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+ ---
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+ datasets:
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+ - BatsResearch/ctga-v1
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+ language:
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+ - en
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+ library_name: transformers
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+ pipeline_tag: text2text-generation
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+ tags:
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+ - data generation
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+ ---
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+
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+ # Bonito-v1 AWQ
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+
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+ - Original model: [BatsResearch/bonito-v1](https://huggingface.co/BatsResearch/bonito-v1)
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+
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+ ## Model Card for bonito
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+ Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning.
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+
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+ ![Bonito](https://raw.githubusercontent.com/BatsResearch/bonito/main/assets/workflow.jpg)
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ Bonito can be used to create synthetic instruction tuning datasets to adapt large language models on users' specialized, private data.
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+ In our [paper](https://github.com/BatsResearch/bonito), we show that Bonito can be used to adapt both pretrained and instruction tuned models to tasks without any annotations.
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+
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+ - **Developed by:** Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
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+ - **Model type:** MistralForCausalLM
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+ - **Language(s) (NLP):** English
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+ - **License:** TBD
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+ - **Finetuned from model:** `mistralai/Mistral-7B-v0.1`
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+
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+ ### Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [https://github.com/BatsResearch/bonito](https://github.com/BatsResearch/bonito)
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+ - **Paper:** Arxiv link
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+ To easily generate synthetic instruction tuning datasets, we recommend using the [bonito](https://github.com/BatsResearch/bonito) package built using the `transformers` and the `vllm` libraries.
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+
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+ ```python
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+ from bonito import Bonito, SamplingParams
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+ from datasets import load_dataset
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+
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+ # Initialize the Bonito model
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+ bonito = Bonito()
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+
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+ # load dataaset with unannotated text
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+ unannotated_text = load_dataset(
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+ "BatsResearch/bonito-experiment",
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+ "unannotated_contract_nli"
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+ )["train"].select(range(10))
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+
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+ # Generate synthetic instruction tuning dataset
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+ sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
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+ synthetic_dataset = bonito.generate_tasks(
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+ unannotated_text,
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+ context_col="input",
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+ task_type="nli",
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+ sampling_params=sampling_params
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+ )
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+ ```
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+
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ Our model is trained to generate the following task types: summarization, sentiment analysis, multiple-choice question answering, extractive question answering, topic classification, natural language inference, question generation, text generation, question answering without choices, paraphrase identification, sentence completion, yes-no question answering, word sense disambiguation, paraphrase generation, textual entailment, and
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+ coreference resolution.
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+ The model might not produce accurate synthetic tasks beyond these task types.