K2

🚀 K2: a fully-reproducible large language model outperforming Llama 2 70B using 35% less compute

LLM360 demystifies the training recipe used for Llama 2 70B with K2. K2 is fully transparent, meaning we’ve open-sourced all artifacts, including code, data, model checkpoints, intermediate results, and more.

✨ Features

About K2

• 65 billion parameter LLM
• Tokens: 1.4T
• Languages: English
• Models Released: base, chat model
• Trained in 2 stages
• License: Apache 2.0

K2 was developed as a collaboration between MBZUAI, Petuum, and LLM360.

LLM360 Model Performance and Evaluation Collection

The LLM360 Performance and Evaluation Collection is a robust evaluations set consisting of general and domain specific evaluations to assess model knowledge and function. Evaluations include standard best practice benchmarks, medical, math, and coding knowledge. More about the evaluations can be found here.

Detailed analysis can be found on the K2 Weights and Biases project here

Open LLM Leaderboard

K2 Gallery

The K2 gallery allows one to browse the output of various prompts on intermediate K2 checkpoints, which provides an intuitive understanding on how the model develops and improves over time. This is inspired by The Bloom Book. View K2 gallery here

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 can be found here

LLM360 Research Suite

Stage 2 - Last 10 Checkpoints

Stage 1 - Last 10 Checkpoints

[to find all branches: git branch -a]

LLM360 Pretraining Suite

We provide step - by - step reproducation tutorials for tech enthusiasts, AI practitioners and academic or industry researchers who want to learn pretraining techniques here.

LLM360 Developer Suite

We provide step - by - step finetuning tutorials for tech enthusiasts, AI practitioners and academic or industry researchers here.

📦 Installation

Not provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LLM360/K2")
model = AutoModelForCausalLM.from_pretrained("LLM360/K2")

prompt = 'what is the highest mountain on earth?'

input_ids = tokenizer(prompt, return_tensors="pt").input_ids
gen_tokens = model.generate(input_ids, do_sample=True, max_new_tokens=128)

print("-"*20 + "Output for model"  + 20 * '-')
print(tokenizer.batch_decode(gen_tokens)[0])

📚 Documentation

About LLM360

LLM360 is an open research lab enabling community - owned AGI through open - source large model research and development. LLM360 enables community - owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development. We believe in a future where artificial general intelligence (AGI) is created by the community, for the community. Through an open ecosystem of equitable computational resources, high quality data, and flowing technical knowledge, we can ensure ethical AGI development and universal access for all innovators. Visit us

📄 License

The project is licensed under the Apache 2.0 license.

Citation

BibTeX:

@article{K2,
      title={LLM360 K2-65B: Scaling Up Fully Transparent Open-Source LLMs}, 
      author={
      Zhengzhong Liu and Bowen Tan
      and Hongyi Wang and Willie Neiswanger and Tianhua Tao
      and Haonan Li and Fajri Koto and Yuqi Wang and Suqi Sun
      and Omkar Pangarkar and Richard Fan and Yi Gu and Victor Miller
      and Liqun Ma and Liping Tang and Nikhil Ranjan and Yonghao Zhuang
      and Guowei He and Renxi Wang and Mingkai Deng and Robin Algayres 
      and Yuanzhi Li and Zhiqiang Shen and Preslav Nakov
      and Eric Xing      
      },
      year={2024},
}