Instructions to use ddh0/Pirouette-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ddh0/Pirouette-7b with Transformers:

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

pipe = pipeline("text-generation", model="ddh0/Pirouette-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ddh0/Pirouette-7b")
model = AutoModelForCausalLM.from_pretrained("ddh0/Pirouette-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ddh0/Pirouette-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ddh0/Pirouette-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ddh0/Pirouette-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ddh0/Pirouette-7b

SGLang

How to use ddh0/Pirouette-7b 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 "ddh0/Pirouette-7b" \
    --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": "ddh0/Pirouette-7b",
		"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 "ddh0/Pirouette-7b" \
        --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": "ddh0/Pirouette-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ddh0/Pirouette-7b with Docker Model Runner:
```
docker model run hf.co/ddh0/Pirouette-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Pirouette-7b

This is a gradient SLERP merge of Eric Hartford's dolphin-2.1-mistral-7b merged with Undi and IkariDev's Noromaid-v0.1.1-13b.

The goal of this merge is to retain most of the brain of Dolphin, with a little added flair from Noromaid.

The prompt format is Alpaca. You can use the standard format as shown, but for best results, I strongly recommend customizing the system prompt to your specific needs.

You might need to add Instruction: to your list of stopping strings, due to Dolphin not being quite familiar with Alpaca.

You are Pirouette, a personable and friendly AI assistant.

### Instruction:
{YOUR MESSAGE HERE}

### Response:
{BOT MESSAGE HERE}

Misc. information

BOS token is <s>
EOS token is </s>
Native context length is 8192
Functional context length extended to 32768 via RoPE with decreased perplexity, see here
Base model is Mistral v0.1

Thanks

Thanks to Eric Hartford for Dolphin
Thanks to Undi and IkariDev for Noromaid

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

F16

Model tree for ddh0/Pirouette-7b

Quantizations

3 models