Explore Falcon-H1 hybrid language models with Transformer + SSM architecture, outperforming larger models with high efficiency and long-context support.
Falcon-H1 Models: Are Hybrid LLMs More Efficient? Read More »
Explore lightning-fast Whisper transcriptions using Hugging Face Inference Endpoints, with state-of-the-art model benchmarks and deployment options.
Whisper Transcription: Is It Fast Enough for Real-Time? Read More »
Finetuning olmOCR improves header and footer extraction in layout-heavy documents. Discover OCR upgrades for better business parsing.
Finetuning olmOCR: Does It Make OCR Better? Read More »
Learn how to train Vision Language Models (VLMs) with nanoVLM, a lightweight PyTorch toolkit perfect for beginners and researchers.
Vision Language Models: Should You Train Your Own? Read More »
Learn how to build an MCP server with Gradio in 5 lines of Python and integrate your tools with LLMs like Claude or Cursor.
MCP Server Python: Are 5 Lines Enough? Read More »
Learn how to use Hugging Face models on Kaggle, including private and gated models, with seamless integration and discoverability.
Hugging Face Models on Kaggle: How Does It Work? Read More »
Transformers library now powers 300+ AI models with seamless integration and standardization across tools like vLLM, MLX, and llama.cpp.
Transformers Library: Is Standardization the Future? Read More »
Learn how AutoRound enables fast, accurate INT2-INT8 quantization for LLMs & VLMs with minimal accuracy loss and broad compatibility with Intel CPUs.
AutoRound Quantization: Is INT2 Accuracy Worth It? Read More »
Explore what Qwen-3’s chat template reveals about reasoning, context management, and tool usage in LLMs.
Qwen-3 Chat Template: What Can It Teach Us? Read More »
Discover how to build a functional AI agent with MCP in just 50 lines using JavaScript. Learn how tool calling enhances LLM capabilities.
MCP Agents: Can You Build One in 50 Lines? Read More »
