Text Generation
Transformers
Safetensors
English
bananaall
novi-ai
language-model
pretrained-model
preview
small-language-model
bananamind-2-style
causal-lm
base-model
pretrained-from-scratch
project-kairo
triton
Instructions to use Novi-AI/Novi-Mini-Base-Preview-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Novi-AI/Novi-Mini-Base-Preview-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Novi-AI/Novi-Mini-Base-Preview-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Novi-AI/Novi-Mini-Base-Preview-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Novi-AI/Novi-Mini-Base-Preview-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Novi-AI/Novi-Mini-Base-Preview-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Novi-AI/Novi-Mini-Base-Preview-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Novi-AI/Novi-Mini-Base-Preview-1
- SGLang
How to use Novi-AI/Novi-Mini-Base-Preview-1 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 "Novi-AI/Novi-Mini-Base-Preview-1" \ --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": "Novi-AI/Novi-Mini-Base-Preview-1", "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 "Novi-AI/Novi-Mini-Base-Preview-1" \ --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": "Novi-AI/Novi-Mini-Base-Preview-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Novi-AI/Novi-Mini-Base-Preview-1 with Docker Model Runner:
docker model run hf.co/Novi-AI/Novi-Mini-Base-Preview-1
Download scheduler.pt from Novi-AI/Novi-Mini-Base-Preview-1: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/Novi-AI/Novi-Mini-Base-Preview-1/resolve/main/scheduler.pt
- Command line
-
hf download hf://Novi-AI/Novi-Mini-Base-Preview-1/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/Novi-AI/Novi-Mini-Base-Preview-1/resolve/main/scheduler.pt
1.47 kB
- Xet hash:
- 54539c0d7009057c3b62dad4a6aff36c8804b73061442d5990b695b267d2f905
- Size of remote file:
- 1.47 kB
- SHA256:
- 657a85bb994ac5ca760b87463452315ead6f22e39c7b8bead8996af9f11f6d58
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