Welcome Beam: a 501B open model you can own, customize, and scale

Hugging Face announced Beam by Reflection, an Apache 2.0-licensed, text-only mixture-of-experts model designed for coding, agentic tasks, and long-context reasoning, with 501 billion total parameters but 23 billion active per token and a one-million-token context window. Its architecture combines mostly sliding-window attention with periodic global-attention layers to reduce KV-cache growth, uses 256 experts with eight selected per token, and offers BF16, FP8, and NVFP4 checkpoints, although deployment still requires memory sufficient for the full model, ranging from large multi-GPU systems to serverless inference providers. Beam supports configurable reasoning effort, preserved reasoning across turns, grammar-based tool calls, and structured response parsing through its tokenizer, with day-one integrations for Transformers, vLLM, SGLang, and a Rust-based harness, plus speculative decoding via DFlash2. Reflection reported benchmark results across coding, reasoning, tool-use, search, and general-capability tests, while Hugging Face also described informal task evaluations suggesting higher reasoning settings can improve performance on complex retrieval, math, and terminal tasks. The release includes recipes for supervised fine-tuning with LoRA and reinforcement learning with GRPO, demonstrations involving macOS application development, multi-agent coding experiments, and fine-tuning a smaller incident-triage decision model, alongside a $10,000 Hub-credit incentive for community projects built with Beam.

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