Instructions to use abhilashms/GLM-5-MLX-4.8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhilashms/GLM-5-MLX-4.8bit 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("abhilashms/GLM-5-MLX-4.8bit")

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
LM Studio

Pi new

How to use abhilashms/GLM-5-MLX-4.8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abhilashms/GLM-5-MLX-4.8bit"

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": "abhilashms/GLM-5-MLX-4.8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use abhilashms/GLM-5-MLX-4.8bit 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 "abhilashms/GLM-5-MLX-4.8bit"

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 abhilashms/GLM-5-MLX-4.8bit

Run Hermes

hermes

MLX LM

How to use abhilashms/GLM-5-MLX-4.8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "abhilashms/GLM-5-MLX-4.8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "abhilashms/GLM-5-MLX-4.8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "abhilashms/GLM-5-MLX-4.8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

See GLM-5 MLX in action - demonstration video

Tested on a M3 Ultra 512GB RAM using Inferencer app v1.10

Single inference ~16.6 tokens/s @ 1000 tokens
Batched inference ~31.8 total tokens/s across six inferences
Memory usage: ~417 GiB

q4.8bit quant typically achieves 1.281 perplexity in our coding test

Quantization	Perplexity	Token Accuracy	Missed Divergence
q3.5	168.0	43.45%	72.57%
q4.5	1.33593	91.65%	27.61%
q4.8	1.28125	93.75%	21.15%
q5.5	1.23437	95.05%	17.28%
q6.5	1.21875	96.95%	12.03%
q8.5	1.21093	97.55%	10.50%
q9	1.21093	97.55%	10.50%
Base	1.20312	100.0%	0.000%

Perplexity: Measures the confidence for predicting base tokens (lower is better)
Token Accuracy: The percentage of correctly generated base tokens
Missed Divergence: Measures severity of misses; how much the token was missed by

Quantized with a modified version of MLX

For more details see demonstration video or visit GLM-5.

Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.

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Safetensors

Model size

744B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

6-bit

Model tree for abhilashms/GLM-5-MLX-4.8bit

Base model

zai-org/GLM-5

Quantized

(25)

this model