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README.md
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---
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license: apache-2.0
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base_model: alignment-handbook/zephyr-7b-sft-full
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tags:
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- generated_from_trainer
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model-index:
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- name: notus-7b-dpo
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results: []
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---
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# notus-7b-dpo
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More
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### Training hyperparameters
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| 0.0059 | 2.8 | 2700 | 0.4694 | -3.4307 | -7.2484 | 0.7976 | 3.8177 | -315.1584 | -333.3234 | -2.1572 | -2.4483 |
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| 0.0054 | 2.91 | 2800 | 0.4707 | -3.4959 | -7.3283 | 0.8056 | 3.8324 | -315.9576 | -333.9758 | -2.1575 | -2.4491 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.1.1+cu121
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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---
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model-index:
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- name: notus-7b-dpo-lora
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results: []
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datasets:
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- argilla/ultrafeedback-binarized-avg-rating-for-dpo
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language:
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- en
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base_model: alignment-handbook/zephyr-7b-sft-full
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- dpo
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- preference
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- ultrafeedback
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license: apache-2.0
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---
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# Model Card for Notus 7B
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Notus is going to be a collection of fine-tuned models using DPO, similarly to Zephyr, but mainly focused
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on the Direct Preference Optimization (DPO) step, aiming to incorporate preference feedback into the LLMs
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when fine-tuning those. Notus models are intended to be used as assistants via chat-like applications, and
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are evaluated with the MT-Bench and AlpacaEval benchmarks, to be directly compared with Zephyr fine-tuned models
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also using DPO.
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## Model Details
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# notus-7b-dpo
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### Model Description
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- **Developed by:** Argilla, Inc. (based on HuggingFace H4 and MistralAI previous efforts and amazing work)
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- **Shared by:** Argilla, Inc.
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- **Model type:** GPT-like 7B model DPO fine-tuned
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- **Language(s) (NLP):** Mainly English
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- **License:** Apache 2.0 (same as Zephyr 7B SFT and Mistral 7B v0.1)
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- **Finetuned from model:** [`alignment-handbook/zephyr-7b-sft-full`](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full)
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### Model Sources [optional]
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- **Repository:** https://github.com/argilla-io/notus-7b-dpo
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- **Paper:** N/A
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- **Demo:** https://argilla-notus-chat-ui.hf.space/
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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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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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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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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Data 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. -->
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[More Information Needed]
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### Training hyperparameters
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| 0.0059 | 2.8 | 2700 | 0.4694 | -3.4307 | -7.2484 | 0.7976 | 3.8177 | -315.1584 | -333.3234 | -2.1572 | -2.4483 |
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| 0.0054 | 2.91 | 2800 | 0.4707 | -3.4959 | -7.3283 | 0.8056 | 3.8324 | -315.9576 | -333.9758 | -2.1575 | -2.4491 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.1.1+cu121
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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## Evaluation
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- Loss: 0.4730
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- Rewards/chosen: -3.5289
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- Rewards/rejected: -7.3700
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- Rewards/accuracies: 0.8016
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- Rewards/margins: 3.8412
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- Logps/rejected: -316.3751
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- Logps/chosen: -334.3053
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- Logits/rejected: -2.1644
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- Logits/chosen: -2.4556
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Data Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Technical Specifications
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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8 x A100 40GB
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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