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---
datasets:
- BatsResearch/ctga-v1
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
tags:
- data generation
license: apache-2.0
---
# Bonito-v1 AWQ
You can find the original model at [BatsResearch/bonito-v1](https://huggingface.co./BatsResearch/bonito-v1)
## Variations
* GEMM: [model.safetensors](https://huggingface.co./alexandreteles/bonito-v1-awq/blob/main/model.safetensors)
* GEMV: [model_gemv.safetensors](https://huggingface.co./alexandreteles/bonito-v1-awq/blob/main/model_gemv.safetensors)
## 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.
## 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://github.com/BatsResearch/bonito), 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:** TBD
- **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:** Arxiv link
## 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, SamplingParams
from datasets import load_dataset
# Initialize the Bonito model
bonito = Bonito()
# 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. |