30B parameter vision-language model with day-0 transformers/vLLM support, hybrid attention architecture, and optional speculative decoding for local agentic deployment.
Summary
Enables privacy-first agent development without cloud inference; benchmarks show competitive agentic reasoning (MCP Atlas 75.5, SWE-Bench Pro 51.2) with local control over latency and cost. Direct transformers integration eliminates custom loading code.
Why it matters
Enables privacy-first agent development without cloud inference; benchmarks show competitive agentic reasoning (MCP Atlas 75.5, SWE-Bench Pro 51.2) with local control over latency and cost. Direct transformers integration eliminates custom loading code.
Implementation verdict
Replaces cloud VLM inference for privacy-sensitive document analysis and coding agents. Requires GPU with 30B capacity (~60GB VRAM unquantized); speculative decoding drafter optional but speeds structured generation. Ready now—day-0 transformers support, HF Hub availability, working code examples provided.
Sources
Dev Signal
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