Instructions to use minchyeom/MemGPT-DPO-MoE-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minchyeom/MemGPT-DPO-MoE-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="minchyeom/MemGPT-DPO-MoE-test")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("minchyeom/MemGPT-DPO-MoE-test") model = AutoModelForCausalLM.from_pretrained("minchyeom/MemGPT-DPO-MoE-test") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use minchyeom/MemGPT-DPO-MoE-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "minchyeom/MemGPT-DPO-MoE-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minchyeom/MemGPT-DPO-MoE-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/minchyeom/MemGPT-DPO-MoE-test
- SGLang
How to use minchyeom/MemGPT-DPO-MoE-test with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "minchyeom/MemGPT-DPO-MoE-test" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minchyeom/MemGPT-DPO-MoE-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "minchyeom/MemGPT-DPO-MoE-test" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minchyeom/MemGPT-DPO-MoE-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use minchyeom/MemGPT-DPO-MoE-test with Docker Model Runner:
docker model run hf.co/minchyeom/MemGPT-DPO-MoE-test
This is a test release of DPO version of MemGPT Language Model.
Model Description
This repository contains a MoE (Mixture of Experts) model of Mistral 7B Instruct. 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.
Key Features
- Function calling
- Dedicated to working with MemGPT
- Supports medium-length context, up to sequences of 8,192
Prompt Format
This model uses ChatML prompt format:
<|im_start|>system
{system_instruction}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{assistant_response}<|im_end|>
Usage
This model is designed to be ran on multiple backends, such as oogabooga's textgen WebUI.
Simply install your preferred backend, and then load up this model.
Then, configure MemGPT using memgpt configure, and chat with MemGPT via memgpt run command!
Model Details
- Developed by: @starsnatched
- Model type: This repo contains a language model based on the transformer decoder architecture.
- Language: English
- Contact: For any questions, concerns or comments about this model, please contact me at Discord, @starsnatched.
Training Infrastructure
- Hardware: The model in this repo was trained on 2x A100 80GB GPUs.
Intended Use
The model is designed to be used as the base model for MemGPT agents.
Limitations and Risks
The model may exhibit unreliable, unsafe, or biased behaviours. Please double check the results this model may produce.
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