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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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license: apache-2.0 |
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--- |
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# Bonito-v1 AWQ |
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You can find the original model at [BatsResearch/bonito-v1](https://huggingface.co/BatsResearch/bonito-v1) |
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## Variations |
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* GEMM: [model.safetensors](https://huggingface.co/alexandreteles/bonito-v1-awq/blob/main/model.safetensors) |
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* GEMV: [model_gemv.safetensors](https://huggingface.co/alexandreteles/bonito-v1-awq/blob/main/model_gemv.safetensors) |
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## Model Card for bonito |
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<!-- Provide a quick summary of what the model is/does. --> |
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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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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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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- **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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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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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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## Uses |
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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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### Direct Use |
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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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```python |
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from bonito import Bonito, SamplingParams |
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from datasets import load_dataset |
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# Initialize the Bonito model |
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bonito = Bonito() |
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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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# 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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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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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. |
