README.md

---
language:
- en
license: mit
library_name: transformers
datasets:
- databricks/databricks-dolly-15k
inference: false
model-index:
- name: dolly-v2-7b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 44.54
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=databricks/dolly-v2-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 69.64
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=databricks/dolly-v2-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 25.18
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=databricks/dolly-v2-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 34.88
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=databricks/dolly-v2-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 60.06
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=databricks/dolly-v2-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 1.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=databricks/dolly-v2-7b
      name: Open LLM Leaderboard
---
# dolly-v2-7b 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`](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-v2-7b` 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](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b`
* [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b`

Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on 
running inference for various GPU configurations.

**Owner**: Databricks, Inc.

## Model Overview
`dolly-v2-7b` 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:

```python
%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](https://huggingface.co/databricks/dolly-v2-3b/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.

```python
import torch
from transformers import pipeline

generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
```

You can then use the pipeline to answer instructions:

```python
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](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py),
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:

```python
import torch
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-7b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-7b", 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.

```python
import torch
from transformers import pipeline

generate_text = pipeline(model="databricks/dolly-v2-7b", 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:

```python
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:

```python
print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip())
```

Example predicting using an instruction with context:

```python
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-v2-7b` 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](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-v2-7b` 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 |

# 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}
}
```

# Happy Hacking!
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_databricks__dolly-v2-7b)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |39.24|
|AI2 Reasoning Challenge (25-Shot)|44.54|
|HellaSwag (10-Shot)              |69.64|
|MMLU (5-Shot)                    |25.18|
|TruthfulQA (0-shot)              |34.88|
|Winogrande (5-shot)              |60.06|
|GSM8k (5-shot)                   | 1.14|