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> ### ▶ GGUF build for ollama / llama.cpp > This is the **GGUF** build (ollama / llama.cpp, Arc, CPU). Includes a > vision mmproj (see "Vision" below) — load both files together for > image understanding. If you use > LLMTray — IPSupport's local AI app for > Apple Silicon, which runs MLX — use the MLX sibling > roman220220/FrogNano-4B-2609-gptq-mlx-jang instead (it also keeps the MTP speculative-decoding head, which GGUF carries as inert tensors — see "Honest notes"). > > [](https://github.com/ipsupport-llc/llmtray/releases/latest/download/LLMTray-Full.dmg) > [](https://github.com/ipsupport-llc/llmtray) A GGUF quantization of microsoft/FrogNano-4B-2609 — Microsoft's compact repository-level coding agent, Qwen3.5-4B post-trained with reinforcement learning on ~1,500 synthetic software-engineering tasks (SWE-bench Verified 61.5% Avg@3 in Microsoft's Leaf harness) — with two deliberate improvements over a stock `Q4_K_M`: 1. **imatrix computed on a broad real corpus** (bartowski's `calibration_datav3` — code + prose + facts + multilingual). 2. **JANG-mixed precision**: full-attention layers, embeddings and the gated-DeltaNet linear-attention layers (most of the network) kept at `Q6_K`, feed-forward at the `Q4_K_M` base preset. (Not `Q8_0` on attention — see "Honest notes": `Q8_0` is ~3x slower than `Q6_K` on Vulkan hardware without a hardware int8 dot product, for no quality gain worth the cost.)

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