Instructions to use cerebras/GLM-4.6-REAP-218B-A32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cerebras/GLM-4.6-REAP-218B-A32B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cerebras/GLM-4.6-REAP-218B-A32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cerebras/GLM-4.6-REAP-218B-A32B")
model = AutoModelForMultimodalLM.from_pretrained("cerebras/GLM-4.6-REAP-218B-A32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cerebras/GLM-4.6-REAP-218B-A32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cerebras/GLM-4.6-REAP-218B-A32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cerebras/GLM-4.6-REAP-218B-A32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cerebras/GLM-4.6-REAP-218B-A32B

SGLang

How to use cerebras/GLM-4.6-REAP-218B-A32B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cerebras/GLM-4.6-REAP-218B-A32B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cerebras/GLM-4.6-REAP-218B-A32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cerebras/GLM-4.6-REAP-218B-A32B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cerebras/GLM-4.6-REAP-218B-A32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cerebras/GLM-4.6-REAP-218B-A32B with Docker Model Runner:
```
docker model run hf.co/cerebras/GLM-4.6-REAP-218B-A32B
```

Prompts with /nothink in the calibration data

by sokann - opened Oct 27, 2025

Discussion

sokann

Oct 27, 2025

With GLM-4.5/4.6, when a user prompt ends with /nothink, then the model is supposed to respond with a dummy <think></think>.

I made a ~133 GiB quant of REAP-218B, and noticed that sometimes it would struggle a bit to come up with the</think>, e.g.

              {
                "id": 151351,
                "token": "</think>",
                "bytes": [60,47,116,104,105,110,107,62],
                "logprob": -0.5386887192726135
              },
              {
                "id": 198,
                "token": "\n",
                "bytes": [10],
                "logprob": -0.8810062408447266
              },

Meanwhile, a ~133 GiB quant of REAP-252B doesn't have this issue. So I suspect the ability to respond with a dummy <think></think> may have been partially pruned in REAP-218B. Is it possible to mitigate that by including some prompts with /nothink in the calibration data? Thanks

sokann

Oct 27, 2025

Btw, I am particularly interested in REAP-218B, as the 133 GiB quant somehow was able to figure out a rather tricky golang concurrency issue. Prior to this, the only models that could figure it out were

O3 Pro - tested and passed with medium
O3 - tested and passed with medium
GPT-5 - tested and passed with high/medium/low, failed with minimal

After the heavy pruning, REAP-218B somehow was able to stumble to the correct answer after a long reasoning, and its final answer was 99% coherent. The answers from full 355B (via fireworks) and the REAP-252B quant were totally wrong.

sokann

Oct 27, 2025

Also, it is expected that these REAP models are not able to recite wikitext right? The PPL of https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp/resolve/main/wiki.test.raw.gz is atrocious 😂

Baseline: about 3.5, see the graph from https://huggingface.co/ubergarm/GLM-4.6-GGUF

REAP-252B 133 GiB quant: 12.7295 +/- 0.10395

REAP-218B 133 GiB quant: 18.7934 +/- 0.16674

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