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 17 days ago

Commit

daf21e2

verified ·

1 Parent(s): 14e1a9f

docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -17,6 +17,23 @@ license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-l
 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.

 pipeline_tag: text-generation
 ---
+> [!TIP]
+> **KV-cache quantization without any fork (recommended, 2026):** upstream
+> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
+> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
+> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
+> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
+> K and V types symmetric to stay on the fast fused Flash-Attention path.
+> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
+> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
+> which greatly improves low-bit KV quality (opt-out:
+> `LLAMA_ATTN_ROT_DISABLE=1`).
+>
+> The RotorQuant/TurboQuant fork flow below is **experimental/legacy**: the
+> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
+> is unmaintained relative to mainline. It is NOT required to use this model.
+<!-- kv-upstream-note -->
 # 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.