Instructions to use RichardErkhov/alpindale_-_pygmalion-instruct-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/alpindale_-_pygmalion-instruct-4bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/alpindale_-_pygmalion-instruct-4bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/alpindale_-_pygmalion-instruct-4bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/alpindale_-_pygmalion-instruct-4bits")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/alpindale_-_pygmalion-instruct-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/alpindale_-_pygmalion-instruct-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/alpindale_-_pygmalion-instruct-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/alpindale_-_pygmalion-instruct-4bits

SGLang

How to use RichardErkhov/alpindale_-_pygmalion-instruct-4bits 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 "RichardErkhov/alpindale_-_pygmalion-instruct-4bits" \
    --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": "RichardErkhov/alpindale_-_pygmalion-instruct-4bits",
		"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 "RichardErkhov/alpindale_-_pygmalion-instruct-4bits" \
        --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": "RichardErkhov/alpindale_-_pygmalion-instruct-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/alpindale_-_pygmalion-instruct-4bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/alpindale_-_pygmalion-instruct-4bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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pygmalion-instruct - bnb 4bits

Model creator: https://huggingface.co/alpindale/
Original model: https://huggingface.co/alpindale/pygmalion-instruct/

Original model description:

license: mit

Model Details

Experimental model. Trained with the Pygmalion and the WizardLM datasets.

The purpose of this model is to enable complex Instruct prompting but with the RP capabilties of Pygmalion.

Prompting format

instruction:
output:

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

The intended use-case is Role-Playing with Instruct prompts. Guiding the bot towards a certain conversation style should be easier this way. Subject to experimentation.

Out-of-Scope Use

Assistant Bot [subject to providing incorrect instructions]
Complex multi-character chat

Risks

The model can generate potentially harmful or NSFW outputs. Please use with caution.

Citation

WizardLM:

@misc{xu2023wizardlm,
      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, 
      author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
      year={2023},
      eprint={2304.12244},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F32

F16

Paper for RichardErkhov/alpindale_-_pygmalion-instruct-4bits

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Paper • 2304.12244 • Published Apr 24, 2023 • 14