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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
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+[Request more models](https://github.com/RichardErkhov/quant_request)
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+
+dolly-v2-7b - bnb 4bits
+- Model creator: https://huggingface.co/databricks/
+- Original model: https://huggingface.co/databricks/dolly-v2-7b/
+
+
+
+
+Original model description:
+---
+license: mit
+language:
+- en
+library_name: transformers
+inference: false
+datasets:
+- databricks/databricks-dolly-15k
+---
+# 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!
+