Nex-N2-mini-MXFP4 MOE-GGUF

GGUF quantization of nex-agi/Nex-N2-mini — a 35B MoE agentic model (3B active) built on Qwen3.5-35B-A3B-Base with 256 experts, Gated DeltaNet hybrid attention, 262K context, and 27-layer vision encoder.

Quantized to MXFP4 MOE format for efficient inference with minimal quality loss.

About MXFP4 MOE

MXFP4_MOE (Microscaling FP4 for Mixture-of-Experts) is an OCP standard 4-bit format. Expert weights are stored in MXFP4 while non-expert tensors (attention, embeddings, norms) remain at Q8_0, balancing quality and compression for MoE models. Works on any GPU or CPU.

Files

Quantization Details

Model Description

• Developer: Nex AGI
• Base Model: Qwen3.5-35B-A3B-Base
• Architecture: Mixture-of-Experts (MoE) with Gated DeltaNet + full attention
• Parameters: 35B total, 3B activated per token
• Experts: 256 routed experts (8 per token) + 1 shared
• Context Length: 262,144 tokens
• Vision: 27-layer ViT encoder (1152 hidden), image-text-to-text
• Languages: English, Chinese, multilingual
• License: Apache 2.0

Usage

llama.cpp (CLI)

# Text + Image
llama-cli -m nex-n2-mini-mxfp4_moe.gguf \
  --mmproj mmproj-nex-n2-mini-f16.gguf \
  --image photo.jpg \
  -p "Describe this image in detail" \
  -n 512

# Text only
llama-cli -m nex-n2-mini-mxfp4_moe.gguf \
  -p "Explain quantum computing in simple terms" \
  -n 512

# OpenAI-compatible server
llama-server -m nex-n2-mini-mxfp4_moe.gguf \
  --mmproj mmproj-nex-n2-mini-f16.gguf \
  --port 8080

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="FreedomAISVR/Nex-N2-mini-MXFP4-MOE-GGUF",
    filename="nex-n2-mini-mxfp4_moe.gguf",
    n_gpu_layers=-1,
)

response = llm.create_chat_completion([
    {"role": "user", "content": "What is the capital of France?"}
])
print(response["choices"][0]["message"]["content"])

Direct download

from huggingface_hub import hf_hub_download

for filename in ["nex-n2-mini-mxfp4_moe.gguf", "mmproj-nex-n2-mini-f16.gguf"]:
    hf_hub_download(
        repo_id="FreedomAISVR/Nex-N2-mini-MXFP4-MOE-GGUF",
        filename=filename,
        local_dir="./models"
    )

Quantization Pipeline

1. Download source weights
   huggingface_hub.snapshot_download("nex-agi/Nex-N2-mini")

2. Convert text model to F16 GGUF
   convert_hf_to_gguf.py --outtype f16

3. Extract vision encoder
   convert_hf_to_gguf.py --mmproj --outtype f16

4. Quantize to MXFP4 MOE
   llama-quantize --allow-requantize nex-n2-mini-f16.gguf nex-n2-mini-mxfp4_moe.gguf MXFP4_MOE

Hardware

License

Apache 2.0 — same as the original nex-agi/Nex-N2-mini.