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
Datasets and Mix
The following data mix was used to train K2 and achieve results in line with Llama 2 70B. The full data sequence will be available soon.
| 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.