Runtime: Osaurus 0.25.21 or newer (osaurus.json: required_osaurus_version).

This bundle uses the JANGH expert format (jangtq2 banks); it needs a runtime with the JANGH MoE kernels.

OsaurusAI/LFM2.5-24B-A2B-JANGH2

A JANG bundle of LiquidAI/LFM2.5-24B-A2B: Liquid AI's 24B mixture-of-experts reasoning model with 2B active parameters (40 layers: 30 short-convolution and 10 full-attention, 64 routed experts with 4 active, two dense layers), built for on-device agents. It always thinks before answering and calls tools in Liquid's Pythonic format. 44.7 GiB of bf16 weights in 7.3 GiB.

What is quantized how

part	format
routed experts gate / up	JANGH 2-bit: Hadamard-32 rotation + odd-cubic codebook, fp16 per-row scale, act-order GPTQ per expert
routed experts down	JANGH 3-bit, same scheme
dense MLP (layers 0-1), attention q/k/v/out, short-conv in/out projections	affine 8-bit, GPTQ
embeddings (tied lm_head)	affine 8-bit
routers, expert bias, depthwise conv kernels, norms	source values

Every quantized matrix with an input Hessian was rounded with GPTQ. The Hessians come from the model's own outputs: the bf16 model wrote its thinking and answers for math, finance, code in eleven languages, terminal and coding-agent sessions (with real tool results), multi-step tool calls, networking, cybersecurity, science, office tasks, long documents and multilingual chat at its native sampling settings, and those sequences were the calibration data. Per-expert Hessians use only the tokens routed to that expert. Affine quantization is MLX's own grid (bf16 scales and biases).

Fidelity vs the bf16 model

232,094 teacher-forced positions on held-out sequences the bf16 model generated itself (never seen in calibration), scored against the bf16 model: top-128 KL, top-1 agreement, and the change in log-likelihood of the real next token (dNLL).

This model routes chaotically: changing the bf16 embeddings by a single bf16 ULP already moves top-1 on 1.8% of positions. That run is the floor row below; read the quantized rows against it.

	size	KL median	KL mean	top-1	dNLL
bf16 model, embeddings perturbed by 1 ULP (the floor)	44.7 GiB	1.1e-04	0.0036	98.24%	-0.0000
JANGH2	7.3 GiB	5.5e-03	0.1136	89.93%	+0.0989

By prompt family (KL median, top-1):

family	KL median	top-1
multi-step tool agent	0.0195	86.3%
tool use (single turn)	0.0212	88.9%
code: cpp	0.0015	91.1%
code: go	0.0000	94.6%
code: javascript	0.0014	91.0%
code: python	0.0002	93.5%
code: rust	0.0081	89.3%
code: shell	0.0111	89.0%
code: swift	0.0072	89.8%
code: typescript	0.0029	90.8%
creative writing	0.0289	85.8%
cybersecurity	0.0525	83.4%
finance	0.0068	90.1%
general / multilingual / structured output	0.0127	87.9%
coding agent (native tools)	0.0017	93.7%
coding agent, next turn	0.0059	93.5%
long documents	0.0150	87.6%
math	0.0010	92.6%
networking	0.0014	91.9%
office tasks	0.0043	91.8%
PyTorch code	0.0033	90.4%
science	0.0034	90.9%
SWE-agent	0.0313	84.7%
terminal agent (shell)	0.0038	90.5%
turn after a tool result	0.0279	87.7%

Decisions: the quantized model picks the same token as bf16 at 98 / 99 </think> closes (floor run: 99 / 99) and at 22 / 24 tool-call starts (floor run: 24 / 24).

Model notes

• Text only. 131,072-token context.
• Reasoning: always on. The chat template opens <think> for every answer; there is no off switch and no effort levels. preserve_thinking keeps earlier turns' thinking in the context.
• Tools: tool definitions go into the system prompt; calls come back as <|tool_call_start|>[name(arg='value')]<|tool_call_end|> (a Python-literal list, parsed as data). Tool results use the tool role.
• Sampling (generation_config.json, mirrored in jang_config.json): temperature 0.1, top-k 50, repetition penalty 1.05, EOS <|im_end|>.
• Router: sigmoid scores with the expert bias used only to pick the top-4; the expert weights are the unbiased scores. The bundle's jang_config.json documents this; a softmax router produces wrong output on this model.
• No draft model exists for the 24B, so there is no speculative decoder in this bundle.

Source

• LiquidAI/LFM2.5-24B-A2B revision 1769516ac588, Apache-2.0.
• Quantization and bundle by Jinho Jang (eric@osaurus.ai), 2026-10-13.