---
model-index:
- name: notus-7b-dpo-lora
  results: []
datasets:
- argilla/ultrafeedback-binarized-avg-rating-for-dpo
language:
- en
base_model: alignment-handbook/zephyr-7b-sft-full
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- preference
- ultrafeedback
license: apache-2.0
---

# Model Card for Notus 7B

Notus is going to be a collection of fine-tuned models using DPO, similarly to Zephyr, but mainly focused
on the Direct Preference Optimization (DPO) step, aiming to incorporate preference feedback into the LLMs
when fine-tuning those. Notus models are intended to be used as assistants via chat-like applications, and 
are evaluated with the MT-Bench and AlpacaEval benchmarks, to be directly compared with Zephyr fine-tuned models
also using DPO.

## Model Details

### Model Description

- **Developed by:** Argilla, Inc. (based on HuggingFace H4 and MistralAI previous efforts and amazing work)
- **Shared by:** Argilla, Inc.
- **Model type:** GPT-like 7B model DPO fine-tuned using LoRA
- **Language(s) (NLP):** Mainly English
- **License:** Apache 2.0 (same as Zephyr 7B SFT and Mistral 7B v0.1)
- **Finetuned from model:** [`alignment-handbook/zephyr-7b-sft-full`](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full)

### Model Sources [optional]

- **Repository:** https://github.com/argilla-io/notus-7b-dpo
- **Paper:** N/A
- **Demo:** https://argilla-notus-chat-ui.hf.space/

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary


## Technical Specifications

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

8 x A100 40GB

#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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## Training procedure