metadata

license: other
inference: true
datasets:
  - databricks/databricks-dolly-15k
  - OpenAssistant/oasst1
  - sahil2801/CodeAlpaca-20k
language:
  - en

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Allen AI's Tulu 13B fp16

These files are pytorch format fp16 model files for Allen AI's Tulu 13B.

It is the result of merging and/or converting the source repository to float16.

Repositories available

Prompt template

The following template should be used:

<|user|>
prompt goes here
<|assistant|>

Note: There should be a newline after <|assistant|>. This appears to be very important for getting this model to respond correctly.

In other words, the prompt is:

<|user|>\nprompt goes here\n<|assistant|>\n

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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Original model card: Allen AI's Tulu 13B

Tulu 13B

This model is a 13B LLaMa model finetuned on a mixture of instruction datasets (FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT). Please note this is a model diff - see below for usage instructions.

This was trained as part of the paper How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources. The codebase used to train and evaluate this model can be found at https://github.com/allenai/open-instruct.

This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt).

Usage

We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here: https://huggingface.co/docs/transformers/main/model_doc/llama

Clone https://github.com/allenai/open-instruct and install the required dependencies, or just copy scripts/weight_diff.py and install the minimal requirements listed in weight-diff-requirements.txt. Then download or clone this model diff to the same machine.

Then, run:

python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location}

And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models.

Input Format

The model is trained to use the following format (note the newlines):

<|user|>
Your message here!
<|assistant|>

For best results, format all inputs in this manner.

Performance

Here is the performance of this model across benchmarks explored in our paper How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources:

MMLU 0-shot	MMLU 5-shot	GSM Direct	GSM CoT	BBH Direct	BBH CoT	TydiQA Gold-Passage	TydiQA Closed-book	Codex-Eval Pass@1	Codex-Eval Pass@10	AlpacaFarm vs Davinci-003	Average
49.2	51.8	5.0	36.5	41.3	42.8	46.1	9.2	21.3	35.0	53.9	37.2

If you use this model, please cite our work, the llama paper, and the original datasets:

@misc{wang2023far,
      title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, 
      author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
      year={2023},
      eprint={2306.04751},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{touvron2023llama,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
      year={2023},
      eprint={2302.13971},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{dolly,
  author = {Databricks},
  title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {Blog post},
  url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}
}

@article{longpre2023flan,
  title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
  author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others},
  journal={arXiv preprint arXiv:2301.13688},
  year={2023}
}

@misc{köpf2023openassistant,
      title={OpenAssistant Conversations -- Democratizing Large Language Model Alignment}, 
      author={Andreas Köpf and Yannic Kilcher and Dimitri von Rütte and Sotiris Anagnostidis and Zhi-Rui Tam and Keith Stevens and Abdullah Barhoum and Nguyen Minh Duc and Oliver Stanley and Richárd Nagyfi and Shahul ES and Sameer Suri and David Glushkov and Arnav Dantuluri and Andrew Maguire and Christoph Schuhmann and Huu Nguyen and Alexander Mattick},
      year={2023},
      eprint={2304.07327},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{peng2023instruction,
  title={Instruction Tuning with GPT-4},
  author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng},
  journal={arXiv preprint arXiv:2304.03277},
  year={2023}
}

@misc{codealpaca,
  author = {Sahil Chaudhary},
  title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/sahil280114/codealpaca}},
}