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. 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…
Open weights
apache-2.0
2.7B parameters
131,072 tokens
mlx
Diplodocus preview 4, the Osaurus assistant for finance, documents, spreadsheets and tool-assisted work, built on IFM/K2-Horizon-7B and trained on Osaurus' own workspace tools (workbooks and documents edited in place, the built-in calculate tool, reasoning on and off). This is the GGUF build for llama.cpp: one calibrated Q6K file, 7.76 GB. Using Osaurus on a Mac? Use the MLX bundle OsaurusAI/Diplodocus-preview-JANG6M instead. Same weights, same template, same reasoning toggle. Requires llama.cpp with K2 Horizon support (ggml-org/llama.cpp#29535, merged 2026-10-06). Older builds, and apps that bundle an older llama.cpp, will not load the k2-horizon architecture. Trained rounds R8–R11 on top…
Open weights
apache-2.0
gguf
