Instructions to use avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw 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("avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw")

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 avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw"

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": "avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw 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 "avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw"

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 avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw

Run Hermes

hermes

MLX LM

How to use avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "avlp12/GLM-5.2-Alis-MLX-Dynamic-2.56bpw",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

GLM-5.2-Alis-MLX-Dynamic-2.56bpw / model-00002-of-00046.safetensors

avlp12

Upload folder using huggingface_hub

dfdc979 verified 12 days ago

Download

History Blame Contribute Delete

5.15 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d1090a36761f010614ebf06b7fa60bfe629d18a94dfbe9a04d0be99fa763fc49
Size of remote file:: 5.15 GB
SHA256:: 641773aeaacc9b424f89160fe8acd6a3f61b311afb213aba822e5646e12d01cc

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.