affectively-ai/glm-ocr-gguf

GLM OCR (GGUF, Q4_K_M)

Production-ready GGUF quantization of THUDM/glm-4v-9b for distributed optical character recognition — powered by the Aether edge inference runtime.

Highlights

• ~2B parameters — OCR-optimized model based on GLM-4V architecture. Document and scene text recognition.
• ~2 GB Q4_K_M quantized — optimized for distributed edge inference
• GLM-4 architecture — proven, stable, well-tested
• Aether runtime compatible — layer-sharded across distributed nodes via Edgework.ai

Model Details

Property	Value
Base model	THUDM/glm-4v-9b
Parameters	~2B
Architecture	GLM-4
Quantization	Q4_K_M
Format	GGUF
Size	~2 GB
License	other

Usage

With llama.cpp

./llama-cli -m glm-ocr-q4_k_m.gguf -p "Your prompt here" -n 256

With Aether (Distributed Inference)

This model is deployed across the Aether distributed inference network. Weights are layer-sharded and distributed across multiple edge nodes for parallel inference.

Deployment Architecture

This model runs on the Aether distributed inference runtime — our custom engine that shards model layers across multiple nodes for parallel execution:

1. Coordinator receives requests and manages token generation
2. Layer nodes each hold a subset of model layers
3. Hidden states flow between nodes via gRPC
4. Zero cold start via warm pool scheduling

Deployed via Edgework.ai — bringing fast, cheap, and private inference as close to the user as possible.

About

Published by AFFECTIVELY · Managed by @buley

We quantize and publish production-ready models for distributed edge inference via the Aether runtime. Every release is tested for correctness and stability before publication.

• All models · GitHub · Edgework.ai