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

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -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/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -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

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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "plasmova/nova-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "plasmova/nova-v3",
		"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 "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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "plasmova/nova-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

File size: 1,013 Bytes

{
  "harness": "lm-evaluation-harness 0.4.13",
  "fewshot": 0,
  "limit_per_task": 500,
  "device": "cpu",
  "nova_model": "plasmova/nova-v3",
  "baseline_model": "HuggingFaceTB/SmolLM2-135M",
  "comparison": {
    "hellaswag": {
      "metric": "acc_norm",
      "nova": 0.436,
      "smollm2": 0.442,
      "delta": -0.006000000000000005,
      "nova_stderr": 0.0221989546414768,
      "smollm2_stderr": 0.02223197069632112
    },
    "winogrande": {
      "acc": {
        "nova": 0.526,
        "smollm2": 0.52,
        "delta": 0.006000000000000005,
        "nova_stderr": 0.02235279165091416,
        "smollm2_stderr": 0.02236516042423134
      },
      "acc_norm": {
        "nova": 0.522,
        "smollm2": 0.506,
        "delta": 0.016000000000000014,
        "nova_stderr": 0.02236139673920787,
        "smollm2_stderr": 0.022381462412439324
      }
    }
  },
  "nova_evaluation_seconds": "452.16297420003684",
  "baseline_evaluation_seconds": "185.393080800015"
}