---
language:
- en
library_name: mlx
license: other
license_name: openmdw-1.1
license_link: https://openmdw.ai/
pipeline_tag: text-generation
base_model: poolside/Laguna-S-2.1
tags:
- mlx
- jang
- jang-6m
- awq
- quantized
- apple-silicon
- laguna
- moe
- agentic-coding
---

<div align="center">
  <img src="jangq-logo.png" height="80" alt="JANGQ-AI"/>
</div>

# JANGQ-AI/Laguna-S-2.1-JANG_6M

JANG_6M JANG affine bundle of [poolside/Laguna-S-2.1](https://huggingface.co/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

| Field | Value |
|---|---|
| Source | `poolside/Laguna-S-2.1` @ `e80da38` |
| Architecture | `laguna` — 48 layers (12 global + 36 SWA w512), 118B-A8B, 256 experts top-10 + shared, 1M ctx |
| On-disk size | 96.5 GB (21 shards) |
| Routed experts | 6-bit gate/up/down affine, group 64, AWQ folded |
| Attention q/k/v/o + g_proj | 8-bit affine |
| Shared expert / dense FFN | 8-bit affine |
| Embeddings / lm_head | 6-bit / 8-bit affine |
| Router, e_score bias, norms | fp16 passthrough |
| Modality | text-only (verified from tensor index) |

## Measured (M5 Max, 128 GB, greedy, wired)

| Metric | Value |
|---|---|
| Decode | 30.7 tok/s |
| Long-context cache parity | teacher-forced top-1 agreement 1.000 / 1.000 (pre/post the 512 sliding window, 2,913-token pass) |

## Chat / reasoning

- GLM-style think tags; `enable_thinking` toggles reasoning (thinking is ON by default in this revision's template; pass `enable_thinking=False` to 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, top_p 1.0, top_k 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](https://openmdw.ai/) (inherited).