Instructions to use majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-MLX-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-MLX-2bit

Run Hermes

hermes

OpenClaw new

How to use majentik/Nemotron-3-Nano-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-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-4B-TurboQuant-MLX-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

majentik commited on May 6

Commit

14e1a9f

verified ·

1 Parent(s): 0f29d5d

docs: Tier 2 polish — variant matrix + quant trade-off

Browse files

Files changed (1) hide show

README.md +42 -11

README.md CHANGED Viewed

@@ -2,21 +2,19 @@
 base_model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
 library_name: mlx
 tags:
-- turboquant
-- kv-cache-quantization
-- nemotron
-- nvidia
-- mamba2
-- hybrid
-- quantized
-- mlx
-- 2bit
 license: other
 license_name: nvidia-open-model-license
 license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
 pipeline_tag: text-generation
-language:
-- en
 ---
 # Nemotron-3-Nano-4B - TurboQuant MLX 2-bit
@@ -95,3 +93,36 @@ This model requires approximately 1.2 GB of unified memory. Recommended hardware
 - [majentik/Nemotron-3-Nano-4B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-MLX-2bit) -- RotorQuant MLX 2-bit variant
 - [TurboQuant Paper (arXiv: 2504.19874)](https://arxiv.org/abs/2504.19874)
 - [MLX Framework](https://github.com/ml-explore/mlx)

 base_model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
 library_name: mlx
 tags:
+  - turboquant
+  - kv-cache-quantization
+  - nemotron
+  - nvidia
+  - mamba2
+  - hybrid
+  - quantized
+  - mlx
+  - 2bit
 license: other
 license_name: nvidia-open-model-license
 license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
 pipeline_tag: text-generation
 ---
 # Nemotron-3-Nano-4B - TurboQuant MLX 2-bit
 - [majentik/Nemotron-3-Nano-4B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-MLX-2bit) -- RotorQuant MLX 2-bit variant
 - [TurboQuant Paper (arXiv: 2504.19874)](https://arxiv.org/abs/2504.19874)
 - [MLX Framework](https://github.com/ml-explore/mlx)
+## Quant trade-off (MLX lane)
+| Bits | Approx size | Use case | Recommendation |
+|---|---|---|---|
+| **2-bit** | ~1.0 GB | Aggressive quantization | **Very low-RAM Macs** |
+| 3-bit | ~1.4 GB | Lossy but small | Low-RAM Macs |
+| 4-bit | ~1.7 GB | Balanced default | Recommended for most Macs |
+| 5-bit | ~2.0 GB | Higher fidelity | Quality-sensitive |
+| 6-bit | ~2.4 GB | Approaching FP16 quality | High-fidelity |
+| 8-bit | ~3.0 GB | Near-lossless reference | Fidelity-critical work |
+(Current variant — **2bit** — is bolded.)
+## Variants in this family
+(Showing 13 sibling variants under `majentik/nemotron3-nano-4b-*`. The current variant — `TurboQuant-MLX-2bit` — is **bolded**.)
+| Variant | Runtime | Approx size | Use case |
+|---|---|---|---|
+| [RotorQuant](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
+| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-IQ4_XS) | llama.cpp | ~3.4 GB | Lossy 4-bit, low-RAM CPU/edge |
+| [RotorQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-Q2_K) | llama.cpp | ~2.4 GB | Lossy, low-RAM CPU/edge |
+| [RotorQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-Q3_K_M) | llama.cpp | ~3.1 GB | Smaller 3-bit, CPU-friendly |
+| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-Q4_K_M) | llama.cpp | ~4.4 GB | Balanced default |
+| [RotorQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-mlx-2bit) | mlx-lm | ~1.3 GB | Apple Silicon, smallest |
+| [RotorQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-mlx-4bit) | mlx-lm | ~2.5 GB | Apple Silicon balanced |
+| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-mlx-8bit) | mlx-lm | ~4.7 GB | Apple Silicon reference |
+| [TurboQuant](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
+| [TurboQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-gguf-Q4_K_M) | llama.cpp | ~4.4 GB | Balanced default |
+| **TurboQuant-MLX-2bit** | mlx-lm | ~1.3 GB | Apple Silicon, smallest |
+| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-mlx-4bit) | mlx-lm | ~2.5 GB | Apple Silicon balanced |
+| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-mlx-8bit) | mlx-lm | ~4.7 GB | Apple Silicon reference |