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README.md
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---
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model-index:
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- name: notus-7b-
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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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license: apache-2.0
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---
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# Model Card for Notus 7B
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/LU-vKiC0R7UxxITrwE1F_.png"/>
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<p style="text-align: center;">
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Image was artificially generated by Dalle-3 via ChatGPT Pro
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</p>
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</div>
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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
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also using DPO.
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## Model Details
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### Model Sources [optional]
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- **Repository:** https://github.com/argilla-io/notus-7b
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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 Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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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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## Training procedure
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---
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model-index:
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- name: notus-7b-v1-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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license: apache-2.0
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---
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# Model Card for Notus 7B v1 (LoRA)
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/LU-vKiC0R7UxxITrwE1F_.png" alt="Image was artificially generated by Dalle-3 via ChatGPT Pro"/>
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</div>
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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, AlpacaEval, and LM Evaluation Harness benchmarks, to be directly compared
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with Zephyr fine-tuned models also using DPO.
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## Model Details
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### Model Sources [optional]
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- **Repository:** https://github.com/argilla-io/notus-7b
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- **Paper:** N/A
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- **Demo:** https://argilla-notus-chat-ui.hf.space/
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