---
license: apache-2.0
base_model: nex-agi/Nex-N2-mini
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
tags:
  - gguf
  - quantized
  - imatrix
  - iq3_xxs
  - qwen3
  - qwen35moe
  - moe
  - 35b
  - 3b-active
  - agentic
  - tool-use
  - reasoning
language:
  - en
---

# Nex-N2-mini IQ3_XXS

imatrix-calibrated IQ3_XXS of [nex-agi/Nex-N2-mini](https://huggingface.co/nex-agi/Nex-N2-mini). 13.6 GB on disk, fits in 15 GB GPU memory with room for context. smallest quant of this model on the hub as of 2026-06-09.

made it because i wanted to run nex-n2-mini on my laptop's AMD iGPU (15 GB GTT cap) and every existing quant was 14 GB+.

gets **~14 tok/s on CPU only** (Ryzen 7 PRO 7735U, no GPU offload). vulkan offload pushes it higher.

## architecture

| | |
|---|---|
| Base | Qwen3.5-35B-A3B-Base (post-trained by Nex AGI) |
| Architecture | `qwen35moe` |
| Total params | ~35B |
| Active params | ~3B per token |
| Experts | 256 total, 8 routed + 1 shared per token |
| Hidden size | 2048 |
| Trunk layers | 40 (MTP head not included — see below) |
| Train context | 262144 |
| Vocab | 248320 |
| Vision | not in this GGUF (text-only — see "stuff to know") |

## file

| file | quant | size | bpw | notes |
|---|---|---:|---:|---|
| `Nex-N2-mini-IQ3_XXS.gguf` | IQ3_XXS | 13.6 GB | 3.14 | attention kept at Q4_K, FFN experts pushed to IQ3 |
| `imatrix.dat` | — | 183 MB | — | importance matrix, re-quantize from this if you want a different size |
| `patch_gguf.py` | — | 3.5 KB | — | fixes the MTP load error (see below) |
| `Modelfile` | — | 1 KB | — | for `ollama create` |

## using it

needs a llama.cpp from after **2026-02-10** (when qwen35moe arch landed in [PR #19468](https://github.com/ggml-org/llama.cpp/pull/19468)).

LM Studio: drop the gguf in `~/.lmstudio/models/<you>/Nex-N2-mini-GGUF/`, load it. update the bundled llama.cpp runtime to 2.13+ if it refuses to load.

Ollama 0.19+:
```
ollama create nex-n2-mini -f Modelfile
ollama run nex-n2-mini "hi"
```

llama-cli (ChatML):
```
llama-cli -m Nex-N2-mini-IQ3_XXS.gguf -ngl 999 \
  -p $'<|im_start|>system\nyou are helpful<|im_end|>\n<|im_start|>user\nhi<|im_end|>\n<|im_start|>assistant\n' \
  -n 100
```

ollama before 0.19 wont work — too old to know the qwen35moe arch.

## if you quantize it yourself

you'll hit:
```
missing tensor 'blk.39.nextn.eh_proj.weight'
```

took me forever to figure out. nex agi didn't release the MTP draft head weights with the public release, but `config.json` claims they exist, so the convert script writes "has MTP" into the GGUF header and llama.cpp's loader then refuses because it can't find the tensor.

two metadata values to flip:

```
qwen35moe.nextn_predict_layers: 1 → 0
qwen35moe.block_count:          41 → 40
```

`patch_gguf.py` in this repo does it. 4-byte edits, idempotent, takes 30 seconds. way faster than re-converting from safetensors (8h on a laptop).

## stuff to know

- **reasoning model** — outputs contain `<think>...</think>` blocks. handle them in your wrapper or strip them
- **no MTP speedup** — weights aren't in the public release. inference works fine, you just don't get the speculative-decoding bonus
- **text only** — the base model's `config.json` has `vision_config` + image/video token slots, but llama.cpp's qwen35moe converter is text-only (PR #19468 literally titled "no vision"). if you want vision, look at quants that ship an `mmproj-*.gguf` alongside
- **Q3 means ~1-3% benchmark drop vs Q4** — for chat and tool-calling i can't tell the difference. for code/math it'll be more noticeable

## how i made it

```
huggingface-cli download nex-agi/Nex-N2-mini --local-dir source
python convert_hf_to_gguf.py source --outtype f16
python patch_gguf.py source/*-F16.gguf
llama-imatrix -m <f16.gguf> -f calibration.txt -o imatrix.dat --chunks 50
llama-quantize --imatrix imatrix.dat <f16.gguf> Nex-N2-mini-IQ3_XXS.gguf IQ3_XXS
```

calibration text = Pride and Prejudice from gutenberg + the nex-n2 README (~750 KB total, 50 chunks of 512 tokens). imatrix-aware quantizer kept attention tensors at Q4_K and pushed expert FFN weights down to IQ3 — ended up at 3.14 bpw avg.

`imatrix.dat` is in the repo if you want to re-quantize to IQ2_S, Q4_K_S, or anything else without redoing the calibration pass.

---

base model © Nex AGI · base architecture © Qwen team · llama.cpp © ggml-org. apache 2.0, same as the base.