Text Generation
Transformers
Safetensors
English
gpt2
novi
novi-nano
novi-nano-instruct
causal-lm
from-scratch
instruction-tuning
chatml
conversational
text-generation-inference
Instructions to use SLM-Archive/Novi-Nano-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLM-Archive/Novi-Nano-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SLM-Archive/Novi-Nano-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SLM-Archive/Novi-Nano-Instruct") model = AutoModelForCausalLM.from_pretrained("SLM-Archive/Novi-Nano-Instruct", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SLM-Archive/Novi-Nano-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SLM-Archive/Novi-Nano-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SLM-Archive/Novi-Nano-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SLM-Archive/Novi-Nano-Instruct
- SGLang
How to use SLM-Archive/Novi-Nano-Instruct 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 "SLM-Archive/Novi-Nano-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SLM-Archive/Novi-Nano-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "SLM-Archive/Novi-Nano-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SLM-Archive/Novi-Nano-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SLM-Archive/Novi-Nano-Instruct with Docker Model Runner:
docker model run hf.co/SLM-Archive/Novi-Nano-Instruct
Download banner.jpg from SLM-Archive/Novi-Nano-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 468 kB
-
https://huggingface.co/SLM-Archive/Novi-Nano-Instruct/resolve/main/banner.jpg
- Command line
-
hf download hf://SLM-Archive/Novi-Nano-Instruct/banner.jpg
-
curl -L -o banner.jpg https://huggingface.co/SLM-Archive/Novi-Nano-Instruct/resolve/main/banner.jpg
468 kB

- Xet hash:
- ed9955cece00d2b793785bd3040c87fedc0d9925a3a2c89f2e26ed63f606a88b
- Size of remote file:
- 468 kB
- SHA256:
- f6f75251f28bb4b99204c96e90c9754c246cd96d377dcb97134baeb4850d4625
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.