JANGQ-AI/Laguna-S-2.1-JANG_6M
<div align="center"> <img src="jangq-logo.png" height="80" alt="JANGQ-AI"/> </div>
<p align="center"> <a href="https://vmlx.net"><img src="vmlx-app.png" alt="vMLX — run JANG models on Apple Silicon" width="820"></a> </p>
<h3 align="center">⚡ All JANG models are meant to be run in <a href="https://vmlx.net">vMLX</a></h3>
JANGQ-AI/Laguna-S-2.1-JANG_6M
JANG_6M JANG affine bundle of poolside/Laguna-S-2.1 — 118B-parameter / ~8B-active MoE for agentic coding and long-horizon work, quantized for Apple Silicon.
Attention (incl. the softplus output gate), shared expert, router, and norms stay high precision; the 256-routed-expert bulk carries 6-bit gate/up/down, protected by activation-aware (AWQ) scales chosen by measured quantization error against a no-AWQ baseline (3.1% lower reconstruction error at this bit width).
Bundle
Measured (M5 Max, 128 GB, greedy, wired)
Chat / reasoning
- GLM-style think tags;
enable_thinkingtoggles reasoning (thinking is ON by default in this revision's template; passenable_thinking=Falseto disable) - The FULL chat template is inlined in
tokenizer_config.json(upstream ships only an{% include %}stub that most runtimes cannot resolve — inlining is what makes the reasoning toggle actually work) - Stop tokens
eos_token_id = [2, 24]— id 24 is end-of-turn and must be in the stop set - Template emits its own leading
〈|EOS|〉(bos 2): do not prepend another - Tool calls:
<tool_call>name<arg_key>k</arg_key><arg_value>v</arg_value></tool_call> - Vendor sampling: temperature 1.0, topp 1.0, topk 20
Loaders must honor the per-module {bits, group_size, mode} overrides in config.json[quantization].
Quantized and verified by Jinho Jang (eric@jangq.ai). License: OpenMDW-1.1 (inherited).
