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Model: BatsResearch/bonito-v1
Source: Original Platform

README.md

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+---
+datasets:
+- BatsResearch/ctga-v1
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- data generation
+license: apache-2.0
+---
+
+# Model Card for bonito
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. 
+
+![Bonito](https://raw.githubusercontent.com/BatsResearch/bonito/main/assets/workflow.png)
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+Bonito can be used to create synthetic instruction tuning datasets to adapt large language models on users' specialized, private data.
+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.
+
+- **Developed by:** Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
+- **Model type:** MistralForCausalLM
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** `mistralai/Mistral-7B-v0.1`
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/BatsResearch/bonito](https://github.com/BatsResearch/bonito)
+- **Paper:** [Learning to Generate Instruction Tuning Datasets for
+Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334)
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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. 
+
+```python
+from bonito import Bonito
+from vllm import SamplingParams
+from datasets import load_dataset
+
+# Initialize the Bonito model
+bonito = Bonito("BatsResearch/bonito-v1")
+
+# load dataaset with unannotated text
+unannotated_text = load_dataset(
+    "BatsResearch/bonito-experiment",
+    "unannotated_contract_nli"
+)["train"].select(range(10))
+
+# Generate synthetic instruction tuning dataset
+sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
+synthetic_dataset = bonito.generate_tasks(
+    unannotated_text,
+    context_col="input",
+    task_type="nli",
+    sampling_params=sampling_params
+)
+```
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+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
+coreference resolution.
+The model might not produce accurate synthetic tasks beyond these task types. 
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+**Limitations** 
+
+Our work relies on the availability of large amounts of unannotated text. 
+If only a small quantity of unannotated text is present, the target language model, after adaptation, may experience a drop in performance. 
+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.
+
+**Risks**
+
+Bonito poses risks similar to those of any large language model.
+For example, our model could be used to generate factually incorrect datasets in specialized domains.
+Our model can exhibit the biases and stereotypes of the base model, Mistral-7B, even after extensive supervised fine-tuning.
+Finally, our model does not include safety training and can potentially generate harmful content.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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. 
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+To train Bonito, we create a new dataset called conditional task generation with attributes by remixing existing instruction tuning datasets. 
+See [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1) for more details. 
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Training Hyperparameters
+
+- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+We train the model using [Q-LoRA](https://github.com/artidoro/qlora) by optimizing the cross entropy loss over the output tokens. 
+The model is trained for 100,000 steps. 
+The training takes about 4 days on four GPUs to complete.
+
+We use the following hyperparameters: 
+  - Q-LoRA rank (r): 64 
+  - Q-LoRA scaling factor (alpha): 4 
+  - Q-LoRA dropout: 0 
+  - Optimizer: Paged AdamW 
+  - Learning rate scheduler: linear 
+  - Max. learning rate: 1e-04
+  - Min. learning rate: 0 
+  - Weight decay: 0 
+  - Dropout: 0 
+  - Max. gradient norm: 0.3 
+  - Effective batch size: 16 
+  - Max. input length: 2,048 
+  - Max. output length: 2,048
+  - Num. steps: 100,000
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+```
+@inproceedings{bonito:aclfindings24,
+  title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
+  author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
+  booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
+  year = {2024}}
+```