Instructions to use royallab/Pygmalion-2-13b-SuperCOT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use royallab/Pygmalion-2-13b-SuperCOT with Transformers:

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

pipe = pipeline("text-generation", model="royallab/Pygmalion-2-13b-SuperCOT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("royallab/Pygmalion-2-13b-SuperCOT")
model = AutoModelForCausalLM.from_pretrained("royallab/Pygmalion-2-13b-SuperCOT")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use royallab/Pygmalion-2-13b-SuperCOT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "royallab/Pygmalion-2-13b-SuperCOT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "royallab/Pygmalion-2-13b-SuperCOT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/royallab/Pygmalion-2-13b-SuperCOT

SGLang

How to use royallab/Pygmalion-2-13b-SuperCOT 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 "royallab/Pygmalion-2-13b-SuperCOT" \
    --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": "royallab/Pygmalion-2-13b-SuperCOT",
		"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 "royallab/Pygmalion-2-13b-SuperCOT" \
        --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": "royallab/Pygmalion-2-13b-SuperCOT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use royallab/Pygmalion-2-13b-SuperCOT with Docker Model Runner:
```
docker model run hf.co/royallab/Pygmalion-2-13b-SuperCOT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card: Pygmalion-2-13b-SuperCOT

This is a merge between:

Pygmalion 2 13b
Ausboss's Llama2 SuperCOT loras at a weight of 1.00.

Quantizations provided by us and TheBloke:

The merge was performed by a commandline version of EzTrainer by CoffeeVampire/Blackroot via zaraki-tools by Zaraki.

The intended objective is to make Pygmalion-2 smarter and try to make it drift off less.

The SuperCOT lora was merged at a weight of 1.

Usage:

Since this is a merge between Pygmalion-2 and SuperCOT, the following instruction formats should work:

Metharme:

<|system|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<|user|>Start!<|model|>

Alpaca:

### Instruction:
Your instruction or question here.
### Response:

Bias, Risks, and Limitations

The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.

Training Details

This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.

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Safetensors

Model size

13B params

Tensor type

F16

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