dolly-v2-12b Model Card

Summary

Databricks’ dolly-v2-12b, an instruction-following large language model trained on the Databricks machine learning platform
that is licensed for commercial use. Based on pythia-12b, Dolly is trained on ~15k instruction/response fine tuning records
databricks-dolly-15k generated
by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
information extraction, open QA and summarization. dolly-v2-12b is not a state-of-the-art model, but does exhibit surprisingly
high quality instruction following behavior not characteristic of the foundation model on which it is based.

Dolly v2 is also available in these smaller models sizes:

• dolly-v2-7b, a 6.9 billion parameter based on pythia-6.9b
• dolly-v2-3b, a 2.8 billion parameter based on pythia-2.8b

Please refer to the dolly GitHub repo for tips on
running inference for various GPU configurations.

Owner: Databricks, Inc.

Model Overview

dolly-v2-12b is a 12 billion parameter causal language model created by Databricks that is derived from
EleutherAI’s Pythia-12b and fine-tuned
on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA)

Usage

To use the model with the transformers library on a machine with GPUs, first make sure you have the transformers and accelerate libraries installed.
In a Databricks notebook you could run:

%pip install accelerate>=0.12.0 transformers[torch]==4.25.1

示例代码

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

# device参数需要指定cpu或者cuda
if __name__ == '__main__':
    model = "AI-ModelScope/dolly-v2-12b"
    pipe = pipeline(Tasks.text_generation, model=model, device='cuda:0', model_revision='v1.0.2')
    instruction = "Explain to me the difference between nuclear fission and fusion."
    output = pipe(instruction)
    print(output)

Known Limitations

Performance Limitations

dolly-v2-12b is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform
competitively with more modern model architectures or models subject to larger pretraining corpuses.

The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community.
In particular, dolly-v2-12b struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors,
dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.
Moreover, we find that dolly-v2-12b does not have some capabilities, such as well-formatted letter writing, present in the original model.

Dataset Limitations

Like all language models, dolly-v2-12b reflects the content and limitations of its training corpuses.

• The Pile: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,
  it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly
  in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit
  associations.

• databricks-dolly-15k: The training data on which dolly-v2-12b is instruction tuned represents natural language instructions generated
  by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages
  for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or
  personally identifying information about non-public figures, but it may contain typos and factual errors.
  The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects
  the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large.

Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that
maximize the potential of all individuals and organizations.

Benchmark Metrics

Below you’ll find various models benchmark performance on the EleutherAI LLM Evaluation Harness;
model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that dolly-v2-12b is not state of the art,
and in fact underperforms dolly-v1-6b in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets,
but a robust statement as to the sources of these variations requires further study.

Happy Hacking!