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"}
     ]
   }'

pipenetwork commited on 2 days ago

Commit

144d946

verified ·

1 Parent(s): 8c620f4

Add model card

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README.md +32 -3

README.md CHANGED Viewed

@@ -1,7 +1,36 @@
 ---
-language: en
 tags:
 - mlx
-pipeline_tag: text-generation
-library_name: mlx
 ---

 ---
+license: mit
+library_name: mlx
+pipeline_tag: text-generation
+language:
+- en
+base_model: zai-org/GLM-5.2
+base_model_relation: quantized
 tags:
 - mlx
+- glm_moe_dsa
+- moe
+- nvfp4
 ---
+# GLM-5.2-MLX-nvfp4
+An **MLX** conversion of [zai-org/GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) quantized to **NVFP4** (4-bit FP4, group size 16) for Apple Silicon with [mlx-lm](https://github.com/ml-explore/mlx-lm).
+This is the MLX analog of NVIDIA's [nvidia/GLM-5.2-NVFP4](https://huggingface.co/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](https://huggingface.co/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
+```bash
+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.