Instructions to use PygmalionAI/pygmalion-6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PygmalionAI/pygmalion-6b with Transformers:

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

pipe = pipeline("text-generation", model="PygmalionAI/pygmalion-6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("PygmalionAI/pygmalion-6b")
model = AutoModelForMultimodalLM.from_pretrained("PygmalionAI/pygmalion-6b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PygmalionAI/pygmalion-6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PygmalionAI/pygmalion-6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PygmalionAI/pygmalion-6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PygmalionAI/pygmalion-6b

SGLang

How to use PygmalionAI/pygmalion-6b 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 "PygmalionAI/pygmalion-6b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PygmalionAI/pygmalion-6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "PygmalionAI/pygmalion-6b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PygmalionAI/pygmalion-6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PygmalionAI/pygmalion-6b with Docker Model Runner:
```
docker model run hf.co/PygmalionAI/pygmalion-6b
```

It should be a good choice to use bloom or bloomz to train the new pygmalion model?

#20

by win10 - opened Mar 14, 2023

Discussion

win10

Mar 14, 2023

Have you considered bloomz or bloom 560M, 1.1B, 1.7B, 3B models?
I have used bloom 1B1 to load the character card, it is quite coherent.

Minami-su

Mar 17, 2023

I personally think it would be better in llama

11b

Pygmalion org Mar 17, 2023

Very early into the project I gave Bloom a shot for the 560M and 1.1B models, but it performs worse than OPT and Pythia at similar model sizes if used for English conversation. As luhao said, LLaMA would likely be the better base model, but I'm still waiting for Meta to decide whether or not they're okay with people releasing fine-tunes before doing anything with it.

Chat-Error

Mar 19, 2023

I think you can train a LoRA for the LLaMA model like Alpaca did.

Minami-su

Mar 20, 2023

add rlhf

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment