👉 Want the unmodified, recommended version? Go to SC117/Nex-N2-mini-APEX-GGUF (use with Nex's patched llama.cpp)

This is a temporary, unofficial workaround for stock llama.cpp users. The original chat_template.jinja has been replaced with a fixed version so that --reasoning-format works without --chat-template-file.

⚠️ The Nex team explicitly recommends against modifying the chat template. The model was trained strictly on the original template — deviating from the training-time format may degrade output quality. See discussion #3 for details.

The recommended approach is to use Nex's patched llama.cpp with the unmodified GGUF. Once Nex's upstream patch is merged into stock llama.cpp, these template-fixed GGUFs will be superseded.

Use this only if you cannot use Nex's patched llama.cpp and need thinking mode to work on stock builds. Be aware that output quality may differ from the original model.

These GGUF files are quantized using APEX, a MoE-aware mixed-precision quantization technique that outperforms standard quantization methods while being significantly smaller.

APEX beats Q8_0 perplexity at half the size — and even beats F16.

APEX classifies every tensor by its role — routed expert, shared expert, or attention — and applies a layer-wise precision gradient, giving the most sensitive edge layers higher precision and compressing the redundant middle layers more aggressively.

⚠ All GGUF files above (except mmproj) contain a modified chat_template.jinja. See warning above.

Text only

./llama-server \ -m Nex-N2-mini-APEX-Quality.gguf \ -ngl 99 -ncmoe 19 -c 32768 \ --host 0.0.0.0 --port 8081

With vision

./llama-server \ -m Nex-N2-mini-APEX-Quality.gguf \ --mmproj mmproj-Nex-N2-mini.F16.gguf \ -ngl 99 -ncmoe 19 -c 32768 \ --host 0.0.0.0 --port 8081

No --chat-template-file needed — the fixed template is embedded in the GGUF. Thinking mode works out of the box. Add --mmproj mmproj-Nex-N2-mini.F16.gguf for vision. Replace Nex-N2-mini-APEX-Quality.gguf with your preferred quantization tier (Quality / Balanced / Compact). Recommended sampling: temperature 0.7, top_p 0.95, top_k 40, min_p 0.

From the original Nex-N2-mini model card (BF16, full precision).