Text Generation
Transformers
Safetensors
English
gpt2
novi
novi-nano
causal-lm
from-scratch
text-generation-inference
Instructions to use Novi-AI/Novi-Nano-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Novi-AI/Novi-Nano-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Novi-AI/Novi-Nano-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Novi-AI/Novi-Nano-Base") model = AutoModelForCausalLM.from_pretrained("Novi-AI/Novi-Nano-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Novi-AI/Novi-Nano-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Novi-AI/Novi-Nano-Base" # 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-Nano-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Novi-AI/Novi-Nano-Base
- SGLang
How to use Novi-AI/Novi-Nano-Base 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-Nano-Base" \ --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-Nano-Base", "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-Nano-Base" \ --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-Nano-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Novi-AI/Novi-Nano-Base with Docker Model Runner:
docker model run hf.co/Novi-AI/Novi-Nano-Base
Download training_config.json from Novi-AI/Novi-Nano-Base: direct link, hf CLI and curl.
- Browser
- Download file 611 Bytes
-
https://huggingface.co/Novi-AI/Novi-Nano-Base/resolve/main/training_config.json
- Command line
-
hf download hf://Novi-AI/Novi-Nano-Base/training_config.json
-
curl -L -o training_config.json https://huggingface.co/Novi-AI/Novi-Nano-Base/resolve/main/training_config.json
611 Bytes
| { | |
| "model": "Novi-Nano-Base", | |
| "version": 2, | |
| "vocab_size": 8192, | |
| "block_size": 256, | |
| "n_embd": 96, | |
| "n_layer": 4, | |
| "n_head": 4, | |
| "n_inner": 384, | |
| "parameters": 1258560, | |
| "training_tokens": 300000000, | |
| "tokens_seen": 300023808, | |
| "optimizer_steps": 2289, | |
| "batch_size": 256, | |
| "grad_accum_steps": 2, | |
| "learning_rate": 0.0003, | |
| "min_learning_rate": 3e-05, | |
| "warmup_steps": 500, | |
| "weight_decay": 0.1, | |
| "grad_clip": 1.0, | |
| "validation_tokens": 5000000, | |
| "final_validation_loss": 5.418698577137737, | |
| "final_validation_perplexity": 225.5853494508474, | |
| "best_validation_loss": 5.418698577137737 | |
| } |