Instructions to use Hypersniper/The_OverThinker_Phi_1_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hypersniper/The_OverThinker_Phi_1_5 with Transformers:

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

pipe = pipeline("text-generation", model="Hypersniper/The_OverThinker_Phi_1_5", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Hypersniper/The_OverThinker_Phi_1_5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hypersniper/The_OverThinker_Phi_1_5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hypersniper/The_OverThinker_Phi_1_5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hypersniper/The_OverThinker_Phi_1_5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hypersniper/The_OverThinker_Phi_1_5

SGLang

How to use Hypersniper/The_OverThinker_Phi_1_5 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 "Hypersniper/The_OverThinker_Phi_1_5" \
    --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": "Hypersniper/The_OverThinker_Phi_1_5",
		"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 "Hypersniper/The_OverThinker_Phi_1_5" \
        --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": "Hypersniper/The_OverThinker_Phi_1_5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hypersniper/The_OverThinker_Phi_1_5 with Docker Model Runner:
```
docker model run hf.co/Hypersniper/The_OverThinker_Phi_1_5
```

The_OverThinker_Phi_1_5 / pytorch_model.bin

Hypersniper

Upload 11 files

45ffcd9 almost 3 years ago

History Blame Contribute Delete

2.84 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:: d56fc8b87249e073e9712de88ca98d4a9a24fc069a357d2e16f87132800eb62f
Size of remote file:: 2.84 GB
SHA256:: 324e44d394669786db3285c13b354306b6b1fc3cd6948d3e5d860a501adce930

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

Hypersniper
/

The_OverThinker_Phi_1_5

Detected Pickle imports (4)

Xet Pointer Details