Cluster · Models

Models.

Gemma 4, Qwen 3.6, Falcon Arabic, and how to pick an open-weight model for sovereign Arabic-first deployment without bringing the cloud back in. Capability, context length, language coverage, and licence.

Models.

Articles

Gemma 4 with 256K Context: A Deep Dive for Sovereign On-Premise Deployment

Qwen 3.6 for Arabic NLP: Benchmarks, Strengths, and Production Deployment

DeepSeek R1 Reasoning On-Premise: When Step-by-Step Thinking Matters

Falcon Arabic LLM: TII's Open Model and Its Place in Sovereign AI

LoRA, QLoRA, and RLHF on Customer Hardware: Fine-Tuning Without the Cloud

Gemma 4 vs Llama 4 vs Qwen 3.6 on Arabic Evaluation

Practical Use-Cases for 256K Token Context Windows

Arabic NLP Evaluation Suites: AraBench, ALUE, and Arabic MMLU

Tokenizer Efficiency Across Arabic LLMs

Choosing Embedding Models for Bilingual Arabic-English RAG

Fine-Tuning Gemma 4 on an Omani Legal Corpus

Fine-Tuning Qwen 3.6 on Classified Documents: RLHF Safety Patterns

DeepSeek R1 for Audit Analysis Workflows

Methodology for Evaluating Multilingual Models in Sovereign Settings

Sourcing Training Data for Sovereign Fine-Tuning Without Compromising Privacy

Falcon Arabic at the Edge: Sizing for Branch Deployments

GGUF Quantization for Arabic: Q4_K_M vs Q5_K_M Quality Tradeoffs

vLLM vs TGI vs llama.cpp for Production LLM Serving

Prompt Engineering for Formal Arabic Government Tone

LLM Evaluation Frameworks for Sovereign Deployments: Ragas and DeepEval

Building an Arabic Instruction-Tuning Dataset for Sovereign Use

KV Cache Optimization for Long-Context Arabic Inference

DPO and RLHF on Private Data: An Operational Playbook

Comparing Open-Source Models on Omani Arabic Dialect

Synthetic Data Generation for Sovereign Model Training