Instructions to use SciPhi/Sensei-7B-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SciPhi/Sensei-7B-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SciPhi/Sensei-7B-V1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SciPhi/Sensei-7B-V1") model = AutoModelForCausalLM.from_pretrained("SciPhi/Sensei-7B-V1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SciPhi/Sensei-7B-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SciPhi/Sensei-7B-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SciPhi/Sensei-7B-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SciPhi/Sensei-7B-V1
- SGLang
How to use SciPhi/Sensei-7B-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 "SciPhi/Sensei-7B-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": "SciPhi/Sensei-7B-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 "SciPhi/Sensei-7B-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": "SciPhi/Sensei-7B-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SciPhi/Sensei-7B-V1 with Docker Model Runner:
docker model run hf.co/SciPhi/Sensei-7B-V1
Sensei
#1
by aA34543534 - opened
Just an fyi. Model seems to work pretty well with my memory rag. Handles the persona well and handles the web results well in the built in system. (Mornings trigger a website view of NPR up first. Then discussions continue on the daily news and goal setting of the day.) Main thoughts that maybe a MOE system where the RAG part is just one of the experts. Will continue to evaluate the model.
that sounds like an exciting idea, would love to hear if anyone gets a chance to experiment.