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 Novi-AI/Novi-Nano-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Novi-AI/Novi-Nano-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Novi-AI/Novi-Nano-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Novi-AI/Novi-Nano-Instruct") model = AutoModelForCausalLM.from_pretrained("Novi-AI/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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Novi-AI/Novi-Nano-Instruct 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-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": "Novi-AI/Novi-Nano-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Novi-AI/Novi-Nano-Instruct
- SGLang
How to use Novi-AI/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 "Novi-AI/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": "Novi-AI/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 "Novi-AI/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": "Novi-AI/Novi-Nano-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Novi-AI/Novi-Nano-Instruct with Docker Model Runner:
docker model run hf.co/Novi-AI/Novi-Nano-Instruct
Download training_info.json from Novi-AI/Novi-Nano-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 885 Bytes
-
https://huggingface.co/Novi-AI/Novi-Nano-Instruct/resolve/main/training_info.json
- Command line
-
hf download hf://Novi-AI/Novi-Nano-Instruct/training_info.json
-
curl -L -o training_info.json https://huggingface.co/Novi-AI/Novi-Nano-Instruct/resolve/main/training_info.json
885 Bytes
| { | |
| "model": "Novi-Nano-Instruct", | |
| "base_model": "Novi-AI/Novi-Nano-Base", | |
| "parameters": 1258848, | |
| "trainable_parameters": 1258848, | |
| "base_vocab_size": 8192, | |
| "final_vocab_size": 8195, | |
| "added_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>" | |
| ], | |
| "context_length": 256, | |
| "epochs": 5, | |
| "train_examples": 500, | |
| "validation_examples": 10, | |
| "batch_size": 16, | |
| "gradient_accumulation_steps": 2, | |
| "effective_batch_size": 32, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "scheduler": "cosine", | |
| "bf16": false, | |
| "fp16": false, | |
| "validation_loss": 5.153667449951172, | |
| "validation_perplexity": 173.0650352145914, | |
| "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}" | |
| } |