Instructions to use digitalassistant-ai/Selling-Assistant-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digitalassistant-ai/Selling-Assistant-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="digitalassistant-ai/Selling-Assistant-V1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("digitalassistant-ai/Selling-Assistant-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use digitalassistant-ai/Selling-Assistant-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "digitalassistant-ai/Selling-Assistant-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "digitalassistant-ai/Selling-Assistant-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/digitalassistant-ai/Selling-Assistant-V1
- SGLang
How to use digitalassistant-ai/Selling-Assistant-V1 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 "digitalassistant-ai/Selling-Assistant-V1" \ --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": "digitalassistant-ai/Selling-Assistant-V1", "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 "digitalassistant-ai/Selling-Assistant-V1" \ --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": "digitalassistant-ai/Selling-Assistant-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use digitalassistant-ai/Selling-Assistant-V1 with Docker Model Runner:
docker model run hf.co/digitalassistant-ai/Selling-Assistant-V1

- Xet hash:
- 5e11b363b3441f7e8760abed3ff2400fa9c5ef64cb8ab5ced33111df89c1b17e
- Size of remote file:
- 1.48 MB
- SHA256:
- 63c893e448b6664d535219f79b22473b26f1e3aba41e3469b90b7f93582104ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.