Text Generation
MLX
Safetensors
English
NemotronH_Nano_Omni_Reasoning_V3
nemotron
multimodal
mamba2
Mixture of Experts
quantized
turboquant
apple-silicon
mlx-lm
text-tower-only
conversational
custom_code
3-bit
Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit 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-TurboQuant-MLX-3bit") 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
- Pi
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit 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-TurboQuant-MLX-3bit"
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-TurboQuant-MLX-3bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit 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-TurboQuant-MLX-3bit"
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-TurboQuant-MLX-3bit
Run Hermes
hermes
- OpenClaw new
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit 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-TurboQuant-MLX-3bit"
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-TurboQuant-MLX-3bit" \ --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-TurboQuant-MLX-3bit 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-TurboQuant-MLX-3bit"
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-TurboQuant-MLX-3bit" # 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-TurboQuant-MLX-3bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
docs: Tier 1 polish — frontmatter + quickstart + KV-root rewrite
Browse files
README.md
CHANGED
|
@@ -3,7 +3,13 @@ license: other
|
|
| 3 |
license_name: nvidia-open-model-license
|
| 4 |
license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
|
| 5 |
base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
|
| 6 |
-
tags: [nemotron, multimodal, mamba2, moe, quantized, turboquant, mlx
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 7 |
---
|
| 8 |
|
| 9 |
# Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant MLX 3-bit
|
|
@@ -21,6 +27,34 @@ see [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit-TQ-KV`
|
|
| 21 |
For the runtime KV-cache modifier itself, see
|
| 22 |
[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant).
|
| 23 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 24 |
## Modality matrix
|
| 25 |
|
| 26 |
| Modality | Encoder | Quantization in this variant |
|
|
|
|
| 3 |
license_name: nvidia-open-model-license
|
| 4 |
license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
|
| 5 |
base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
|
| 6 |
+
tags: [nemotron, multimodal, mamba2, moe, quantized, turboquant, mlx, apple-silicon,
|
| 7 |
+
mlx-lm, text-tower-only]
|
| 8 |
+
library_name: mlx
|
| 9 |
+
pipeline_tag: text-generation
|
| 10 |
+
language: [en]
|
| 11 |
+
datasets: [nvidia/Nemotron-Image-Training-v3]
|
| 12 |
+
inference: false
|
| 13 |
---
|
| 14 |
|
| 15 |
# Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant MLX 3-bit
|
|
|
|
| 27 |
For the runtime KV-cache modifier itself, see
|
| 28 |
[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant).
|
| 29 |
|
| 30 |
+
## Quickstart
|
| 31 |
+
|
| 32 |
+
```python
|
| 33 |
+
# Today (mlx-lm 0.31.x): the NemotronH_Nano_Omni_Reasoning_V3 model class
|
| 34 |
+
# is not yet registered in mlx-lm. The cell below is the API shape that WILL
|
| 35 |
+
# work once upstream lands the class (track ml-explore/mlx-lm#386).
|
| 36 |
+
|
| 37 |
+
from mlx_lm import load, generate
|
| 38 |
+
|
| 39 |
+
model, tokenizer = load("majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit")
|
| 40 |
+
|
| 41 |
+
prompt = tokenizer.apply_chat_template(
|
| 42 |
+
[{"role": "user", "content": "Solve: 17 * 23"}],
|
| 43 |
+
add_generation_prompt=True,
|
| 44 |
+
enable_thinking=False, # set True to enable extended reasoning (default)
|
| 45 |
+
)
|
| 46 |
+
|
| 47 |
+
response = generate(
|
| 48 |
+
model, tokenizer,
|
| 49 |
+
prompt=prompt,
|
| 50 |
+
max_tokens=512,
|
| 51 |
+
sampler=lambda x: x.argmax(axis=-1), # or use mlx_lm.sample_utils.make_sampler(temp=0.6, top_p=0.95)
|
| 52 |
+
)
|
| 53 |
+
print(response)
|
| 54 |
+
```
|
| 55 |
+
|
| 56 |
+
> ⚠️ This variant covers the **text tower only**. For multimodal inference (vision + audio + video), use the GGUF variants with `llama-mtmd-cli` — see the GGUF cards in this family.
|
| 57 |
+
|
| 58 |
## Modality matrix
|
| 59 |
|
| 60 |
| Modality | Encoder | Quantization in this variant |
|