Instructions to use Poly-Pixel/shrek-test-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Poly-Pixel/shrek-test-small with Transformers:

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

pipe = pipeline("text-generation", model="Poly-Pixel/shrek-test-small")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Poly-Pixel/shrek-test-small")
model = AutoModelForCausalLM.from_pretrained("Poly-Pixel/shrek-test-small", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Poly-Pixel/shrek-test-small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Poly-Pixel/shrek-test-small

SGLang

How to use Poly-Pixel/shrek-test-small 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 "Poly-Pixel/shrek-test-small" \
    --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": "Poly-Pixel/shrek-test-small",
		"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 "Poly-Pixel/shrek-test-small" \
        --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": "Poly-Pixel/shrek-test-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Poly-Pixel/shrek-test-small with Docker Model Runner:
```
docker model run hf.co/Poly-Pixel/shrek-test-small
```

shrek-test-small / pytorch_model.bin

poly

add model

f10ef3d about 5 years ago

History Blame Contribute Delete

510 MB

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:: 1ae6f084591743c508cee4bb531629a16311a2d0cce5c5d048874bcd14ba7ca2
Size of remote file:: 510 MB
SHA256:: fda93d4d9ae47bf62cad5e8afbbca42a9848a7add4ad4ee87c755af74fff6927

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