Text Generation
Transformers
Safetensors
Korean
English
nova_gdn2
nova
gdn2
gated-deltanet
hybrid
korean
custom_code
Instructions to use Insightnova/Nova-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Insightnova/Nova-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Insightnova/Nova-1B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Insightnova/Nova-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Insightnova/Nova-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Insightnova/Nova-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Insightnova/Nova-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Insightnova/Nova-1B
- SGLang
How to use Insightnova/Nova-1B 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 "Insightnova/Nova-1B" \ --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": "Insightnova/Nova-1B", "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 "Insightnova/Nova-1B" \ --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": "Insightnova/Nova-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Insightnova/Nova-1B with Docker Model Runner:
docker model run hf.co/Insightnova/Nova-1B
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"architecture_options": {
"attention_backend": "flash_2",
"attention_bias": false,
"attn_qk_norm": true,
"d_model": 1536,
"enable_swa": true,
"ffn_bias": false,
"ffn_hidden": 6144,
"gdn_allow_neg_eigval": true,
"gdn_conv_bias": false,
"gdn_conv_size": 4,
"gdn_expand_v": 2.0,
"head_dim": 128,
"layer_norm_bias": false,
"layer_norm_eps": 1e-05,
"lm_head_bias": false,
"n_heads": 12,
"n_kv_heads": 4,
"n_layers": 26,
"rope_full_precision": true,
"rope_theta": 500000,
"swa_full_attention_first_layer": false,
"swa_full_attention_last_layer": false,
"swa_window": 4096,
"tie_word_embeddings": true
},
"architecture_preset": "main",
"architectures": [
"NovaForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_nova.NovaConfig",
"AutoModel": "modeling_nova.NovaModel",
"AutoModelForCausalLM": "modeling_nova.NovaForCausalLM"
},
"bos_token_id": 1,
"compute_dtype": "bfloat16",
"dtype": "float32",
"eos_token_id": 2,
"fast_decode": true,
"generation_context_policy": "remaining",
"hidden_size": 1536,
"inference_backend": "torch",
"max_position_embeddings": 8192,
"model_type": "nova_gdn2",
"num_hidden_layers": 26,
"pad_token_id": 0,
"tokenizer_vocab_size": 65536,
"transformers_version": "4.57.6",
"use_cache": false,
"vocab_size": 65536
}
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