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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use royallab/Pygmalion-2-13b-SuperCOT-weighed with Docker Model Runner:
```
docker model run hf.co/royallab/Pygmalion-2-13b-SuperCOT-weighed
```

kingbri commited on Sep 17, 2023

Commit

6cc1794

1 Parent(s): 726c063

Update README.md

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@@ -15,6 +15,11 @@ This is an experimental weighted merge between:
 - [Pygmalion 2 13b](https://huggingface.co/PygmalionAI/pygmalion-2-13b)
 - [Ausboss's Llama2 SuperCOT loras](https://huggingface.co/ausboss/llama2-13b-supercot-loras)
 The merge was performed by a gradient merge script (apply-lora-weight-ltl.py) from [zaraki-tools](https://github.com/zarakiquemparte/zaraki-tools) by Zaraki.
 Thanks to Zaraki for the inspiration and help.

 - [Pygmalion 2 13b](https://huggingface.co/PygmalionAI/pygmalion-2-13b)
 - [Ausboss's Llama2 SuperCOT loras](https://huggingface.co/ausboss/llama2-13b-supercot-loras)
+Quantizations provided by us and TheBloke:
+- [Exl2](https://huggingface.co/royallab/Pygmalion-2-13b-SuperCOT-weighed-exl2)
+- [GPTQ](https://huggingface.co/TheBloke/Pygmalion-2-13B-SuperCOT-weighed-GPTQ)
+- [GGUF](https://huggingface.co/TheBloke/Pygmalion-2-13B-SuperCOT-weighed-GGUF)
 The merge was performed by a gradient merge script (apply-lora-weight-ltl.py) from [zaraki-tools](https://github.com/zarakiquemparte/zaraki-tools) by Zaraki.
 Thanks to Zaraki for the inspiration and help.