Instructions to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers 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 meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

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 meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
./llama-cli -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

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 meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Use Docker

docker model run hf.co/meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

LM Studio
Jan

vLLM

How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Ollama
How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with Ollama:
```
ollama run hf.co/meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers
```
Unsloth Desktop

How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with Docker Model Runner:
```
docker model run hf.co/meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers
```

Lemonade

How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-layers" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

jamesdumay commited on 8 days ago

Commit

af48b33

1 Parent(s): c27196a

Add reviewed generation defaults (#1)

Browse files

- Add reviewed generation defaults (e81de61142e4a1d7b410f5512919e1969f3004db)

Files changed (1) hide show

model-package.json +21 -1

model-package.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "schema_version": 2,
-  "package_id": "sha256:09f630904d7b345ddc1477decb64f31d24dd4f930acd3baa5f3703c0bd01601e",
   "model_id": "unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:UD-Q4_K_XL",
   "source_model": {
     "sha256": "f22083eb6b15acb52905308ab083e8b0cc38897005cc45e8881abd164580aac2",
@@ -580,6 +580,26 @@
       }
     ]
   },
   "native_abi_version": "0.1.54",
   "generator_version": "0.76.0",
   "created_at_unix_secs": 1789252502

 {
   "schema_version": 2,
+  "package_id": "sha256:16d0f166cb550d01988dac2f06d8f11b995cb19c19769ebf67338cf160b9d2d4",
   "model_id": "unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:UD-Q4_K_XL",
   "source_model": {
     "sha256": "f22083eb6b15acb52905308ab083e8b0cc38897005cc45e8881abd164580aac2",
       }
     ]
   },
+  "generation": {
+    "request_defaults": {
+      "selection": {
+        "default": "recommended"
+      },
+      "profiles": {
+        "recommended": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "provenance": {
+            "source_repo": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
+            "revision": "2dc98e2afe4face0e4ce40972a915c45368bd34a",
+            "file": "README.md",
+            "section": "Quick Start",
+            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16/blob/2dc98e2afe4face0e4ce40972a915c45368bd34a/README.md"
+          }
+        }
+      }
+    }
+  },
   "native_abi_version": "0.1.54",
   "generator_version": "0.76.0",
   "created_at_unix_secs": 1789252502