The serving runtime for Escha 2-/3-bit (escha) quantized models of the qwen35 dense architecture (Qwen3.8-27B and siblings). One repo per model architecture, one directory per engine — this architecture currently has one engine, sglang/. The engine is a fork of SGLang bundled inside the wheel, running the Escha CUDA kernels. No separate sglang install is needed, and none should be present — the wheel ships its own. Full detail, including the per-GPU cookbook and troubleshooting: Then check the stack and the endpoint: This is a reasoning model. With thinking on, the reasoning arrives in reasoningcontent and the answer in content — read both, or you will see half the response. Two per-request…
Organization
Escha Labs
EschaLabs
Models in Library2
Datasets in Library0
Models on Hugging Face4
Followers235
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This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.
Reported licenses: apache-2.0. Open a record to review its specific license and conditions.
Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.
Models
Serving runtimes for Escha 2-/3-bit (eschamoe) quantized models of the qwen35moe architecture (Qwen3.5 / Qwen3.6 Mixture-of-Experts, 256 experts). One repo per model architecture, one directory per engine — pick the engine that fits your workload: Neither engine is uniformly faster. ZML wins sustained decode on long single-stream answers and is far simpler to deploy; SGLang wins short replies, wins time-to-first-token on long prompts, and is the only option for concurrency, tool calling or structured output. If unsure: multi-user, agents-at-scale, or structured output → SGLang; long-form single-user generation on your own GPU → ZML. The ZML engine is built on ZML (Zig + MLIR/XLA); the…