Text Generation
Transformers
Safetensors
English
bananaall
novi
novi-micro
causal-lm
from-scratch
custom_code
Instructions to use SLM-Archive/Novi-Micro-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLM-Archive/Novi-Micro-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SLM-Archive/Novi-Micro-Base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SLM-Archive/Novi-Micro-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SLM-Archive/Novi-Micro-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SLM-Archive/Novi-Micro-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SLM-Archive/Novi-Micro-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SLM-Archive/Novi-Micro-Base
- SGLang
How to use SLM-Archive/Novi-Micro-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 "SLM-Archive/Novi-Micro-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": "SLM-Archive/Novi-Micro-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 "SLM-Archive/Novi-Micro-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": "SLM-Archive/Novi-Micro-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SLM-Archive/Novi-Micro-Base with Docker Model Runner:
docker model run hf.co/SLM-Archive/Novi-Micro-Base
Download config.json from SLM-Archive/Novi-Micro-Base: direct link, hf CLI and curl.
- Browser
- Download file 744 Bytes
-
https://huggingface.co/SLM-Archive/Novi-Micro-Base/resolve/main/config.json
- Command line
-
hf download hf://SLM-Archive/Novi-Micro-Base/config.json
-
curl -L -o config.json https://huggingface.co/SLM-Archive/Novi-Micro-Base/resolve/main/config.json
744 Bytes
| { | |
| "architecture_style": "bananamind2", | |
| "architectures": [ | |
| "BananaAllForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_bananaall.BananaAllConfig", | |
| "AutoModelForCausalLM": "modeling_bananaall.BananaAllForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "head_dim": 40, | |
| "hidden_size": 160, | |
| "intermediate_size": 864, | |
| "lft": false, | |
| "max_position_embeddings": 4096, | |
| "model_type": "bananaall", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 9, | |
| "num_key_value_heads": 2, | |
| "pad_token_id": 0, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 100000.0, | |
| "ternary": false, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.14.1", | |
| "use_cache": false, | |
| "vocab_size": 3840 | |
| } | |