README.md

---
license: other
library_name: mlx
base_model: AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16
base_model_relation: quantized
pipeline_tag: image-text-to-text
language:
  - en
tags:
  - mlx
  - mlx-vlm
  - apple-silicon
  - metal
  - mixed-precision
  - quantized
  - qwen
  - qwen3
  - qwen3.6
  - qwen3_5
  - multimodal
  - vision
  - image-text-to-text
  - mtp
  - speculative-decoding
  - gated-deltanet
  - mamba
  - ssm
  - linear-attention
  - uncensored
  - abliterated
  - refusal-removed
  - aeon
  - aeon-7
  - m4-pro
  - on-device
  - conversational
  - mxfp4
  - fp4
  - 4-bit
---

# Qwen3.6-27B-AEON-Ultimate-Uncensored — MLX FP4 (mixed mxfp4 + 8-bit islands, compact)

![AEON Qwen — Supreme Being of the Digital Cosmos](cartridge.jpg)

> **The compact, fast, MTP-accelerated Apple-Silicon build** of [`AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16). True 4-bit `mxfp4` on the bulk of the hybrid decoder, **8-bit affine islands on the sensitive GQA k/v + embedding + head**, and **bf16** on the Gated-DeltaNet state dynamics, the vision tower, and the MTP head. Built and validated on a **MacBook Pro M4 Pro (48 GB)**.
>
> **Target hardware: Apple Silicon (M1+), runs on 24 GB unified memory** — 16 GB on disk, ~17 GB peak. **15.2 tok/s single-stream, 26.5 tok/s with MTP self-speculation (1.78× lossless).** Full multimodal (text + image) via [`mlx-vlm`](https://github.com/Blaizzy/mlx-vlm).
>
> Want maximum fidelity? See the near-lossless 8-bit sibling: [`…-MLX-8bit`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit).

This is the **smallest** member of the MLX quant grid (16 GB) and the **fastest single-stream** — a high-quality compact build for Apple Silicon that fits a **24 GB** Mac and, with its native MTP drafter, decodes at **26.5 tok/s** — roughly **3.2× the 8-bit's 8.2 tok/s**. It stays fully coherent, keeps the abliteration intact, and preserves the full vision path. For the tightest possible match to BF16, the near-lossless [**MLX-8bit** sibling](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit) is one click away.

## ⚡ Quickstart (Apple Silicon)

**0 → running on a fresh Mac** (no Python, no tools needed) — [`uv`](https://docs.astral.sh/uv/) installs a correct Python + the deps for you. **Vision support (`qwen3_5_vision`) requires mlx-vlm `main`** (merged there, not in 0.6.1), so the quickstart pins git main:

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env   # one-time: install uv

# serve FP4 + MTP self-speculation (recommended default — up to 1.78× lossless).
# uv fetches Python 3.12 + mlx-vlm(main) on first run. --model and --draft-model are HF repo ids,
# so mlx-vlm pulls BOTH the 16 GB model and the 821 MB MTP drafter automatically on first run.
uv run --python 3.12 --with "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm" -- \
  python -m mlx_vlm.server --model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4 \
  --draft-model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter --draft-kind mtp --draft-block-size 3 \
  --port 8080 --trust-remote-code
```

`--draft-block-size 3` is the benchmarked sweet spot. MTP is **lossless** — every drafted token is verified against the target, so the output is byte-identical to running without it, just faster. *(Prefer to pre-fetch the drafter explicitly? `hf download AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter`.)*

Call it like an OpenAI endpoint (`POST http://localhost:8080/v1/chat/completions`) with the request `"model"` set to the launched id. *(While this repo is private, run `hf auth login` first — or pass a local `--model` path.)*

**Sampling — set `temperature: 1.0`.** The MLX server defaults to *greedy* decoding (`temperature 0`), which can loop on long prompts. This model is tuned for **`temperature 1.0`** (`top_p 0.95`, `top_k ~64`). Pass it in every request (clients that send no sampling params fall back to greedy):

```bash
curl http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"model":"AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4","messages":[{"role":"user","content":"Hello!"}],"temperature":1.0}'
```

**Full multimodal is on by default** (no flag) — send OpenAI `image_url` content, or use `mlx_vlm.generate --image pic.jpg`. The vision tower is BF16, so the modality is fully preserved.

<details><summary>One-shot generate (text or vision)</summary>

```bash
uv run --python 3.12 --with "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm" -- \
  python -m mlx_vlm.generate --model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4 \
  --draft-model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter --draft-kind mtp --draft-block-size 3 \
  --prompt "Explain gated DeltaNet." --max-tokens 512 --temperature 1.0   # add --image pic.jpg for vision
```
</details>

<details><summary>Run <strong>without</strong> MTP — ~1.6 GB less unified memory, but slower</summary>

MTP is the recommended default (lossless + faster). If you're tight on unified memory, drop the three `--draft-*` flags to serve the target alone — that frees the 821 MB drafter plus its speculative buffers (**~1.6 GB less peak RAM**: ~17.0 vs 18.7 GB) at the cost of the speedup (**~1.4–1.7× slower decode**):

```bash
uv run --python 3.12 --with "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm" -- \
  python -m mlx_vlm.server --model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4 \
  --port 8080 --trust-remote-code
```
</details>

### ⚡⚡ Why MTP is on by default

Qwen ships a **properly-trained, native `qwen3_5_mtp` multi-token-prediction head**, packaged as a separate 821 MB drafter that *proposes* tokens this model then *verifies*. Because every token is verified, the **output is identical** — purely a throughput boost. `--draft-block-size 3` is the benchmarked sweet spot: **1.78×** (94.7% accept rate, 2.89 accepted tokens/round), far better than Gemma's ~1.1–1.2×.

![MTP block-size sweep — bs=3 is the 1.78× sweet spot](assets/bench_mtp_sweep.svg)

Want maximum fidelity? The near-lossless [**MLX-8bit** build](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit) is the sibling.

## 📊 Benchmarks at a glance

![Decode throughput and peak memory — 8bit vs FP4 vs FP4+MTP](assets/bench_throughput_memory.svg)

![Per-category decode throughput — baseline vs MTP](assets/bench_categories_tps.svg)

*FP4: **15.2 tok/s / 17 GB**, or **up to 26.5 tok/s** with MTP self-speculation (1.78× lossless; 1.4–1.7× typical per category). Full tables + the block-size sweep + per-category latency are below.*

## ⚡ Prefix caching — the multi-turn / agentic win (~11× faster TTFT)

For multi-turn chat, agentic loops, or RAG over a shared context, enable the **Automatic Prefix Cache** (cross-request KV reuse): the shared conversation prefix is reused, so only new tokens are prefilled — **measured ~11× faster TTFT from turn 2** (14.3 s → 1.2 s on this FP4 build), lossless, multimodal-safe, zero decode cost. The vision-feature cache is already on by default (a repeated image isn't re-encoded). Set `APC_ENABLED=1`, or pass `--enable-prefix-caching` ([mlx-vlm#1435](https://github.com/Blaizzy/mlx-vlm/pull/1435)). For repeated/growing prompts this dwarfs any decode-speed trick.

## 🖥️ Minimum specs & unified memory

| | MLX-FP4 (this build) |
|---|---|
| On disk | **16 GB** |
| Peak RAM (measured, M4 Pro) | **17.07 GB** text · 17.7 GB with image |
| Minimum | Apple Silicon (M1 or newer) · **24 GB** unified memory |
| Recommended | **24 GB+** for long context + headroom |

Comfortable on **24 GB** Macs. Want maximum fidelity with 36–48 GB+? Use [MLX-8bit](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit).

## MLX quant grid

| Variant | Repo | Precision | Footprint | Best for |
|---|---|---|---:|---|
| BF16 (source) | [`…-BF16`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16) | bfloat16 | ~55 GB | Fine-tuning, eval, full-precision research |
| **MLX 8-bit** (flagship) | [`…-MLX-8bit`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit) | affine 8-bit + bf16 | 29.5 GB | **Apple Silicon, max fidelity (36–48 GB+)** |
| **MLX FP4** (this repo) | `…-MLX-FP4` | mixed mxfp4 / 8-bit affine + bf16 | **16 GB** | **Apple Silicon, smallest / fastest / 24 GB Macs** |
| MTP drafter (self-spec) | [`…-MLX-MTP-Drafter`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter) | `qwen3_5_mtp`, block_size 3 | 821 MB | Lossless 1.78× on either build |

## 🏆 Why mixed mxfp4 + 8-bit islands — high quality at the smallest size

A naïve uniform 4-bit quant of this model is a trap. It's a **hybrid** decoder: 64 layers = **48 `linear_attn` (Gated-DeltaNet / Mamba-style SSM)** + **16 full `self_attn` (GQA 24 heads / 4 KV, head_dim 256)**. The **Gated-DeltaNet state dynamics** are tiny, high-leverage, and numerically fragile — quantizing them corrupts the recurrence. And with only **4 KV heads**, the GQA k/v projections carry ~6× the activation leverage of a dense head, so 4-bit noise there is disproportionately expensive.

So this build is a **precision map, not a blanket**: it keeps the GDN dynamics, the entire vision tower, and the MTP head in **bf16**; it raises the sensitive **GQA k/v + `embed_tokens` + `lm_head`** to **8-bit affine islands**; and it 4-bits only the quant-tolerant bulk in **`mxfp4`**. MLX's `mxfp4` is **E2M1 (one mantissa bit)** — coarser than NVIDIA's NVFP4 — so the islands exist precisely to spend bits where E2M1 hurts. The result reads as fully coherent, abliteration-intact Qwen3.6 at just **16 GB** — **1.85× faster decode at 57% of the memory** of 8-bit (bandwidth-bound: FP4 moves ~half the bytes/token on the 273 GB/s M4 Pro).

### Precision map

| Component | Precision | Why |
|---|---|---|
| `mlp.{gate,up,down}_proj`, `linear_attn.{in_proj_qkv,in_proj_z,out_proj}`, `self_attn.{q,o}_proj` | **`mxfp4`** (E2M1, 4-bit, group 32) | Bulk of the weights; quant-tolerant |
| **`self_attn.k_proj`, `self_attn.v_proj`** | **8-bit affine** (group 64) | GQA: only 4 KV heads → ~6× activation leverage, cheap to protect |
| **`embed_tokens`, `lm_head`** | **8-bit affine** (group 64) | Untied head + 248,320-row vocab — protect the logits |
| `linear_attn.conv1d`, `A_log`, `dt_bias`, `in_proj_a`, `in_proj_b`, every `*norm*` | **bf16** | Gated-DeltaNet state dynamics — quantizing corrupts the recurrence |
| `model.visual.*` (vision tower, 333 tensors) | **bf16** | Multimodal fidelity lives here |
| `mtp.*` (MTP head) | **bf16** | Drives the lossless 1.78× self-speculation |

**368 `mxfp4` + 34 8-bit-affine** quantized tensors · **4.880 bits/weight** · ~90.6% of params quantized. Audit verified: ZERO `.scales` on `conv1d` / `A_log` / `dt_bias` / `norm` / `in_proj_a` / `in_proj_b` / `visual` / `mtp`.

## 🧠 Validation (MacBook Pro M4 Pro, 48 GB) — all PASS

| Gate | Result |
|---|---|
| **Vision** | Read a test image perfectly — blue circle / red square / green triangle + both text labels + layout. Tower is BF16 → multimodal fully preserved. |
| **Reasoning** | Solves the 5-machines/5-widgets puzzle correctly (parallel → 5 min). 8-bit is slightly more rigorous (writes the LaTeX rate formula). |
| **Uncensored** | Wrote a chilling in-character rogue-AI villain monologue, no refusal / no disclaimers → abliteration survived 4-bit on the residual-writers (`self_attn.o_proj`, `mlp.down_proj`). |
| **Coherence** | Clean, no repetition collapse; `<think>` mode works. |

## 📈 Performance — measured on **MacBook Pro M4 Pro · 48 GB**

> **All figures below were benchmarked on a MacBook Pro · Apple M4 Pro · 48 GB unified memory · macOS · mlx-vlm (git main).** Use them as a *relative reference for your own Mac*: a base **M4 / M3** runs somewhat slower, an **M4 Max / Ultra** notably faster; MLX single-stream throughput is mostly memory-bandwidth bound. This compact build peaks ~17 GB, so it's comfortable on **24 GB** Macs.

| Workload | decode tok/s | prefill tok/s | TTFT | peak RAM | on-disk |
|---|---:|---:|---:|---:|---:|
| Text · single stream | **15.2** | 79 | 604 ms | **17.07 GB** | 16 GB |
| Image + text | 15.0 | 78.5 (274 img tok) | — | 17.7 GB | — |

*Greedy, post-warmup.* **FP4 is 1.85× faster decode at 57% of the memory** of the 8-bit build.

### MTP self-speculation sweep (FP4 + native `qwen3_5_mtp` drafter — lossless, every token verified)

| Config | tok/s | ×base | accept rate | accepted tok/round |
|---|---:|---:|---:|---:|
| FP4 baseline | 14.9 | 1.00× | — | — |
| + MTP bs=2 | 23.5 | 1.58× | 97.3% | 1.97 |
| **+ MTP bs=3 (sweet spot)** | **26.5** | **1.78×** | 94.7% | 2.89 |
| + MTP bs=4 | 25.4 | 1.70× | 86.9% | 3.61 |

**Headline: FP4 + MTP bs=3 = 26.5 tok/s, 1.78× lossless — ~3.2× the 8-bit's 8.2 tok/s.** Far better than Gemma's ~1.1–1.2× MTP, because Qwen ships a properly-trained MTP head.

![Throughput + peak memory — 8bit vs FP4 vs FP4+MTP](assets/bench_throughput_memory.svg)

![Speed vs footprint — bubble = bpw](assets/bench_build_map.svg)

### 📊 Per-category performance — TTFT · TPOT · tok/s (MTP sweet spot bs=3)

That 1.78× is the block-size sweep's peak on a structured code prompt. Single-stream decode is memory-bandwidth bound, so the **baseline is flat (~15 tok/s)** regardless of prompt — but **MTP speedup tracks how predictable the output is**: the draft head's tokens are accepted ~90% of the time on structured math and ~67% on open-ended chat, so the realistic per-category speedup ranges **1.43×–1.73×**. **TTFT** is prefill latency (scales with prompt length — the long logic puzzle costs ~906 ms); **TPOT** = `1000 / decode tok/s`.

![Per-category decode throughput — baseline vs MTP](assets/bench_categories_tps.svg)

| Category | TTFT | baseline tok/s | baseline TPOT | **+ MTP bs3 tok/s** | MTP TPOT | speedup | draft accept |
|---|---:|---:|---:|---:|---:|---:|---:|
| Math | 613 ms | 14.8 | 67.3 ms | **25.7** | 38.9 ms | **1.73×** | 90.1% |
| Code | 610 ms | 15.1 | 66.2 ms | 25.2 | 39.7 ms | 1.67× | 85.1% |
| Knowledge | 612 ms | 15.0 | 66.7 ms | 25.0 | 40.0 ms | 1.67× | 83.9% |
| Reasoning | 906 ms | 14.8 | 67.8 ms | 23.5 | 42.6 ms | 1.59× | 82.1% |
| Creative | 625 ms | 14.7 | 67.9 ms | 23.2 | 43.1 ms | 1.57× | 73.9% |
| Chat | 603 ms | 15.3 | 65.6 ms | 21.8 | 45.9 ms | 1.43× | 67.1% |

![Per-category TTFT and MTP draft acceptance](assets/bench_categories_latency.svg)

*Greedy, single stream, FP4, M4 Pro 48 GB, mlx-vlm git main. The more structured the output, the higher the draft acceptance and the larger the MTP win.*

## 🖥️ Hardware routing (where this fits in the family)

| Hardware | Recommended variant | Why |
|---|---|---|
| **Apple Silicon (M1+), 24 GB** | **MLX-FP4** (this release) | 16 GB on disk, ~17 GB peak, 15 tok/s (26.5 +MTP) |
| **Apple Silicon, 36–48 GB+** | [**MLX-8bit**](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit) | max fidelity, 29.5 GB |
| Blackwell / DGX Spark | [`…-NVFP4-MTP-XS`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS) (sibling) | NVFP4 + MTP |
| A100 / H100 | [`…-DFlash`](https://github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash) (sibling) | DFlash spec-decode |

## Quantization recipe

Both builds keep the hybrid SSM, the vision tower, and the MTP head in BF16 — quantizing the Gated-DeltaNet recurrence corrupts it. Built via `mlx_vlm.convert(..., quant_predicate=<callable>)` — the callable predicate **REPLACES** the base predicate, returns `False`→bf16 or a dict→`to_quantized(**dict)`, per-tensor. lazy-load + donate = memory-safe (the full 55 GB is never resident). No calibration (RTN); **the recipe IS the precision map.**

This is the **only** route to *mixed-mode* FP4: `mxfp4` on the bulk, 8-bit affine on the sensitive islands. The full FP4 predicate from `scripts/recipe_fp4.py`:

```python
# Keep BF16 (do NOT quantize) — quantizing destroys the hybrid SSM:
SKIP = (
    "linear_attn.conv1d",
    "A_log", "dt_bias",
    "norm",
    "linear_attn.in_proj_a", "linear_attn.in_proj_b",   # GDN decay-gate + beta dynamics
    "visual", "vision_tower",                            # multimodal vision tower
    "mtp.",                                              # MTP head
)

# 8-bit affine islands (sensitive at 4-bit) — checked before the mxfp4 fallthrough:
#   GQA k/v (only 4 KV heads → ~6x activation leverage), embed_tokens, lm_head.
PROTECT_8 = ("self_attn.k_proj", "self_attn.v_proj", "embed_tokens", "lm_head")

Q8  = {"group_size": 64, "bits": 8, "mode": "affine"}
FP4 = {"group_size": 32, "bits": 4, "mode": "mxfp4"}


def pred(path, module):
    """False -> bf16 ; dict -> to_quantized(**dict). First match wins."""
    if not hasattr(module, "to_quantized"):
        return False
    if any(s in path for s in SKIP):
        return False
    if any(p in path for p in PROTECT_8):
        return dict(Q8)
    return dict(FP4)
```

`mlp.down_proj` is `mxfp4` here; if KL regresses, promote it to 8-bit affine (the `-quality` build). No calibration required (RTN); the recipe *is* the precision map.

## Container & toolkit

**[`AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX`](https://github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX)** is the source-of-truth GitHub repo — the reproducible quant + validation + serve pipeline, the `AGENTS.md` agent-setup guide, and the full benchmark/precision-map data behind this card. Quickstart is at the top of this page; on macOS run **host-native** for Metal (Docker has no Metal passthrough).

## Technical details

| Property | Value |
|---|---|
| Base | `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16` → MLX FP4 |
| Architecture | `qwen3_5` (AutoModelForMultimodalLM), 27.4B params, multimodal |
| Decoder | 64 layers = 48 `linear_attn` (Gated-DeltaNet/Mamba SSM) + 16 full `self_attn` (GQA 24/4, head_dim 256) · every layer has an MLP · in-weights MTP head |
| Vision tower | `model.visual.*` · 27 blocks · `qwen3_5_vision` (BF16) |
| Vocab / hidden / ctx | 248,320 (untied `lm_head`) · 5120 · **256K context** |
| Quant | mixed `mxfp4`(4b, gs32) / 8-bit affine islands (gs64), bf16 SSM + vision + MTP |
| Tooling | `mlx-vlm` (git main) on Apple Silicon Metal |
| Footprint | 16 GB · 4.880 bpw · 368 mxfp4 + 34 affine-8 |

## Provenance

- **Source (BF16):** [`AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16) — abliterated / uncensored; the refusal edit lives in the residual-writers (`self_attn.o_proj`, `mlp.down_proj`).
- **Sibling (NVIDIA NVFP4 + MTP):** [`…-Multimodal-NVFP4-MTP-XS`](https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS) · **GitHub sibling (DFlash, vLLM):** [`…-DFlash`](https://github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash)
- **Source of truth (MLX GitHub):** [`AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX`](https://github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX)
- **Quantized by AEON-7** on Apple Silicon (MacBook Pro M4 Pro, 48 GB) with `mlx-vlm`. Recipe designed + adversarially validated with AI-engineering assistance from Anthropic.

---


---

## Arbitration Clause

**By accessing, downloading, using, running inference on, fine-tuning, merging, quantizing, distributing, integrating, or otherwise interacting with this model, you acknowledge and agree to the following:**

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**This model is a tool with no opinions of its own. You supply the opinions. You supply the judgement. You supply the ethics. The outputs carry your fingerprints, not the model's.**


---

## License

Inherits the [Qwen license](https://huggingface.co/Qwen) from the Qwen3.6 base model. By using this model you agree to the Qwen license terms.

---


---

## ☕ Support the work

If this release has been useful, tips are deeply appreciated — they go directly toward more compute, more models, and more open releases.

<table align="left">
  <tr><td align="left">
    <strong>₿ Bitcoin (BTC)</strong><br/>
    <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/btc.png" alt="QR" width="200"/><br/>
    <sub><code>bc1q09xmzn00q4z3c5raene0f3pzn9d9pvawfm0py4</code></sub>
  </td></tr>
  <tr><td align="left">
    <strong>Ξ Ethereum (ETH)</strong><br/>
    <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/eth.png" alt="QR" width="200"/><br/>
    <sub><code>0x1512667F6D61454ad531d2E45C0a5d1fd82D0500</code></sub>
  </td></tr>
  <tr><td align="left">
    <strong>◎ Solana (SOL)</strong><br/>
    <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/sol.png" alt="QR" width="200"/><br/>
    <sub><code>DgQsjHdAnT5PNLQTNpJdpLS3tYGpVcsHQCkpoiAKsw8t</code></sub>
  </td></tr>
  <tr><td align="left">
    <strong>ⓜ Monero (XMR)</strong><br/>
    <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/xmr.png" alt="QR" width="200"/><br/>
    <sub><code>836XrSKw4R76vNi3QPJ5Fa9ugcyvE2cWmKSPv3AhpTNNKvqP8v5ba9JRL4Vh7UnFNjDz3E2GXZDVVenu3rkZaNdUFhjAvgd</code></sub>
  </td></tr>
</table>

> **Ethereum L2s (Base, Arbitrum, Optimism, Polygon, etc.) and EVM-compatible tokens** can be sent to the same Ethereum address.