datasets, language, library_name, pipeline_tag, tags, license

datasets	language	library_name	pipeline_tag	tags	license
BatsResearch/ctga-v1	en	transformers	text2text-generation	data generation	apache-2.0

Bonito-v1 GGUF

You can find the original model at BatsResearch/bonito-v1

Variations

Name	Quant method	Bits
bonito-v1_iq4_nl.gguf	IQ4_NL	4
bonito-v1_q4_k_m.gguf	Q4_K_M	4
bonito-v1_q5_k_2.gguf	Q5_K_S	5
bonito-v1_q5_k_m.gguf	Q5_K_M	5
bonito-v1_q6_k.gguf	Q6_K	6
bonito-v1_q8_0.gguf	Q8_0	8
bonito-v1_f16.gguf	FP16	16

Model Card for bonito

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

Bonito can be used to create synthetic instruction tuning datasets to adapt large language models on users' specialized, private data. In our paper, 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

• Repository: https://github.com/BatsResearch/bonito
• Paper: Arxiv link

Uses

Direct Use

To easily generate synthetic instruction tuning datasets, we recommend using the bonito package built using the transformers and the vllm libraries.

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

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.