Instructions to use netprojects/SenseNova-Loop-9B-Preview-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netprojects/SenseNova-Loop-9B-Preview-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("netprojects/SenseNova-Loop-9B-Preview-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 netprojects/SenseNova-Loop-9B-Preview-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 "netprojects/SenseNova-Loop-9B-Preview-MLX-8bit"

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": "netprojects/SenseNova-Loop-9B-Preview-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use netprojects/SenseNova-Loop-9B-Preview-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 "netprojects/SenseNova-Loop-9B-Preview-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "netprojects/SenseNova-Loop-9B-Preview-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": "netprojects/SenseNova-Loop-9B-Preview-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use netprojects/SenseNova-Loop-9B-Preview-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 "netprojects/SenseNova-Loop-9B-Preview-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 netprojects/SenseNova-Loop-9B-Preview-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use netprojects/SenseNova-Loop-9B-Preview-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "netprojects/SenseNova-Loop-9B-Preview-MLX-8bit"

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 "netprojects/SenseNova-Loop-9B-Preview-MLX-8bit" \
  --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"

SenseNova-Loop-9B-Preview-MLX-8bit / config.json

Paris99

SenseNova-Loop-9B-Preview (9937e91) MLX 8-bit: weights, MLX model code, card, Apache-2.0 LICENSE and NOTICE

a8e5503 verified about 10 hours ago

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3.21 kB

{
  "architectures": [
    "SenseNovaLoopForConditionalGeneration"
  ],
  "attn_implementation": "sdpa",
  "bos_token_id": null,
  "dtype": "bfloat16",
  "eos_token_id": [
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  "hidden_size": 4096,
  "image_token_id": 248056,
  "loop_settings": {
    "counts": [
      2
    ],
    "end": 28,
    "eval_count": 2,
    "start": 4
  },
  "mhc": {
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    "fixed_identity": false,
    "iterations": 20,
    "pre_scale": 1.0,
    "start": 4,
    "streams": 2
  },
  "model_name": "SenseNova-Loop-9B-Preview",
  "model_type": "sensenova_loop",
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  "patch_conv3d": true,
  "quantization": {
    "group_size": 64,
    "bits": 8,
    "mode": "affine"
  },
  "quantization_config": {
    "group_size": 64,
    "bits": 8,
    "mode": "affine"
  },
  "text_config": {
    "attention_bias": false,
    "attention_dropout": 0.0,
    "attn_output_gate": true,
    "bos_token_id": null,
    "dtype": "bfloat16",
    "eos_token_id": 248044,
    "full_attention_interval": 4,
    "head_dim": 256,
    "hidden_act": "silu",
    "hidden_size": 4096,
    "initializer_range": 0.02,
    "intermediate_size": 12288,
    "layer_types": [
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    "linear_num_key_heads": 16,
    "linear_num_value_heads": 32,
    "linear_value_head_dim": 128,
    "mamba_ssm_dtype": "float32",
    "max_position_embeddings": 262144,
    "mlp_only_layers": [],
    "model_type": "qwen3_5_text",
    "mtp_num_hidden_layers": 1,
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    "num_attention_heads": 16,
    "num_hidden_layers": 32,
    "num_key_value_heads": 4,
    "pad_token_id": 248044,
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    "rms_norm_eps": 1e-06,
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  "video_token_id": 248057,
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  "model_file": "sensenova_loop.py",
  "sensenova_mlx": {
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    "revision": "9937e91c7301b7dcbb5bf17e3e4e95c499a11e99",
    "text_only": true
  }
}

	{
	"architectures": [
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	"attn_implementation": "sdpa",
	"bos_token_id": null,
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	"loop_settings": {
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	"end": 28,
	"eval_count": 2,
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	"mhc": {
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	"fixed_identity": false,
	"iterations": 20,
	"pre_scale": 1.0,
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	"streams": 2
	},
	"model_name": "SenseNova-Loop-9B-Preview",
	"model_type": "sensenova_loop",
	"pad_token_id": 248044,
	"patch_conv3d": true,
	"quantization": {
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	"bits": 8,
	"mode": "affine"
	},
	"quantization_config": {
	"group_size": 64,
	"bits": 8,
	"mode": "affine"
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	"text_config": {
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attn_output_gate": true,
	"bos_token_id": null,
	"dtype": "bfloat16",
	"eos_token_id": 248044,
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	"mlp_only_layers": [],
	"model_type": "qwen3_5_text",
	"mtp_num_hidden_layers": 1,
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	"rms_norm_eps": 1e-06,
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	"mrope_section": [
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	}