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Model: verseAI/databricks-dolly-v2-3b Source: Original Platform
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README.md
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README.md
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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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datasets:
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- databricks/databricks-dolly-15k
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duplicated_from: databricks/dolly-v2-3b
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---
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# dolly-v2-3b Model Card
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# Fork of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b)
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Original README copied below:
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## Summary
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Databricks’ `dolly-v2-3b`, 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-2.8b`, 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-v2-3b` 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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Dolly v2 is also available in these larger models sizes:
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* [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b`
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* [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b`
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Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on
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running inference for various GPU configurations.
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**Owner**: Databricks, Inc.
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## Model Overview
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`dolly-v2-3b` is a 2.8 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-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) 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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```python
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%pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
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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-v2-3b/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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```python
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import torch
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from transformers import pipeline
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generate_text = pipeline(model="databricks/dolly-v2-3b", 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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```python
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res = generate_text("Explain to me the difference between nuclear fission and fusion.")
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print(res[0]["generated_text"])
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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-v2-3b/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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```python
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import torch
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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-v2-3b", padding_side="left")
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model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16)
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generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
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```
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### LangChain Usage
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To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned
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and the default for the pipeline is to only return the new text.
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```python
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import torch
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from transformers import pipeline
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generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16,
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trust_remote_code=True, device_map="auto", return_full_text=True)
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```
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You can create a prompt that either has only an instruction or has an instruction with context:
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```python
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from langchain import PromptTemplate, LLMChain
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from langchain.llms import HuggingFacePipeline
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# template for an instrution with no input
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prompt = PromptTemplate(
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input_variables=["instruction"],
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template="{instruction}")
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# template for an instruction with input
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prompt_with_context = PromptTemplate(
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input_variables=["instruction", "context"],
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template="{instruction}\n\nInput:\n{context}")
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hf_pipeline = HuggingFacePipeline(pipeline=generate_text)
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llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
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llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
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```
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Example predicting using a simple instruction:
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```python
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print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip())
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```
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Example predicting using an instruction with context:
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```python
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context = """George Washington (February 22, 1732[b] – December 14, 1799) was an American military officer, statesman,
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and Founding Father who served as the first president of the United States from 1789 to 1797."""
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print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip())
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```
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## Known Limitations
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### Performance Limitations
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**`dolly-v2-3b` 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-v2-3b` 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-v2-3b` 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-v2-3b` 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-v2-3b` 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-v2-3b` is not state of the art.
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It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters.
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| model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean |
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| --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------|
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| EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 |
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| EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 |
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| databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 |
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| EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 |
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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-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 |
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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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# Happy Hacking!
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