K2 / README.md
victormiller's picture
Update README.md
bfa3f60 verified
|
raw
history blame
3.04 kB
metadata
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
library_name: transformers
tags:
  - nlp
  - llm

K2 - Deciphering Llama 2 70B

K2 is a fully transparent large language model on par with Llama 2 - 70B.

Evaluations

eval table

Datasets and Mix

Dataset Starting Tokens Multiplier Total Tokens % of Total
dm-math 4.33B 3x 13B 1%
pubmed-abstracts 4.77B 3x 14.3B 1.1%
uspto 4.77B 3x 14.3B 1.1%
pubmed-central 26B 1x 26B 2%
redpajama.arxiv 27.3B 1x 27.3B 2.1%
starcoder.spm 67.6B 0.5x 33.8B 2.6%
starcoder.fim 67.6B 0.5x 33.8B 2.6%
redpajama.stackexchange 61.1B 1x 61.1B 4.7%
starcoder 132.6B 0.5x 66.3B 5.1%
pile-of-law 76.7B 1x 76.7B 5.9%
redpajama.book 80.6B 1x 80.6B 6.2%
s2orc 107.9B 1x 107.9B 8.3%
redpajama.wikipedia 22.1B 6x 132.6B 10.2%
refinedweb 612.3B 1x 612.3B 47.1%
Totals - - 1.3T 100%

First 10 Checkpoints

Checkpoints
Checkpoint 360[link] Checkpoint 355[link]
Checkpoint 359[link] Checkpoint 354[link]
Checkpoint 358[link] Checkpoint 353[link]
Checkpoint 357[link] Checkpoint 352[link]
Checkpoint 356[link] Checkpoint 351[link]

Additional Artifacts

We are working on release caliber artifacts for the dataset, code, and analysis which will be released over the next few weeks.

Model Description

  • Model type: Language model with the same architecture as LLaMA.
  • Language(s) (NLP): English
  • License: Apache 2.0
  • Resources for more information:
    • [Training Code]
    • [Data Preparation]
    • [Metrics]
    • [Fully processed Amber pretraining data]

About LLM360

LLM360 is an initiative for comprehensive and fully open-sourced LLMs, where all training details, model checkpoints, intermediate results, and additional analyses are made available to the community. Our goal is to advance the field by inviting the community to deepen the understanding of LLMs together. As the first step of the project LLM360, we release all intermediate model checkpoints, our fully-prepared pre-training dataset, all source code and configurations, and training details. We are committed to continually pushing the boundaries of LLMs through this open-source effort.

Visit us