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--- |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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inference: false |
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--- |
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# dolly-v2-12b Model Card |
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## Summary |
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Databricks’ `dolly-v2-12b`, an instruction-following large language model trained on the Databricks machine learning platform |
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that is licensed for commercial use. Based on `pythia-12b`, Dolly is trained on ~15k instruction/response fine tuning records |
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[`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated |
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by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, |
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information extraction, open QA and summarization. `dolly-v2-12b` is not a state-of-the-art model, but does exhibit surprisingly |
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high quality instruction following behavior not characteristic of the foundation model on which it is based. |
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**Owner**: Databricks, Inc. |
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## Model Overview |
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`dolly-v2-12b` is a 12 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from |
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[EleutherAI’s](https://www.eleuther.ai/) [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) and fine-tuned |
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on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) |
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## Usage |
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To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. |
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In a Databricks notebook you could run: |
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``` |
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%pip install accelerate>=0.12.0 transformers[torch]==4.25.1 |
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``` |
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The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` |
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found in the model repo [here](https://huggingface.co/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. |
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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. |
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It is also fine to remove it if there is sufficient memory. |
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``` |
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import torch |
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from transformers import pipeline |
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generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") |
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``` |
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You can then use the pipeline to answer instructions: |
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``` |
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generate_text("Explain to me the difference between nuclear fission and fusion.") |
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``` |
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Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py), |
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store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: |
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``` |
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from instruct_pipeline import InstructionTextGenerationPipeline |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left") |
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model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto") |
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generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) |
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``` |
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## Known Limitations |
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### Performance Limitations |
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**`dolly-v2-12b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform |
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competitively with more modern model architectures or models subject to larger pretraining corpuses. |
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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. |
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In particular, `dolly-v2-12b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, |
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dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. |
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Moreover, we find that `dolly-v2-12b` does not have some capabilities, such as well-formatted letter writing, present in the original model. |
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### Dataset Limitations |
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Like all language models, `dolly-v2-12b` reflects the content and limitations of its training corpuses. |
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- **The Pile**: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, |
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it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly |
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in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit |
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associations. |
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- **`databricks-dolly-15k`**: The training data on which `dolly-v2-12b` is instruction tuned represents natural language instructions generated |
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by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages |
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for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or |
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personally identifying information about non-public figures, but it may contain typos and factual errors. |
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The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects |
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the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. |
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Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that |
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maximize the potential of all individuals and organizations. |
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### Benchmark Metrics |
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Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); |
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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, |
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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, |
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but a robust statement as to the sources of these variations requires further study. |
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``` |
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+------------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+ |
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| model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | |
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+------------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------| |
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| EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | |
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| databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | |
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| databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | |
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| EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | |
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+------------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+ |
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``` |
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# Happy Hacking! |
