Instructions to use hancheolp/nemo_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hancheolp/nemo_final 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("hancheolp/nemo_final") 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 hancheolp/nemo_final with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "hancheolp/nemo_final"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "hancheolp/nemo_final" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use hancheolp/nemo_final with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "hancheolp/nemo_final"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "hancheolp/nemo_final" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hancheolp/nemo_final", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use hancheolp/nemo_final 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 "hancheolp/nemo_final"
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 hancheolp/nemo_final
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hancheolp/nemo_final with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "hancheolp/nemo_final"
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 "hancheolp/nemo_final" \ --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"
Nemotron 3.5 Lightning 30B-A3B ternary (MLX 2-bit)
Ternary (1.58-bit) Nemotron 3.5 Lightning 30B-A3B. Same module layout as the mlx-community 4-bit release: the MoE router and every norm, A_log, D, dt_bias and conv1d stay full precision, and the experts ship pre-stacked as switch_mlp.fc1/fc2. The body weights are ternary. Embeddings are affine 4-bit and lm_head is affine 8-bit: the chat stop token's row carries a third of the median norm, which puts it in the worst 1% of rows at 4 bits.
Format
MLX native affine quantization, no custom kernel and no runtime shim:
bits 2
group_size 64
mode affine
levels {0, 1, 2} level 3 is unused
bias == -scale so dequantisation is scale * (q - 1) = {-a, 0, +a}
There is no rotation anywhere in this model, so there is no signs tensor and
no Hadamard transform to apply at load time.
Load
from mlx_lm import load
model, tokenizer = load("<repo>")
The container is stock MLX, but the architecture still has to be implemented in your mlx-lm / mlx-vlm build for the full model to load.
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2-bit