Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit"

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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit

Run Hermes

hermes

OpenClaw new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit"

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 "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

MLX LM

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

majentik commited on 11 days ago

Commit

7751a17

verified ·

1 Parent(s): 783cf24

Card accuracy sweep: honest brand labeling, remove dead links, upstream KV tip

Browse files

Files changed (1) hide show

README.md +2 -58

README.md CHANGED Viewed

@@ -40,9 +40,8 @@ encoders are NOT included — MLX-VLM Nemotron-Omni model class is **pending ups
 `llama-mtmd-cli` instead.
 For the matched-KV stack — RotorQuant weights + RotorQuant KV-cache modifier —
-see [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit-RQ-KV`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-2bit-RQ-KV).
 For the runtime KV-cache modifier itself, see
-[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant).
 ## Quickstart
@@ -122,59 +121,4 @@ variant card exists — this is a runtime flag, not a model variant.
 | Variant | Runtime | Approx size | Use case |
 |---|---|---|---|
-| [mmproj-F16](https://huggingface.co/majentik/nemotron3-nano-omni-30b-mmproj-f16) | llama-mtmd-cli | ~1-2 GB | Multimodal projector (pair with any GGUF) |
-| [RotorQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
-| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-IQ4_XS) | llama.cpp | ~26 GB | Lossy 4-bit, low-RAM CPU/edge |
-| [RotorQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-MXFP4_MOE) | llama.cpp | ~30 GB | MXFP4 MoE quant |
-| [RotorQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q2_K) | llama.cpp | ~18 GB | Lossy, low-RAM CPU/edge |
-| [RotorQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q3_K_M) | llama.cpp | ~23 GB | Smaller 3-bit, CPU-friendly |
-| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q4_K_M) | llama.cpp | ~33 GB | Balanced default |
-| [RotorQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q5_K_M) | llama.cpp | ~40 GB | Higher fidelity, more RAM |
-| [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q8_0) | llama.cpp | ~63 GB | Near-lossless reference |
-| [RotorQuant-GGUF-IQ4_XS-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-iq4_xs-rq-kv) | llama.cpp | ~26 GB | IQ4_XS + RotorQuant KV |
-| [RotorQuant-GGUF-MXFP4_MOE-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-mxfp4_moe-rq-kv) | llama.cpp | ~30 GB | MXFP4 MoE + RotorQuant KV |
-| [RotorQuant-GGUF-Q2_K-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q2_k-rq-kv) | llama.cpp | ~18 GB | Q2_K + RotorQuant KV |
-| [RotorQuant-GGUF-Q3_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q3_k_m-rq-kv) | llama.cpp | ~23 GB | Q3_K_M + RotorQuant KV |
-| [RotorQuant-GGUF-Q4_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q4_k_m-rq-kv) | llama.cpp | ~33 GB | Q4_K_M + RotorQuant KV |
-| [RotorQuant-GGUF-Q5_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q5_k_m-rq-kv) | llama.cpp | ~40 GB | Q5_K_M + RotorQuant KV |
-| [RotorQuant-GGUF-Q8_0-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q8_0-rq-kv) | llama.cpp | ~63 GB | Q8_0 + RotorQuant KV |
-| **RotorQuant-MLX-2bit** | mlx-lm | ~9.6 GB | Apple Silicon, smallest |
-| [RotorQuant-MLX-2bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit-rq-kv) | mlx-lm | ~9.6 GB | 2-bit + RotorQuant KV |
-| [RotorQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit) | mlx-lm | ~14 GB | Apple Silicon, small |
-| [RotorQuant-MLX-3bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit-rq-kv) | mlx-lm | ~14 GB | 3-bit + RotorQuant KV |
-| [RotorQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
-| [RotorQuant-MLX-4bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit-rq-kv) | mlx-lm | ~19 GB | 4-bit + RotorQuant KV |
-| [RotorQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit) | mlx-lm | ~23 GB | Apple Silicon, higher fidelity |
-| [RotorQuant-MLX-5bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit-rq-kv) | mlx-lm | ~23 GB | 5-bit + RotorQuant KV |
-| [RotorQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit) | mlx-lm | ~27 GB | Apple Silicon, near-lossless |
-| [RotorQuant-MLX-6bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit-rq-kv) | mlx-lm | ~27 GB | 6-bit + RotorQuant KV |
-| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit) | mlx-lm | ~35 GB | Apple Silicon reference |
-| [RotorQuant-MLX-8bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit-rq-kv) | mlx-lm | ~35 GB | 8-bit + RotorQuant KV |
-| [RotorQuant-MLX-MXFP4](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-mxfp4) | mlx-lm | ~19 GB | Apple Silicon MXFP4 |
-| [TurboQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
-| [TurboQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-IQ4_XS) | llama.cpp | ~26 GB | Lossy 4-bit, low-RAM CPU/edge |
-| [TurboQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-MXFP4_MOE) | llama.cpp | ~30 GB | MXFP4 MoE quant |
-| [TurboQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q2_K) | llama.cpp | ~18 GB | Lossy, low-RAM CPU/edge |
-| [TurboQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q3_K_M) | llama.cpp | ~23 GB | Smaller 3-bit, CPU-friendly |
-| [TurboQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q4_K_M) | llama.cpp | ~33 GB | Balanced default |
-| [TurboQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q5_K_M) | llama.cpp | ~40 GB | Higher fidelity, more RAM |
-| [TurboQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q8_0) | llama.cpp | ~63 GB | Near-lossless reference |
-| [TurboQuant-GGUF-IQ4_XS-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-iq4_xs-tq-kv) | llama.cpp | ~26 GB | IQ4_XS + TurboQuant KV |
-| [TurboQuant-GGUF-MXFP4_MOE-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-mxfp4_moe-tq-kv) | llama.cpp | ~30 GB | MXFP4 MoE + TurboQuant KV |
-| [TurboQuant-GGUF-Q2_K-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q2_k-tq-kv) | llama.cpp | ~18 GB | Q2_K + TurboQuant KV |
-| [TurboQuant-GGUF-Q3_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q3_k_m-tq-kv) | llama.cpp | ~23 GB | Q3_K_M + TurboQuant KV |
-| [TurboQuant-GGUF-Q4_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q4_k_m-tq-kv) | llama.cpp | ~33 GB | Q4_K_M + TurboQuant KV |
-| [TurboQuant-GGUF-Q5_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q5_k_m-tq-kv) | llama.cpp | ~40 GB | Q5_K_M + TurboQuant KV |
-| [TurboQuant-GGUF-Q8_0-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q8_0-tq-kv) | llama.cpp | ~63 GB | Q8_0 + TurboQuant KV |
-| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit) | mlx-lm | ~9.6 GB | Apple Silicon, smallest |
-| [TurboQuant-MLX-2bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit-tq-kv) | mlx-lm | ~9.6 GB | 2-bit + TurboQuant KV |
-| [TurboQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit) | mlx-lm | ~14 GB | Apple Silicon, small |
-| [TurboQuant-MLX-3bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit-tq-kv) | mlx-lm | ~14 GB | 3-bit + TurboQuant KV |
-| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
-| [TurboQuant-MLX-4bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit-tq-kv) | mlx-lm | ~19 GB | 4-bit + TurboQuant KV |
-| [TurboQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit) | mlx-lm | ~23 GB | Apple Silicon, higher fidelity |
-| [TurboQuant-MLX-5bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit-tq-kv) | mlx-lm | ~23 GB | 5-bit + TurboQuant KV |
-| [TurboQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit) | mlx-lm | ~27 GB | Apple Silicon, near-lossless |
-| [TurboQuant-MLX-6bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit-tq-kv) | mlx-lm | ~27 GB | 6-bit + TurboQuant KV |
-| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit) | mlx-lm | ~35 GB | Apple Silicon reference |
-| [TurboQuant-MLX-8bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit-tq-kv) | mlx-lm | ~35 GB | 8-bit + TurboQuant KV |

 `llama-mtmd-cli` instead.
 For the matched-KV stack — RotorQuant weights + RotorQuant KV-cache modifier —
 For the runtime KV-cache modifier itself, see
+`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant`.
 ## Quickstart
 | Variant | Runtime | Approx size | Use case |
 |---|---|---|---|
+| **RotorQuant-MLX-2bit** | mlx-lm | ~9.6 GB | Apple Silicon, smallest |