Instructions to use TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF

SGLang

How to use TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF 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 "TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with Docker Model Runner:
```
docker model run hf.co/TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF
```

📋 Model Overview

GLM-4.5-Air-REAP-82B-A12B has the following specifications:

Base Model: GLM-4.5-Air
Compression Method: REAP (Router-weighted Expert Activation Pruning)
Compression Ratio: 25% expert pruning
Type: Sparse Mixture-of-Experts (SMoE) Causal Language Model
Number of Parameters: 82B total, 12B activated per token
Number of Layers: 46
Number of Attention Heads (GQA): 96 for Q and 8 for KV
Number of Experts: 96 (uniformly pruned from 128)
Number of Activated Experts: 8 per token
Context Length: 131,072 tokens
License: MIT

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Model tree for TobDeBer/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF

Base model

zai-org/GLM-4.5-Air

Finetuned

(39)

this model