Norm-Head, with open Pure/Boost controls isolating instruction-data inflation.
-
VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
VLA Foundry provides a single training stack for VLA models and releases open models that match prior closed-source performance or outperform baselines on multi-task manipulation in simulation.
-
ZONOS2 Technical Report
ZONOS2 8B is a scaled MoE TTS model with 900M active parameters trained on 6M hours of data that reports competitive SOTA results on naturalness, speaker similarity, WER, and a new ZTTS1-Eval benchmark while releasing...
-
ZONOS2 Technical Report
ZONOS2 8B scales a prior TTS system to 8B parameters with MoE architecture and 6M hours of data, reporting competitive benchmark performance on naturalness and speaker similarity while releasing weights.
Discussion (0). Sign in to comment.