microsoft/dolly-v2-7b-olive-optimized

dolly-v2-7b Olive Optimized Model Card

Summary

Databricks’ dolly-v2-7b, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on pythia-6.9b, 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-v2-7b 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 other models sizes:

• dolly-v2-12b, a 12 billion parameter based on pythia-12b
• 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.

Olive Optimization

This repo hosts model files that may be loaded as an ORTModelForCausalLM when using Python with 🤗 Optimum. Alternatively, the ONNX models may be composed into a custom pipeline in any language that supports ONNX Runtime & DirectML. If you choose to use ONNX Runtime & DirectML outside of Python, then you will need to provide your own implementation of the tokenizer.

Model	Impl
dolly-v2-7b decoder merged with past	ONNX Model
Tokenizer	AutoTokenizer (🤗 Transformers)

The ONNX model above was processed with the Olive toolchain using the Olive + Dolly V2 with DirectML Sample. The Olive sample performs the following steps:

1. Run the OptimumConversion Pass
2. Run the OrtTransformersOptimization Pass, which leverages the ONNX Runtime Transformer Model Optimization Tool. This step executes several time-consuming graph transformations, such as fusing subgraphs into LayerNorm.
3. Convert the optimized ONNX models from FLOAT32 to FLOAT16.
4. Run the OptimumMerging Pass to leverage caching and reduce memory usage by merging the decoder_model.onnx and decoder_with_past_model.onnx models together.

Model Overview

dolly-v2-7b is a 6.9 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-6.9b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA)

dolly-v2-7b-olive-optimized is an optimized ONNX model of dolly-v2-7b generated by Olive that is meant to be used with ONNX Runtime and DirectML.

Usage

To use the model with the transformers library on a machine with ONNX Runtime and DirectML, first make sure you have the transformers, accelerate, optimum, onnxruntime-directml and onnx libraries installed:

pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" "optimum>=1.8.8,<2" "onnxruntime-directml>=1.15.1,<2" "onnx>=1.14.0<2"

You can then download instruct_pipeline.py and construct the pipeline from the loaded model and tokenizer:

from transformers import AutoTokenizer, TextStreamer
from optimum.onnxruntime import ORTModelForCausalLM
from instruct_pipeline import InstructionTextGenerationPipeline

tokenizer = AutoTokenizer.from_pretrained("microsoft/dolly-v2-7b-olive-optimized", padding_side="left")
model = ORTModelForCausalLM.from_pretrained("microsoft/dolly-v2-7b-olive-optimized", provider="DmlExecutionProvider", use_cache=True, use_merged=True, use_io_binding=False)

streamer = TextStreamer(tokenizer, skip_prompt=True)
generate_text = InstructionTextGenerationPipeline(model=model, streamer=streamer, tokenizer=tokenizer, max_new_tokens=128)
generate_text("Explain to me the difference between nuclear fission and fusion.")

Known Limitations

Performance Limitations

dolly-v2-7b 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-v2-7b 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-v2-7b does not have some capabilities, such as well-formatted letter writing, present in the original model.

Dataset Limitations

Like all language models, dolly-v2-7b 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-v2-7b 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-v2-7b 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

Happy Hacking!

This model is an optimized version of Databricks, Inc. databricks/dolly-v2-7b​.