Instructions to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF 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 ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

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 ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

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 ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

SGLang

How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF 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 "ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF" \
    --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": "ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF",
		"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 "ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF" \
        --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": "ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF with Ollama:
```
ollama run hf.co/ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K
```
Unsloth Desktop
Docker Model Runner
How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K
```

Lemonade

How to use ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ijohn07/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF-Q6_K

List all available models

lemonade list

Atomic Chat

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-Q6_K-GGUF / nvidia-nemotron-3.5-lightning-30b-a3b-bf16-q6_k.gguf

ijohn07

Upload nvidia-nemotron-3.5-lightning-30b-a3b-bf16-q6_k.gguf with huggingface_hub

69b983b verified about 1 month ago

Download

History Blame GGUF Editor new Delete

33.5 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 8b0978235f6d713918c7ca90468a2d74e93fb421d04844ececa5854f34c492c6
Size of remote file:: 33.5 GB
SHA256:: b9a0e0eefa9e2a0e8029947b63a9fdccf9874cbd9b0af00651567b2756434cca

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.