98 lines
4.1 KiB
Markdown
98 lines
4.1 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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# Bonito-v1 GGUF
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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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| Name | Quant method | Bits |
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| ---- | ---- | ---- |
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| [bonito-v1_iq4_nl.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_iq4_nl.gguf) | IQ4_NL | 4 | 4.16 GB|
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| [bonito-v1_q4_k_m.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q4_k_m.gguf) | Q4_K_M | 4 | 4.37 GB|
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| [bonito-v1_q5_k_2.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q5_k_s.gguf) | Q5_K_S | 5 | 5.00 GB|
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| [bonito-v1_q5_k_m.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q5_k_m.gguf) | Q5_K_M | 5 | 5.13 GB|
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| [bonito-v1_q6_k.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q6_k.gguf) | Q6_K | 6 | 5.94 GB|
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| [bonito-v1_q8_0.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q8_0.gguf) | Q8_0 | 8 | 7.70 GB|
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| [bonito-v1_f16.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_f16.gguf) | FP16 | 16 | 14.5 GB|
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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. |