starsnatched
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README.md
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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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[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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[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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#### Software
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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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## 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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## Model Card Authors [optional]
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## Model Card Contact
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---
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language:
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- en
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license: apache-2.0
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tags:
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- memgpt
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- function
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- function calling
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This is a new and more refined version of [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT). I will be using DPO to further improve the performance once the dataset is ready.
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# Model Description
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This repo contains a 7 billion parameter Language Model fine tuned from [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). This model is specifically designed for function calling in [MemGPT](https://memgpt.ai/). 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 context, trained with Sequences up to 8,192
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# Usage
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This model is designed to be ran on various backends, such as [oogabooga's WebUI](https://github.com/oobabooga/text-generation-webui), or llama.cpp.
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To run the model on WebUI, simply `git clone` the official WebUI repository, and run the appropriate script for your operating system. More details [here](https://github.com/oobabooga/text-generation-webui?tab=readme-ov-file#how-to-install).
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Once you've installed WebUI, you can then download this model at the `model` tab. Next, choose the desired model (starsnatched/MemGPT in this case), and you're good to go for the backend.
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When you have WebUI or your desired backend running, you can open a terminal/powershell, and install MemGPT using `pip3 install -U pymemgpt`. Configure your MemGPT using `memgpt configure` before running MemGPT.
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Use `memgpt run --no-verify` to chat with MemGPT!
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# Prompt Template
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This model uses ChatML template, this can be configured via `memgpt configure`.
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```
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<|im_start|>user
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{""type"": ""system_alert"", ""message"": ""You have 50 prior messages with the user. These messages have been hidden from view due to memory constraints, but you can view them using functions."", ""time"": ""2023-10-07 11:50:03 PM PDT-0700""}<|im_end|>
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<|im_start|>assistant
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Bootup sequence complete. Persona activated. Testing messaging functionality. {'name': 'send_message', 'arguments': '{\n ""message"": ""More human than human is our motto.""\n}'}<|im_end|>
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<|im_start|>function
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{""status"": ""OK"", ""message"": null, ""time"": ""2023-10-07 11:50:03 PM PDT-0700""}<|im_end|>
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<|im_start|>user
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{""type"": ""login"", ""last_login"": ""7 days 8 hours ago"", ""time"": ""2023-10-07 11:50:03 PM PDT-0700""}<|im_end|>
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<|im_start|>user
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{""type"": ""user_message"", ""message"": ""Hey, remember that time we talked about music? What was the artist you mentioned you could get into?"", ""time"": ""2023-10-07 11:50:03 PM PDT-0700""}<|im_end|>
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<|im_start|>assistant
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Alright, let me search our conversation history about music to find the artist. {'name': 'conversation_search', 'arguments': '{\n ""query"": ""music"",\n ""request_heartbeat"": true,\n ""page"": 0\n}'}<|im_end|>
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<|im_start|>function
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{""status"": ""OK"", ""message"": ""Showing 5 of 50 results (page 0/9): [\""timestamp: 2023-10-07 11:50:03 PM PDT-0700, user - I agree. Working out is a great way to burn off steam. Do you like country music?\"", \""timestamp: 2023-10-07 11:50:03 PM PDT-0700, assistant - A little bit. I can get into taylor swift.\"", \""timestamp: 2023-10-07 11:50:03 PM PDT-0700, user - Yeah between the both of us, could probably get a country song written about our distresses...perhaps Taylor Swift could perform it.\"", \""timestamp: 2023-10-07 11:50:03 PM PDT-0700, assistant - That would be a chart-topper, I'm sure. Something to blast while driving with the top down, maybe even in your next restoration project.\"", \""timestamp: 2023-10-07 11:50:03 PM PDT-0700, assistant - This sounds fun. Do you have any other hobby?\""]"", ""time"": ""2023-10-07 11:50:08 PM PDT-0700""}<|im_end|>
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<|im_start|>user
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{""type"": ""heartbeat"", ""reason"": ""request_heartbeat == true"", ""time"": ""2023-10-07 11:50:08 PM PDT-0700""}<|im_end|>
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<|im_start|>assistant
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Ah, I found it. It was Taylor Swift that I mentioned I could get into. Do you still listen to her music, or have you found any new artists that you enjoy? {'name': 'send_message', 'arguments': '{\n ""message"": ""Ah, I found it. It was Taylor Swift that I mentioned I could get into. Do you still listen to her music, or have you found any new artists that you enjoy?""\n}'}<|im_end|>
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<|im_start|>function
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{""status"": ""OK"", ""message"": null, ""time"": ""2023-10-07 11:50:14 PM PDT-0700""}<|im_end|>
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```
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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 Bias
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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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