152 lines
6.4 KiB
Markdown
152 lines
6.4 KiB
Markdown
---
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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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# 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://arxiv.org/abs/2402.18334), 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:** Apache 2.0
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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:** [Learning to Generate Instruction Tuning Datasets for
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Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334)
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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
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from vllm import SamplingParams
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from datasets import load_dataset
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# Initialize the Bonito model
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bonito = Bonito("BatsResearch/bonito-v1")
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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.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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**Limitations**
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Our work relies on the availability of large amounts of unannotated text.
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If only a small quantity of unannotated text is present, the target language model, after adaptation, may experience a drop in performance.
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While we demonstrate positive improvements on pretrained and instruction-tuned models, our observations are limited to the three task types (yes-no question answering, extractive question answering, and natural language inference) considered in our paper.
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**Risks**
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Bonito poses risks similar to those of any large language model.
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For example, our model could be used to generate factually incorrect datasets in specialized domains.
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Our model can exhibit the biases and stereotypes of the base model, Mistral-7B, even after extensive supervised fine-tuning.
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Finally, our model does not include safety training and can potentially generate harmful content.
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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We recommend users thoroughly inspect the generated tasks and benchmark performance on critical datasets before deploying the models trained with the synthetic tasks into the real world.
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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To train Bonito, we create a new dataset called conditional task generation with attributes by remixing existing instruction tuning datasets.
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See [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1) for more details.
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Training Hyperparameters
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- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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We train the model using [Q-LoRA](https://github.com/artidoro/qlora) by optimizing the cross entropy loss over the output tokens.
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The model is trained for 100,000 steps.
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The training takes about 4 days on four GPUs to complete.
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We use the following hyperparameters:
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- Q-LoRA rank (r): 64
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- Q-LoRA scaling factor (alpha): 4
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- Q-LoRA dropout: 0
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- Optimizer: Paged AdamW
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- Learning rate scheduler: linear
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- Max. learning rate: 1e-04
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- Min. learning rate: 0
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- Weight decay: 0
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- Dropout: 0
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- Max. gradient norm: 0.3
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- Effective batch size: 16
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- Max. input length: 2,048
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- Max. output length: 2,048
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- Num. steps: 100,000
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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```
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@inproceedings{bonito:aclfindings24,
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title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
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author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
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booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
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year = {2024}}
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``` |