README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- nlp
- llm
---
# K2: a Fully Transparent OSS Language at Llama 2 Performance Using 35% Less Compute

LLM360 demystifies the training recipe used for Llama 2 - 70B with K2. Reaching a comparable performance with Llama 2, K2 has 65B parameters 
and is trained on around 1.4T tokens, resulting a receipe of approximately 35% less compute.

## Evaluations
<center><img src="eval_table_temp.png" alt="eval table"/></center>

## 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](https://huggingface.co/datasets/cerebras/SlimPajama-627B)   | 27.3B        | 1x       | 27.3B       | 2.1%       |
| [starcoder.spm](https://huggingface.co/datasets/bigcode/starcoderdata)   | 67.6B        | 0.5x       | 33.8B       | 2.6%       |
| [starcoder.fim](https://huggingface.co/datasets/bigcode/starcoderdata)   | 67.6B        | 0.5x       | 33.8B       | 2.6%       |
| [redpajama.stackexchange](https://huggingface.co/datasets/cerebras/SlimPajama-627B)   | 61.1B        | 1x       | 61.1B       | 4.7%       |
| [starcoder](https://huggingface.co/datasets/bigcode/starcoderdata)   | 132.6B        | 0.5x       | 66.3B       | 5.1%       |
| [pile-of-law](https://huggingface.co/datasets/pile-of-law/pile-of-law)   | 76.7B        | 1x       | 76.7B       | 5.9%       |
| [redpajama.book](https://huggingface.co/datasets/cerebras/SlimPajama-627B)   | 80.6B        | 1x       | 80.6B       | 6.2%       |
| s2orc   | 107.9B        | 1x       | 107.9B       | 8.3%       |
| [redpajama.wikipedia](https://huggingface.co/datasets/cerebras/SlimPajama-627B)   | 22.1B        | 6x       | 132.6B       | 10.2%       |
| [refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)   | 612.3B        | 1x       | 612.3B       | 47.1%       |
| Totals   | -        | -       | 1.3T       | 100%       |

## First 10 Checkpoints
| Checkpoints      |  |
| ----------- | ----------- |
| [Checkpoint 360](https://huggingface.co/LLM360/K2/tree/ckpt_360)     | [Checkpoint 355](https://huggingface.co/LLM360/K2/tree/ckpt_355)       |
| [Checkpoint 359](https://huggingface.co/LLM360/K2/tree/ckpt_359)   | [Checkpoint 354](https://huggingface.co/LLM360/K2/tree/ckpt_354)        |
| [Checkpoint 358](https://huggingface.co/LLM360/K2/tree/ckpt_358)   | [Checkpoint 353](https://huggingface.co/LLM360/K2/tree/ckpt_353)        |
| [Checkpoint 357](https://huggingface.co/LLM360/K2/tree/ckpt_357)   | [Checkpoint 352](https://huggingface.co/LLM360/K2/tree/ckpt_352)        |
| [Checkpoint 356](https://huggingface.co/LLM360/K2/tree/ckpt_356)   | [Checkpoint 351](https://huggingface.co/LLM360/K2/tree/ckpt_351)        |

[to find all branches: git branch -a]

## 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:** 65 billion parameter language model with the same architecture as LLaMA.
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Resources for more information:**
  - Training Code: TBD
  - Data Preparation: TBD
  - Metrics: TBD
  - Fully processed K2 pretraining dataset: TBD


## 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](https://www.llm360.ai/)