Model Architecture: Glm4ForCausalLM
- Input: Text
- Output: Text
Model Optimizations:
- Activation quantization: FP8
- Weight quantization: FP8
Release Date: 09/06/2025
Version: 1.0
Model Developers: duydq12 (enhance by RedHatAI)

Model Optimizations

This model was obtained by quantizing activations and weights of GLM-4-32B-0414 to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%.

Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization.

Deployment

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

python

# Requires vllm>=0.8.5
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "duydq12/GLM-4-32B-0414-FP8-dynamic"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

Creation details

This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.

python

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model_stub = "THUDM/GLM-4-32B-0414"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(model_stub, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_stub, torch_dtype="auto", device_map="auto")

# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
  ignore=["lm_head"],
  targets="Linear",
  scheme="FP8_dynamic",
)

# Apply quantization
oneshot(
  model=model,
  recipe=recipe,
)

# Save to disk in compressed-tensors format
save_path = model_name + "-FP8-dynamic"
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")

Evaluation

private

Accuracy

private