Starlette!
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library_name: transformers
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<!-- Provide a quick summary of what the model is/does. -->
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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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<!-- 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 Dataset 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 Dataset 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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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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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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[More Information Needed]
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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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## Model Card Contact
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library_name: transformers
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license: apache-2.0
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language:
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- en
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This is a test release of DPO version of [MemGPT](https://github.com/cpacker/MemGPT) Language Model.
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# Model Description
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This repository contains a MoE (Mixture of Experts) model of [Mistral 7B Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). It has 2 experts per token. This model is specifically designed for function calling in MemGPT. It demonstrates comparable performances to GPT-4 when it comes to working with MemGPT.
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# Key Features
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* Function calling
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* Dedicated to working with MemGPT
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* Supports medium-length context, up to sequences of 8,192
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# Prompt Format
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This model uses **ChatML** prompt format:
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```
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<|im_start|>system
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{system_instruction}<|im_end|>
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<|im_start|>user
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{user_message}<|im_end|>
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<|im_start|>assistant
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{assistant_response}<|im_end|>
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```
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# Usage
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This model is designed to be ran on multiple backends, such as [oogabooga's textgen WebUI](https://github.com/oobabooga/text-generation-webui).
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Simply install your preferred backend, and then load up this model.
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Then, configure MemGPT using `memgpt configure`, and chat with MemGPT via `memgpt run` command!
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# Model Details
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* Developed by: @starsnatched
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* Model type: This repo contains a language model based on the transformer decoder architecture.
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* Language: English
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* Contact: For any questions, concerns or comments about this model, please contact me at Discord, @starsnatched.
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# Training Infrastructure
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* Hardware: The model in this repo was trained on 2x A100 80GB GPUs.
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# Intended Use
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The model is designed to be used as the base model for MemGPT agents.
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# Limitations and Risks
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The model may exhibit unreliable, unsafe, or biased behaviours. Please double check the results this model may produce.
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