Instructions to use netgvarun2005/GPTTherapistDeepSpeedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netgvarun2005/GPTTherapistDeepSpeedModel with Transformers:

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

pipe = pipeline("text-generation", model="netgvarun2005/GPTTherapistDeepSpeedModel")

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

tokenizer = AutoTokenizer.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedModel")
model = AutoModelForCausalLM.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedModel", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use netgvarun2005/GPTTherapistDeepSpeedModel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "netgvarun2005/GPTTherapistDeepSpeedModel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "netgvarun2005/GPTTherapistDeepSpeedModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/netgvarun2005/GPTTherapistDeepSpeedModel

SGLang

How to use netgvarun2005/GPTTherapistDeepSpeedModel 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 "netgvarun2005/GPTTherapistDeepSpeedModel" \
    --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": "netgvarun2005/GPTTherapistDeepSpeedModel",
		"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 "netgvarun2005/GPTTherapistDeepSpeedModel" \
        --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": "netgvarun2005/GPTTherapistDeepSpeedModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use netgvarun2005/GPTTherapistDeepSpeedModel with Docker Model Runner:
```
docker model run hf.co/netgvarun2005/GPTTherapistDeepSpeedModel
```

GPTTherapistDeepSpeedModel / pytorch_model.bin

netgvarun2005

Upload 2 files

a9d7adf about 3 years ago

History Blame Contribute Delete

2.73 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:: aa8d9af485f4bc85dc15e1ee8da7a74472d7b4d0f099de89b0f29073bebd40f2
Size of remote file:: 2.73 GB
SHA256:: b3a71bb977070346df01a1144e8e58fffc61b895f8a0884256c6deaa51f98360

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