Instructions to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Ollama:
```
ollama run hf.co/baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M
```
Unsloth Desktop

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

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": "baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Docker Model Runner:
```
docker model run hf.co/baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M
```

Lemonade

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nemotron-3-Super-120B-A12B-MINT-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

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 "baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M" \
  --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"

tomkay commited on Apr 16

Commit

c16952e

verified ·

1 Parent(s): 892ada6

Fix YAML metadata formatting

Browse files

Files changed (1) hide show

README.md +111 -120

README.md CHANGED Viewed

@@ -1,120 +1,111 @@
----
-library_name: gguf
-tags:
-- gguf
-- quantized
-license: other
-license_name: nvidia-open-model-license
-base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
-base_model_relation: quantized
----
-# Nemotron-3-Super-120B-A12B-MINT-GGUF
-GGUF quantized version of [nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16) optimised by [baa.ai](https://baa.ai).
-> Hybrid Mamba-MoE-Attention architecture (512 experts, 22 active per token) — Q4_K_M quantized for llama.cpp.
-## Files
-| File | Quant | Size |
-|------|-------|------|
-| `Nemotron-3-Super-120B-Q4_K_M.gguf` | Q4_K_M | 80 GB |
-## Metrics
-| Metric | Value |
-|--------|-------|
-| **Size** | **80 GB** |
-| Quantization | Q4_K_M |
-| Framework | llama.cpp (GGUF) |
-| Architecture | Hybrid Mamba-2 + MoE + Attention |
-| Parameters | 123.6B (12B active) |
-## Usage
-```bash
-llama-cli -m Nemotron-3-Super-120B-Q4_K_M.gguf \
-    -p "Hello!" -n 256 --threads 8
-```
-## Notes
-- Requires **llama.cpp build ≥ 8500** for NemotronH MoE + latent projection support
-- Peak memory: ~85 GB
-- MTP (Multi-Token Prediction) layers are stripped during conversion
----
-*Quantized by [baa.ai](https://baa.ai)*
----
-## Black Sheep AI Products
-**[Shepherd](https://baa.ai/shepherd.html)** — Private AI deployment platform that shrinks frontier models by 50-60% through RAM compression, enabling enterprises to run sophisticated AI on single GPU instances or Apple Silicon hardware. Deploy in your VPC with zero data leaving your infrastructure. Includes CI/CD pipeline integration, fleet deployment across Apple Silicon clusters, air-gapped and sovereign deployment support, and multi-format export (MLX, GGUF). Annual cloud costs from ~$2,700 — or run on a Mac Studio for electricity only.
-**[Watchman](https://baa.ai/watchman.html)** — Capability audit and governance platform for compressed AI models. Know exactly what your quantized model can do before it goes live. Watchman predicts which capabilities survive compression in minutes — replacing weeks of benchmarking. Includes compliance-ready reporting for regulated industries, quality valley warnings for counterproductive memory allocations, instant regression diagnosis tracing issues to specific tensors, and 22 adversarial security probes scanning for injection, leakage, hallucination, and code vulnerabilities.
-Learn more at **[baa.ai](https://baa.ai)** — Sovereign AI.

+---
+library_name: gguf
+tags:
+- gguf
+- quantized
+license: other
+license_name: nvidia-open-model-license
+base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
+base_model_relation: quantized
+---
+# Nemotron-3-Super-120B-A12B-MINT-GGUF
+GGUF quantized version of [nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16) optimised by [baa.ai](https://baa.ai).
+> Hybrid Mamba-MoE-Attention architecture (512 experts, 22 active per token) — Q4_K_M quantized for llama.cpp.
+## Files
+| File | Quant | Size |
+|------|-------|------|
+| `Nemotron-3-Super-120B-Q4_K_M.gguf` | Q4_K_M | 80 GB |
+## Metrics
+| Metric | Value |
+|--------|-------|
+| **Size** | **80 GB** |
+| Quantization | Q4_K_M |
+| Framework | llama.cpp (GGUF) |
+| Architecture | Hybrid Mamba-2 + MoE + Attention |
+| Parameters | 123.6B (12B active) |
+## Usage
+```bash
+llama-cli -m Nemotron-3-Super-120B-Q4_K_M.gguf \
+    -p "Hello!" -n 256 --threads 8
+```
+## Notes
+- Requires **llama.cpp build ≥ 8500** for NemotronH MoE + latent projection support
+- Peak memory: ~85 GB
+- MTP (Multi-Token Prediction) layers are stripped during conversion
+---
+*Quantized by [baa.ai](https://baa.ai)*
+---
+## Black Sheep AI Products
+**[Shepherd](https://baa.ai/shepherd.html)** — Private AI deployment platform that shrinks frontier models by 50-60% through RAM compression, enabling enterprises to run sophisticated AI on single GPU instances or Apple Silicon hardware. Deploy in your VPC with zero data leaving your infrastructure. Includes CI/CD pipeline integration, fleet deployment across Apple Silicon clusters, air-gapped and sovereign deployment support, and multi-format export (MLX, GGUF). Annual cloud costs from ~$2,700 — or run on a Mac Studio for electricity only.
+**[Watchman](https://baa.ai/watchman.html)** — Capability audit and governance platform for compressed AI models. Know exactly what your quantized model can do before it goes live. Watchman predicts which capabilities survive compression in minutes — replacing weeks of benchmarking. Includes compliance-ready reporting for regulated industries, quality valley warnings for counterproductive memory allocations, instant regression diagnosis tracing issues to specific tensors, and 22 adversarial security probes scanning for injection, leakage, hallucination, and code vulnerabilities.
+Learn more at **[baa.ai](https://baa.ai)** — Sovereign AI.