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>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
The instruction following pipeline can be loaded using the pipeline
function as shown below. This loads a custom InstructionTextGenerationPipeline
found in the model repo here, which is why trust_remote_code=True
is required.
Including torch_dtype=torch.bfloat16
is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality.
It is also fine to remove it if there is sufficient memory.
import torch
from transformers import pipeline
generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
You can then use the pipeline to answer instructions:
res = generate_text("Explain to me the difference between nuclear fission and fusion.")
print(res[0]["generated_text"])
Alternatively, if you prefer to not use trust_remote_code=True
you can download instruct_pipeline.py,
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
import torch
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto", torch_dtype=torch.bfloat16)
generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
LangChain Usage
To use the pipeline with LangChain, you must set return_full_text=True
, as LangChain expects the full text to be returned
and the default for the pipeline is to only return the new text.
import torch
from transformers import pipeline
generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16,
trust_remote_code=True, device_map="auto", return_full_text=True)
You can create a prompt that either has only an instruction or has an instruction with context:
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline
# template for an instrution with no input
prompt = PromptTemplate(
input_variables=["instruction"],
template="{instruction}")
# template for an instruction with input
prompt_with_context = PromptTemplate(
input_variables=["instruction", "context"],
template="{instruction}\n\nInput:\n{context}")
hf_pipeline = HuggingFacePipeline(pipeline=generate_text)
llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
Example predicting using a simple instruction:
print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip())
Example predicting using an instruction with context:
context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman,
and Founding Father who served as the first president of the United States from 1789 to 1797."""
print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip())
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 whichdolly-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.
model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean |
---|---|---|---|---|---|---|---|---|
EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 |
EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 |
databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 |
EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 |
EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 |
databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 |
databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 |
databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 |
EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 |
Citation
@online{DatabricksBlog2023DollyV2,
author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
urldate = {2023-06-30}
}
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