Instructions to use BricksDisplay/phi-1_5-qnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BricksDisplay/phi-1_5-qnn with Transformers:

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

pipe = pipeline("text-generation", model="BricksDisplay/phi-1_5-qnn", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BricksDisplay/phi-1_5-qnn", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("BricksDisplay/phi-1_5-qnn", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BricksDisplay/phi-1_5-qnn with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BricksDisplay/phi-1_5-qnn"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BricksDisplay/phi-1_5-qnn",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BricksDisplay/phi-1_5-qnn

SGLang

How to use BricksDisplay/phi-1_5-qnn 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 "BricksDisplay/phi-1_5-qnn" \
    --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": "BricksDisplay/phi-1_5-qnn",
		"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 "BricksDisplay/phi-1_5-qnn" \
        --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": "BricksDisplay/phi-1_5-qnn",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BricksDisplay/phi-1_5-qnn with Docker Model Runner:
```
docker model run hf.co/BricksDisplay/phi-1_5-qnn
```

phi-1_5-qnn / onnx /decoder_model_merged_quantized.onnx_data

hans00

Upload folder using huggingface_hub

0debfd9 verified about 2 years ago

download

history blame contribute delete

5.67 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:: 63e5594e21556c8c4739d31f3ca225670f02227de9f6e6cd8100d8291b0f0c30
Size of remote file:: 5.67 GB
SHA256:: 50004afac42c8799ef4b0655ac9657e2bb59d2f41965475f75e456ff4aac4ea7

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