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 Apr 14

Commit

fae218e

verified ·

1 Parent(s): de51832

Add model card

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README.md +95 -0

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	@@ -0,0 +1,95 @@

+---
+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
+**2-bit weight-quantized MLX version** of [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) with TurboQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. Maximum compression for running on memory-constrained devices. The dense hybrid Mamba-2 + Attention architecture supports up to 262K context length.
+Approximate model size: **~1.2 GB**
+## Model Specifications
+| Property | Value |
+|---|---|
+| **Base Model** | [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) |
+| **Parameters** | 4 billion (dense) |
+| **Architecture** | Hybrid Mamba-2 + Attention (dense) |
+| **Context Length** | 262,144 tokens (262K) |
+| **License** | NVIDIA Open Model License (commercial use OK) |
+| **Weight Quantization** | 2-bit (~1.2 GB) |
+| **KV-Cache Quantization** | TurboQuant |
+| **Framework** | MLX (Apple Silicon) |
+## Quickstart
+```python
+from mlx_lm import load, generate
+from turboquant import TurboQuantCache
+model, tokenizer = load("majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-2bit")
+prompt = "Explain the theory of relativity."
+response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
+print(response)
+```
+## What is TurboQuant?
+TurboQuant ([arXiv: 2504.19874](https://arxiv.org/abs/2504.19874)) is a KV-cache quantization technique that compresses the key-value cache used during autoregressive generation. Combined with 2-bit weight quantization in MLX, this provides a dual compression strategy: smaller model weights plus compressed KV cache for efficient long-context generation.
+Key benefits:
+- **No weight modification** -- model weights stay at original precision
+- **Reduced inference memory** -- KV cache is compressed significantly
+- **Longer context windows** -- fit more tokens in the same GPU memory
+- **Minimal quality loss** -- carefully designed quantization preserves generation quality
+## KV-Cache Quantization Comparison
+| Method | Prefill Speed | Decode Speed | Memory Savings | Reference |
+|---|---|---|---|---|
+| **TurboQuant** | 1x (baseline) | 1x (baseline) | High | [arXiv: 2504.19874](https://arxiv.org/abs/2504.19874) |
+| **RotorQuant** | **5.3x faster** | **28% faster** | High | [GitHub](https://github.com/scrya-com/rotorquant) |
+## Memory Estimates (Nemotron-3-Nano-4B)
+| Precision | Approximate Size | MLX Variant |
+|---|---|---|
+| BF16 (original) | ~8 GB | -- |
+| 8-bit quantized | ~4 GB | [TurboQuant-MLX-8bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-8bit) |
+| 4-bit quantized | ~2.3 GB | [TurboQuant-MLX-4bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-4bit) |
+| **2-bit quantized** | **~1.2 GB** | **This model** |
+## Hardware Requirements
+This model requires approximately 1.2 GB of unified memory. Recommended hardware:
+- Apple M1 (8 GB+)
+- Apple M2 (8 GB+)
+- Apple M3 (8 GB+)
+- Apple M4 (8 GB+)
+- Any Apple Silicon Mac with 8 GB+ unified memory
+## See Also
+- [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) -- Base model
+- [majentik/Nemotron-3-Nano-4B-TurboQuant](https://huggingface.co/majentik/Nemotron-3-Nano-4B-TurboQuant) -- TurboQuant KV-cache only (transformers)
+- [majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-8bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-8bit) -- MLX 8-bit variant
+- [majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-TurboQuant-MLX-4bit) -- MLX 4-bit variant
+- [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)