Instructions to use minchyeom/MemGPT-DPO-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minchyeom/MemGPT-DPO-uncensored with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="minchyeom/MemGPT-DPO-uncensored")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("minchyeom/MemGPT-DPO-uncensored")
model = AutoModelForCausalLM.from_pretrained("minchyeom/MemGPT-DPO-uncensored")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use minchyeom/MemGPT-DPO-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minchyeom/MemGPT-DPO-uncensored"
# 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-uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/minchyeom/MemGPT-DPO-uncensored

SGLang

How to use minchyeom/MemGPT-DPO-uncensored 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-uncensored" \
    --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-uncensored",
		"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-uncensored" \
        --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-uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use minchyeom/MemGPT-DPO-uncensored with Docker Model Runner:
```
docker model run hf.co/minchyeom/MemGPT-DPO-uncensored
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is an uncensored release of DPO version of MemGPT Language Model.

WARNING

This model is UNCENSORED. That means this model is highly compliant to any requests, even unethical and potentially dangerous ones. I do not take any responsibility whatsoever for any damage caused by the model in this repo.

Model Description

This repository contains an uncensored model of Mistral 7B Instruct. This model is specifically designed for operating within function calling environment 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.

Downloads last month: 10

Safetensors

Model size

7B params

Tensor type

F16

Model tree for minchyeom/MemGPT-DPO-uncensored

Quantizations

2 models