Instructions to use Dans-Archive/Dans-PersonalityEngine-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dans-Archive/Dans-PersonalityEngine-13b with Transformers:

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

pipe = pipeline("text-generation", model="Dans-Archive/Dans-PersonalityEngine-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dans-Archive/Dans-PersonalityEngine-13b")
model = AutoModelForCausalLM.from_pretrained("Dans-Archive/Dans-PersonalityEngine-13b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Dans-Archive/Dans-PersonalityEngine-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dans-Archive/Dans-PersonalityEngine-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dans-Archive/Dans-PersonalityEngine-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Dans-Archive/Dans-PersonalityEngine-13b

SGLang

How to use Dans-Archive/Dans-PersonalityEngine-13b 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 "Dans-Archive/Dans-PersonalityEngine-13b" \
    --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": "Dans-Archive/Dans-PersonalityEngine-13b",
		"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 "Dans-Archive/Dans-PersonalityEngine-13b" \
        --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": "Dans-Archive/Dans-PersonalityEngine-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dans-Archive/Dans-PersonalityEngine-13b with Docker Model Runner:
```
docker model run hf.co/Dans-Archive/Dans-PersonalityEngine-13b
```

Description:

This is a multipurpose chat / chat instruct hybrid model in the same vein as the Pygmalion team's Metharme. It uses a curated pile of training data that has been normalized into a consistent training format. It has been trained on a wide array of one shot instructions, multi round instructions, and role playing scenarios.

Prompt format:

Metharme

The prompt should start with the cursor on the same line directly after "<|model|>" with no space. The following are all valid formats and can be extended to as many rounds as desired.

<|system|>system message here<|user|>user message here<|model|>

<|system|>system message here<|user|>user message here<|model|>model message<|user|>user message here<|model|>

<|system|>system message here<|model|>

<|system|>system message here<|model|>model message<|user|>user message here<|model|>

Some example prompts:

<|system|>The following is a transcript between a helpful assistant and a user.<|user|>Why is the sky blue?<|model|>

<|system|>You are a Virtual Story Generator. You take the user's input and create an excellent and captivating story that goes in that direction. Use an abundance of sensory descriptions and eloquent prose.<|user|>Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<|model|>

<|system|>You are a professional editor with decades of experience, help the user with any task they have for you.<|user|>Can you rewrite this to flow better? "I knew I probably shouldnt have done that but oh well"<|model|>

More will be added at a later date.