Instructions to use plasmova/nova-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use plasmova/nova-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="plasmova/nova-v3", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("plasmova/nova-v3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use plasmova/nova-v3 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf plasmova/nova-v3:F16
# Run inference directly in the terminal:
llama cli -hf plasmova/nova-v3:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf plasmova/nova-v3:F16
# Run inference directly in the terminal:
llama cli -hf plasmova/nova-v3:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf plasmova/nova-v3:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf plasmova/nova-v3:F16

Use Docker

docker model run hf.co/plasmova/nova-v3:F16

Llama new
LM Studio
Jan

vLLM

How to use plasmova/nova-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "plasmova/nova-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "plasmova/nova-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/plasmova/nova-v3:F16

SGLang

How to use plasmova/nova-v3 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 "plasmova/nova-v3" \
    --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": "plasmova/nova-v3",
		"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 "plasmova/nova-v3" \
        --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": "plasmova/nova-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use plasmova/nova-v3 with Ollama:
```
ollama run hf.co/plasmova/nova-v3:F16
```
Unsloth Desktop
Docker Model Runner
How to use plasmova/nova-v3 with Docker Model Runner:
```
docker model run hf.co/plasmova/nova-v3:F16
```

Lemonade

How to use plasmova/nova-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull plasmova/nova-v3:F16

Run and chat with the model

lemonade run user.nova-v3-F16

List all available models

lemonade list

Atomic Chat

nova-v3 / configuration_nova.py

plasmova

Upload Nova v3 Transformers model

1eef4eb verified about 1 month ago

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909 Bytes

from transformers import PretrainedConfig


class NovaConfig(PretrainedConfig):
    model_type = "nova"

    def __init__(
        self,
        vocab_size=32768,
        hidden_size=640,
        num_hidden_layers=26,
        num_attention_heads=10,
        num_key_value_heads=5,
        head_dim=64,
        intermediate_size=1920,
        max_position_embeddings=2048,
        rope_theta=100000.0,
        **kwargs,
    ):
        super().__init__(tie_word_embeddings=True, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.head_dim = head_dim
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta

	from transformers import PretrainedConfig


	class NovaConfig(PretrainedConfig):
	model_type = "nova"

	def __init__(
	self,
	vocab_size=32768,
	hidden_size=640,
	num_hidden_layers=26,
	num_attention_heads=10,
	num_key_value_heads=5,
	head_dim=64,
	intermediate_size=1920,
	max_position_embeddings=2048,
	rope_theta=100000.0,
	**kwargs,
	):
	super().__init__(tie_word_embeddings=True, **kwargs)
	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.num_key_value_heads = num_key_value_heads
	self.head_dim = head_dim
	self.intermediate_size = intermediate_size
	self.max_position_embeddings = max_position_embeddings
	self.rope_theta = rope_theta