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

Libraries

How to use NewstaR/Porpoise-6b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="NewstaR/Porpoise-6b-instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NewstaR/Porpoise-6b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("NewstaR/Porpoise-6b-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NewstaR/Porpoise-6b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NewstaR/Porpoise-6b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NewstaR/Porpoise-6b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NewstaR/Porpoise-6b-instruct

SGLang

How to use NewstaR/Porpoise-6b-instruct 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 "NewstaR/Porpoise-6b-instruct" \
    --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": "NewstaR/Porpoise-6b-instruct",
		"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 "NewstaR/Porpoise-6b-instruct" \
        --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": "NewstaR/Porpoise-6b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NewstaR/Porpoise-6b-instruct with Docker Model Runner:
```
docker model run hf.co/NewstaR/Porpoise-6b-instruct
```

This model is a finetuned version of the DeciLM-6b-instruct on the Dolphin GPT4 Dataset

Please set naive_attention_prefill to true when loading this model.

Example:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer

model_name = "NewstaR/Porpoise-6b-instruct"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    trust_remote_code=True,
    naive_attention_prefill=True,
)
model.config.use_cache = False

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Porpoise-6b-instruct

Datasets used to train NewstaR/Porpoise-6b-instruct