license: mit
datasets:
- tatsu-lab/alpaca
language:
- en
library_name: transformers
inference: false
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 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.
We believe this finding is important because it demonstrates that the ability to create powerful artificial intelligence technologies is vastly more accessible than previously realized.
Databricks is committed to ensuring that every organization and individual benefits from the transformative power of artificial intelligence. The Dolly model family represents our first steps along this journey, and we’re excited to share this technology with the world.
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)
[MATT.HAYES]
The original version of was Dolly was trained using deepspeed ZeRO 3
on the Databricks Machine Learning Platform in just 30 minutes (1 epoch) using a single
NDasrA100_v4 machine with 8x A100 40GB GPUs.
The most recent dolly-v2-12b checkpoint was trained for 10 epochs on the same hardware.
Known 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-v1-6b 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.
Training Data, Bias & Objectionable Content
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.
[LEGAL TO REVIEW]
databricks-dolly-15k: The training data on whichdolly-v2-12bis instruction tuned represents natural language instructions generated by Databricks employees during a 10 day 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.
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.
Intended Uses
[LEGAL]
[MIKE.CONOVER]
Usage
[MATT.HAYES]
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. These results demonstrate that dolly-v2-12b is not state of the art,
and in fact underperforms dolly-v2-12b 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.
+----+------------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+
| | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean |
|----+------------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------|
| 0 | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 |
| 1 | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 |
| 2 | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 |
| 3 | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 |
| 4 | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 |
| 6 | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 |
+----+------------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+