SheikhLM
SheikhLM
Blazingly fast and remarkably powerful.
SheikhLM is a family of state-of-the-art small language models with 135M, 360M, and 1.7B parameters, designed to deliver strong performance while remaining efficient and practical to deploy.
SheikhLM provides the sophisticated, end-to-end tool use performance expected from today’s leading models, but in a streamlined form factor that makes deployment and scaling easier than ever. It enables:
- Faster feedback cycles in compile-run-test and browse-retrieve-cite chains.
- More concurrent runs on the same budget for regression suites and multi-seed explorations.
- Simpler capacity planning with smaller per-request memory and steadier tail latency.
The project focuses on the full model lifecycle, including:
- High-quality data curation, emphasizing cleanliness, diversity, and signal over sheer scale
- Rigorous model training and evaluation, with transparent benchmarks and comparisons
- Practical usage guidance, enabling real-world experimentation, fine-tuning, and deployment
SheikhLM is built for researchers and developers who need compact, capable models that balance performance, speed, and resource efficiency. It is especially suited for local inference, constrained environments, and custom downstream applications where larger models are impractical.