---
title: Nex-N2.5-mini-OptiQ-4bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/Nex-N2.5-mini-OptiQ-4bit"
md_url: "https://www.modelscope.cn/models/mlx-community/Nex-N2.5-mini-OptiQ-4bit.md"
repository: mlx-community/Nex-N2.5-mini-OptiQ-4bit
last_updated: 2026-09-12
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen3_5_moe
architectures:
  - Qwen3_5MoeForConditionalGeneration
base_model:
  - nex-agi/Nex-N2.5-mini
base_model_relation: quantized
parameters: 6.5B
tensor_type:
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 85
stars: 0
tags:
  - mlx
  - quantized
  - mixed-precision
  - 4bit
  - 8bit
  - optiq
  - apple-silicon
  - image-text-to-text
  - text-generation
  - qwen3.5
  - agent
---

# Nex-N2.5-mini-OptiQ-4bit

> Nex-N2.5-mini-OptiQ-4bit - mlx-community 在 ModelScope 开源的模型。mlx-community/Nex-N2.5-mini-OptiQ-4bit

mlx-community/Nex-N2.5-mini-OptiQ-4bit 是 ModelScope 魔搭社区上的 6.5B 参数image-text-to-text模型，采用 apache-2.0 许可，基于 nex-agi/Nex-N2.5-mini 构建。

- **Repository**: mlx-community/Nex-N2.5-mini-OptiQ-4bit
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 6.5B
- **Base model**: nex-agi/Nex-N2.5-mini
- **Tags**: mlx, quantized, mixed-precision, 4bit, 8bit, optiq, apple-silicon, image-text-to-text, text-generation, qwen3.5, agent
- **Downloads**: 85
- **Stars**: 0
- **Last updated**: 2026-09-12

Source: https://www.modelscope.cn/models/mlx-community/Nex-N2.5-mini-OptiQ-4bit

---

# mlx-community/Nex-N2.5-mini-OptiQ-4bit

> **Built with [mlx-optiq](https://mlx-optiq.com)**, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. [Try the Lab](https://mlx-optiq.com/docs/lab/) · [All OptiQ quants](https://mlx-optiq.com/models) · [Docs](https://mlx-optiq.com/docs/)

A 4-bit mixed-precision MLX quant of [nex-agi/Nex-N2.5-mini](https://huggingface.co/nex-agi/Nex-N2.5-mini), Nex's agentic fine-tune of Qwen3.5-35B-A3B (35B total parameters, 3B active, 256 experts, image input). Sensitive layers are kept at 8-bit and robust ones at 4-bit, so the on-disk size stays within a few percent of a stock uniform 4-bit quant while the parts that matter keep their precision.

Nex-N2.5-mini is built for long-horizon agent work: terminal use, coding agents, computer and browser use. Upstream reports 73.4 on Terminal-Bench 2.1 and 43.8 on SWE-Bench Pro for the bf16 model (their numbers, their harness). It is the model behind OptiQ Code's local-agent setup on a 36 GB Mac.

## Quantization details

| Property | Value |
|---|---|
| Predominant precision | 4-bit |
| Layers at 8-bit (sensitive) | 399 |
| Layers at 4-bit (robust) | 113 |
| Total quantized layers | 512 |
| Group size | 64 |
| Per-layer allocation | reused from [mlx-community/Qwen3.5-35B-A3B-OptiQ-4bit](https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-OptiQ-4bit), the base model's measured recipe |
| Vision tower | bf16, bundled as `optiq/optiq_vision.safetensors` (0.9 GB) |
| KV cache | `kv_config.json`, per-layer mixed precision for `optiq serve --kv-config` |
| MTP head | none (the upstream fine-tune ships without one, so `--mtp` has nothing to load) |
| Weights on disk | 22.2 GB (+0.9 GB vision sidecar) |

A fine-tune keeps its base architecture, and which layers tolerate fewer bits is a property of that architecture, so this quant reuses the bit map measured on Qwen3.5-35B-A3B with a KL-divergence sensitivity pass over a [six-domain calibration mix](https://mlx-optiq.com/blog/calibration-mix) rather than re-measuring it. The Capability Score was not re-run for this variant; the base model's score (77.42 against 76.92 for uniform 4-bit) is the reference for what the allocation buys.

We follow the same naming convention `llama.cpp` uses for Q4_K_M and similar mixed-precision quants: the "4-bit" label is for the predominant precision, not the weighted average.

## Usage

Text, with `mlx-lm`:

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Nex-N2.5-mini-OptiQ-4bit")
response = generate(model, tokenizer, prompt="Explain quantum computing in simple terms.", max_tokens=200)
```

Text and images, tool calling, and the mixed-precision KV cache, with [`mlx-optiq`](https://mlx-optiq.com/):

```bash
pip install mlx-optiq
optiq serve --model mlx-community/Nex-N2.5-mini-OptiQ-4bit --kv-config kv_config.json
```

The server speaks the OpenAI chat and Responses APIs and the Anthropic Messages API; image parts in a message are routed through the bundled vision tower. `optiq code` uses the same server as a local coding agent.

### Sampling

Upstream evaluates with `temperature 0.7`, `top_p 0.95`, `top_k 40`. Thinking is controlled with `reasoning_effort` (`none` for a direct answer); the chat template is the upstream one and is bundled as `chat_template.jinja`.

See the [Qwen3.5 family guide](https://mlx-optiq.com/docs/qwen3.5) for serving flags, training recipes, and family caveats.

## Links

- **Project website:** [mlx-optiq.com](https://mlx-optiq.com/)
- **Qwen3.5 family guide:** [mlx-optiq.com/docs/qwen3.5](https://mlx-optiq.com/docs/qwen3.5)
- **PyPI:** [pypi.org/project/mlx-optiq](https://pypi.org/project/mlx-optiq/)
- **Calibration mix:** [mlx-optiq.com/blog/calibration-mix](https://mlx-optiq.com/blog/calibration-mix)
- **Base model:** [nex-agi/Nex-N2.5-mini](https://huggingface.co/nex-agi/Nex-N2.5-mini)
- **Recipe source:** [mlx-community/Qwen3.5-35B-A3B-OptiQ-4bit](https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-OptiQ-4bit)

## Quantize your own

This quant was produced by [mlx-optiq](https://mlx-optiq.com). Point it at any Hugging Face model to get the same sensitivity-aware mixed precision:

```bash
pip install mlx-optiq
optiq convert <hf-model-id> --target-bpw 5.0 --candidate-bits 4,8
optiq lab   # full local workbench: chat, compare, quantize, fine-tune
```

## License

Apache 2.0, inherited from the base model.