metadata
base_model: zai-org/GLM-5
library_name: mlx
license: mit
tags:
- mlx
- safetensors
- glm_moe_dsa
- conversational
- text-generation
- mxfp4
- quantized
language:
- en
- zh
mlx-community/GLM-5-MXFP4-Q8
This model was converted to MLX format from zai-org/GLM-5 using a custom MXFP4-Q8 quantization scheme.
GLM-5 is a 744B parameter (40B active) Mixture-of-Experts model developed by Z.ai, targeting complex systems engineering and long-horizon agentic tasks. It uses Multi-Head Latent Attention (MLA) with 47 transformer layers, 64 routed experts (4 active per token), and 1 shared expert.
Quantization
This model uses a mixed-precision quantization.
| Component | Mode | Bits | Group Size |
|---|---|---|---|
| Expert weights (switch_mlp) | MXFP4 | 4 | 32 |
| Attention, embeddings, shared expert, dense MLP, lm_head | Affine | 8 | 64 |
Use with mlx-lm
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/GLM-5-MXFP4-Q8")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)