Instructions to use CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2")
model = AutoModelForCausalLM.from_pretrained("CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2

SGLang

How to use CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2 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 "CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2" \
    --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": "CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2",
		"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 "CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2" \
        --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": "CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/CorticalStack/pastiche-crown-clown-7b-dare-6.5bpw-exl2
```

pastiche-crown-clown-7b-dare-6.5bpw-exl2 / hidden_states.safetensors

CorticalStack

Upload folder using huggingface_hub

6dcc447 verified over 2 years ago

Download

History Blame Contribute Delete

1.68 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: e5e980aeccbf1d6658caa7932a8891b7bf435810b69c4e45bf92d8719f2d22b5
Size of remote file:: 1.68 GB
SHA256:: 38a17f63b826ac183149cbe1f0bdb6da649969a9192c662db61cd0d59dcb2137

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.