Instructions to use pipenetwork/GLM-5.3-MLX-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/GLM-5.3-MLX-5bit 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-5.3-MLX-5bit")

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-5.3-MLX-5bit 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-5.3-MLX-5bit"

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": "pipenetwork/GLM-5.3-MLX-5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use pipenetwork/GLM-5.3-MLX-5bit 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-5.3-MLX-5bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pipenetwork/GLM-5.3-MLX-5bit"
# 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-5.3-MLX-5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use pipenetwork/GLM-5.3-MLX-5bit 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-5.3-MLX-5bit"

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-5.3-MLX-5bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pipenetwork/GLM-5.3-MLX-5bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pipenetwork/GLM-5.3-MLX-5bit"

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 "pipenetwork/GLM-5.3-MLX-5bit" \
  --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"

pipenetwork commited on 8 days ago

Commit

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@@ -46,7 +46,7 @@ tests: [https://github.com/PipeNetwork/glm53-mlx](https://github.com/PipeNetwork
 ## Size and what is quantized
-**511.5 GB** on disk. RAM: 768 GB (two machines).
 | group | share of parameters | this build |
 |---|---:|---|
@@ -84,7 +84,9 @@ that fit this machine, scored on identical windows:
 | [4bit](https://huggingface.co/pipenetwork/GLM-5.3-MLX-4bit) | 418.6 GB | 2.8636 [2.6681, 3.0714] |
 | [mixed-4_8bit](https://huggingface.co/pipenetwork/GLM-5.3-MLX-mixed-4_8bit) | 427.8 GB | 2.7420 [2.5533, 2.9477] |
 | [mixed-3_6bit](https://huggingface.co/pipenetwork/GLM-5.3-MLX-mixed-3_6bit) | 332.6 GB | 3.0338 [2.8366, 3.2386] |
-| [REAP50-4bit](https://huggingface.co/pipenetwork/GLM-5.3-REAP50-MLX-4bit) | 214.7 GB | 5.0295 [4.7571, 5.3137] |
 **Recommendation.** For a 512 GB Mac, **mixed 4/8-bit** (427.7 GB): perplexity 2.7420, a paired 4.3% better than uniform 4-bit (ratio 0.9575 [0.9537, 0.9612], better on 98.6% of windows) for 9 GB more — the 2.5% of non-expert weights are worth their 8 bits, as on every model we have measured. Uniform 4-bit (418.6 GB) is the fallback when those 9 GB matter. **Mixed 3/6-bit** (332.6 GB) is the 384 GB-class option, at a real cost: 3.0338, +5.9% over 4-bit and +10.6% over mixed 4/8 — it leads the ladder for the first ten layers and then 3-bit expert damage compounds. Among the builds that cannot be run here, the ladder puts 8-bit closest to bfloat16 (free-running error 0.131), then 6-bit (0.167); the **upstream FP8 release scores 0.173, between 6-bit and 5-bit**, which is why these are converted from the bf16 release. 5-bit (0.225) sits just above mixed 4/8 (0.250) at 100 GB more.

 ## Size and what is quantized
+**0.0 GB** on disk. RAM: 768 GB (two machines).
 | group | share of parameters | this build |
 |---|---:|---|
 | [4bit](https://huggingface.co/pipenetwork/GLM-5.3-MLX-4bit) | 418.6 GB | 2.8636 [2.6681, 3.0714] |
 | [mixed-4_8bit](https://huggingface.co/pipenetwork/GLM-5.3-MLX-mixed-4_8bit) | 427.8 GB | 2.7420 [2.5533, 2.9477] |
 | [mixed-3_6bit](https://huggingface.co/pipenetwork/GLM-5.3-MLX-mixed-3_6bit) | 332.6 GB | 3.0338 [2.8366, 3.2386] |
+| [REAP25-4bit](https://huggingface.co/pipenetwork/GLM-5.3-REAP25-MLX-4bit) | 0.0 GB | 3.2872 [3.0703, 3.5184] |
+| [REAP37-4bit](https://huggingface.co/pipenetwork/GLM-5.3-REAP37-MLX-4bit) | 0.0 GB | 3.8517 [3.6212, 4.0937] |
+| [REAP50-4bit](https://huggingface.co/pipenetwork/GLM-5.3-REAP50-MLX-4bit) | 0.0 GB | 5.0295 [4.7571, 5.3137] |
 **Recommendation.** For a 512 GB Mac, **mixed 4/8-bit** (427.7 GB): perplexity 2.7420, a paired 4.3% better than uniform 4-bit (ratio 0.9575 [0.9537, 0.9612], better on 98.6% of windows) for 9 GB more — the 2.5% of non-expert weights are worth their 8 bits, as on every model we have measured. Uniform 4-bit (418.6 GB) is the fallback when those 9 GB matter. **Mixed 3/6-bit** (332.6 GB) is the 384 GB-class option, at a real cost: 3.0338, +5.9% over 4-bit and +10.6% over mixed 4/8 — it leads the ladder for the first ten layers and then 3-bit expert damage compounds. Among the builds that cannot be run here, the ladder puts 8-bit closest to bfloat16 (free-running error 0.131), then 6-bit (0.167); the **upstream FP8 release scores 0.173, between 6-bit and 5-bit**, which is why these are converted from the bf16 release. 5-bit (0.225) sits just above mixed 4/8 (0.250) at 100 GB more.