Instructions to use pipenetwork/GLM-5.2-MLX-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/GLM-5.2-MLX-nvfp4 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("pipenetwork/GLM-5.2-MLX-nvfp4")

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 pipenetwork/GLM-5.2-MLX-nvfp4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pipenetwork/GLM-5.2-MLX-nvfp4"

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": "pipenetwork/GLM-5.2-MLX-nvfp4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pipenetwork/GLM-5.2-MLX-nvfp4 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 "pipenetwork/GLM-5.2-MLX-nvfp4"

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 pipenetwork/GLM-5.2-MLX-nvfp4

Run Hermes

hermes

MLX LM

How to use pipenetwork/GLM-5.2-MLX-nvfp4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "pipenetwork/GLM-5.2-MLX-nvfp4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pipenetwork/GLM-5.2-MLX-nvfp4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "pipenetwork/GLM-5.2-MLX-nvfp4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

GLM-5.2-MLX-nvfp4

An MLX conversion of zai-org/GLM-5.2 quantized to NVFP4 (4-bit FP4, group size 16) for Apple Silicon with mlx-lm.

This is the MLX analog of NVIDIA's nvidia/GLM-5.2-NVFP4. NVIDIA's checkpoint stores weights in ModelOpt-packed NVFP4 that mlx-lm cannot read directly, so this build was produced by quantizing the bf16 base with MLX's own NVFP4 mode (--q-mode nvfp4 --q-group-size 16).

Base model: zai-org/GLM-5.2 (GlmMoeDsaForCausalLM, 753B total / ~40B active MoE, text-only)
Format: MLX, NVFP4 (4-bit FP4, group size 16)
Approx. size on disk: 390G
Converted with: mlx-lm 0.31.2

Usage

pip install -U mlx-lm
mlx_lm.generate --model pipenetwork/GLM-5.2-MLX-nvfp4 --prompt "Explain mixture-of-experts in one sentence." --max-tokens 128

License

MIT, inherited from the base model.

Downloads last month: 818

Safetensors

Model size

743B params

Tensor type

U32

BF16

F32

MLX

Hardware compatibility

4-bit

Model tree for pipenetwork/GLM-5.2-MLX-nvfp4

Base model

zai-org/GLM-5.2

Quantized

(70)

this model

Collection including pipenetwork/GLM-5.2-MLX-nvfp4

GLM-5.2 MLX

Collection

First MLX builds of zai-org/GLM-5.2 (glm_moe_dsa MoE): 4/5/6/8-bit + a 512GB-friendly mixed. • 6 items • Updated 3 days ago