Instructions to use mlx-community/Nex-N2-Pro-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Nex-N2-Pro-mlx-8bit 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("mlx-community/Nex-N2-Pro-mlx-8bit")

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 mlx-community/Nex-N2-Pro-mlx-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Nex-N2-Pro-mlx-8bit"

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": "mlx-community/Nex-N2-Pro-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Nex-N2-Pro-mlx-8bit 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 "mlx-community/Nex-N2-Pro-mlx-8bit"

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 mlx-community/Nex-N2-Pro-mlx-8bit

Run Hermes

hermes

MLX LM

How to use mlx-community/Nex-N2-Pro-mlx-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Nex-N2-Pro-mlx-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Nex-N2-Pro-mlx-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Nex-N2-Pro-mlx-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Nex-N2-Pro-mlx-8bit

8-bit (affine, group-size 64) MLX quantization of nex-agi/Nex-N2-Pro (qwen3_5_moe, 397B-A17B). Produced with mlx_lm and verified serving distributed across a 4-node Apple-Silicon JACCL/RDMA cluster (coherent output, ~29 tok/s decode).

Highlights

Router precision preserved. Per the qwen3_5 quantization predicate, every layer's mlp.gate and mlp.shared_expert_gate are kept at 8-bit (the quantization map in config.json lists all 60 layers) — MoE routing stability is not degraded. A_log (GatedDeltaNet decay) stays fp32.
Tensor-parallel / pipeline ready. Runs under mlx_lm distributed serving (mlx.launch --backend jaccl); validated 4-node and 2-node.
Text serving. mlx_lm strips the vision tower at load and serves the text language_model (responses carry a separate reasoning field — it's a thinking model).

Use with MLX

pip install mlx-lm
mlx_lm.generate --model mlx-community/Nex-N2-Pro-mlx-8bit \
  --prompt "Write a Python function to merge two sorted lists." --max-tokens 512

Distributed (hostfile with the JACCL RDMA device matrix, per the MLX docs):

mlx.launch --backend jaccl --hostfile hostfile.json -- \
  python -m mlx_lm server --model mlx-community/Nex-N2-Pro-mlx-8bit --port 8080

Quantization details


Method	MLX affine (`mlx_lm`)
Bits	8
Group size	64
Router / shared-expert gates	kept 8-bit (predicate)
Size on disk	~392 GB (91 shards)
Architecture	qwen3_5_moe, 60 layers (45 GatedDeltaNet linear + 15 full-attention), 512 experts, 262K ctx

Quantized from the full-precision nex-agi/Nex-N2-Pro weights. A distillation-aware 4-bit (DWQ) variant is in progress. Apache-2.0, inherited from the base model.

Downloads last month: 238

Safetensors

Model size

396B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for mlx-community/Nex-N2-Pro-mlx-8bit

Base model

nex-agi/Nex-N2-Pro

Quantized

(29)

this model