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README.md

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 license: mit
+language:
+- en
+library_name: transformers
+inference: false
 ---
+# dolly-v2-6.9b Model Card
+## Summary
+
+Databricks’ `dolly-v2-6.9b`, 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`](https://github.com/databrickslabs/dolly/tree/master/data) generated 
+by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
+information extraction, open QA and summarization. `dolly-v2-6.9b` 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.  
+
+**Owner**: Databricks, Inc.
+
+## Model Overview
+`dolly-v2-6.9b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from 
+[EleutherAI’s](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned 
+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)
+
+## 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>=0.12.0 transformers[torch]==4.25.1
+```
+
+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](https://huggingface.co/databricks/dolly-v2-6.9b/blob/main/instruct_pipeline.py), 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-v2-6.9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+```
+
+You can then use the pipeline to answer instructions:
+
+```
+generate_text("Explain to me the difference between nuclear fission and fusion.")
+```
+
+Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-6.9b/blob/main/instruct_pipeline.py),
+store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
+
+```
+from instruct_pipeline import InstructionTextGenerationPipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-6.9b", padding_side="left")
+model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-6.9b", device_map="auto")
+
+generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
+```
+
+
+## Known Limitations
+
+### Performance Limitations
+**`dolly-v2-6.9b` 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-v2-6.9b` 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-v2-6.9b` does not have some capabilities, such as well-formatted letter writing, present in the original model.  
+
+### Dataset Limitations
+Like all language models, `dolly-v2-6.9b` 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-v2-6.9b` 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](https://github.com/EleutherAI/lm-evaluation-harness); 
+model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-6.9b` 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.  
+
+TODO benchmarking
+
+# Happy Hacking!