Open Llama 13b

🚀 OpenLLaMA: An Open Reproduction of LLaMA

This repository presents an open - source reproduction of Meta AI's LLaMA large language model with permissive licensing. It offers 3B, 7B, and 13B models trained on 1T tokens, along with pre - trained weights, evaluation results, and comparisons.

🚀 Quick Start

In this repo, we present a permissively licensed open source reproduction of Meta AI's LLaMA large language model. We are releasing 3B, 7B and 13B models trained on 1T tokens. We provide PyTorch and JAX weights of pre - trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the project homepage of OpenLLaMA for more details.

✨ Features

• Open Source Reproduction: Offers an open - source alternative to Meta AI's LLaMA.
• Multiple Model Sizes: Releases 3B, 7B, and 13B models trained on 1T tokens.
• Dual - format Weights: Provides weights in EasyLM and PyTorch formats.
• Comprehensive Evaluation: Presents evaluation results and comparisons with original LLaMA and GPT - J.

📦 Installation

The weights are released in two formats: an EasyLM format to be used with our EasyLM framework, and a PyTorch format to be used with the Hugging Face transformers library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license.

💻 Usage Examples

Basic Usage

import torch
from transformers import LlamaTokenizer, LlamaForCausalLM

# model_path = 'openlm-research/open_llama_3b'
# model_path = 'openlm-research/open_llama_7b'
model_path = 'openlm-research/open_llama_13b'

tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map='auto',
)

prompt = 'Q: What is the largest animal?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids

generation_output = model.generate(
    input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))

Advanced Usage

For more advanced usage, please follow the transformers LLaMA documentation.

Evaluating with LM - Eval - Harness

The model can be evaluated with [lm - eval - harness](https://github.com/EleutherAI/lm - evaluation - harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in use_fast=False to [this part of lm - eval - harness](https://github.com/EleutherAI/lm - evaluation - harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9 - L316C10), as shown in the example below:

tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained(
    pretrained if tokenizer is None else tokenizer,
    revision=revision + ("/" + subfolder if subfolder is not None else ""),
    use_fast=False
)

📚 Documentation

Dataset and Training

We train our models on the RedPajama dataset released by Together, which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA.

We train the models on cloud TPU - v4s using [EasyLM](https://github.com/young - geng/EasyLM), a JAX based training pipeline we developed for training and fine - tuning large language models. We employ a combination of normal data parallelism and [fully sharded data parallelism (also know as ZeRO stage 3)](https://engineering.fb.com/2021/07/15/open - source/fsdp/) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU - v4 chip for our 7B model.

Evaluation

We evaluated OpenLLaMA on a wide range of tasks using [lm - evaluation - harness](https://github.com/EleutherAI/lm - evaluation - harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm - evaluation - harness](https://github.com/EleutherAI/lm - evaluation - harness/issues/443). Additionally, we present the results of GPT - J, a 6B parameter model trained on the Pile dataset by EleutherAI.

The original LLaMA model was trained for 1 trillion tokens and GPT - J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT - J across a majority of tasks, and outperforms them in some tasks.

We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.

📄 License

The project is licensed under the Apache 2.0 license.

🔗 Contact

We would love to get feedback from the community. If you have any questions, please open an issue or contact us.

OpenLLaMA is developed by: [Xinyang Geng](https://young - geng.xyz/)* and Hao Liu* from Berkeley AI Research. *Equal Contribution

🙏 Acknowledgment

We thank the Google TPU Research Cloud program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback.

The OpenLLaMA 13B model is trained in collaboration with Stability AI, and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support.

📚 Reference

If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:

@software{openlm2023openllama,
  author = {Geng, Xinyang and Liu, Hao},
  title = {OpenLLaMA: An Open Reproduction of LLaMA},
  month = May,
  year = 2023,
  url = {https://github.com/openlm-research/open_llama}
}

@software{together2023redpajama,
  author = {Together Computer},
  title = {RedPajama - Data: An Open Source Recipe to Reproduce LLaMA training dataset},
  month = April,
  year = 2023,
  url = {https://github.com/togethercomputer/RedPajama - Data}
}

@article{touvron2023llama,
  title={Llama: Open and efficient foundation language models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie - Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}