library_name: mlx base_model: empero-ai/Qwen3.8-35B-A3B-Distill pipeline_tag: text-generation license: apache-2.0 tags: [mlx, safetensors, quantized, moe, qwen3.8, abliterated, uncensored, apex, nanoplus]

Quick Navigation Index

1. APEX MLX Variants
2. Fidelity Verification
3. Quality Spectrum: APEX MLX NanoPlus vs. MiniPlus
4. Model Files & Technical Specifications
5. Surgical Tensor Quantization Map
6. Apple Silicon Unified Memory & Context
7. Recommended Configuration & Setup
8. Recommended Generation Parameters
9. CRITICAL: Coding Syntax & Repeat Penalty Advisory
10. Native Chat Template
11. Optional Support

Qwen3.8-35B-A3B-Distill MLX APEX-NanoPlus Abliterated (Uncensored)

Compact Q4-Class Frontier MoE · Native MLX Mixed Precision · Apple Silicon · 18.82 GB Weights

[!WARNING]

Known Upstream Long-Generation Limitation

The upstream Qwen3.8-35B-A3B-Distill checkpoint can repeat during very long responses, including after the <think> block. This behavior originates upstream and is not specific to the MLX APEX quantization.

[!NOTE]

🚀 LOOKING FOR THE HIGHER-FIDELITY MLX RELEASE?

If you can spend about 1.13 GB more storage/unified-memory weight budget, use the sister MiniPlus release: 🔗 Qwen3.8-35B-A3B-Distill MLX APEX-MiniPlus Abliterated — Q5-class practical quality, 19.95 GB of Safetensors weights and a larger precision budget on always-active components.

[!TIP]

🔓 ABLITERATED / UNCENSORED MLX EDITION

This is the Abliterated / Uncensored MLX APEX-NanoPlus edition of Qwen3.8-35B-A3B-Distill. It is the compact member of the two-model MLX APEX family and is intended for users who deliberately prefer reduced refusal behavior.

[!IMPORTANT]

THE COMPACT SPECIFICATION: Q4-CLASS QUALITY AT 18.82 GB

MLX APEX-NanoPlus is a native MLX mixed-precision release built around a 3-bit G32 base while selectively retaining substantially higher precision on sensitive and always-active paths. The result is 18.8156 GB (17.52 GiB) of Safetensors weights with a Q4-class practical-quality target.

[!TIP]

🏆 THE MLX NANOPLUS SWEET SPOT: Q4-CLASS PRACTICAL QUALITY

Why NanoPlus is not equivalent to a flat 3-bit model:

• LM head: 6-bit G64.
• Shared experts: 5-bit G64.
• Linear-attention in_proj_z: 8-bit G64.
• Norms, MoE routers and recurrent state/control tensors: protected in FP32.
• Full-attention Q/K/V: 4-bit G64, with o_proj at 6-bit G64.

The Hub may identify the checkpoint from its 3-bit G32 base quantization, but that label does not describe the precision of the complete tensor map.

[!TIP]

🏆 FIDELITY RESULT

Release	Weight Size	Quality Class	ΔPPL	%PPL
MLX APEX-NanoPlus Abliterated (CURRENT)	18.8156 GB	Q4-class	+0.826288	+9.365%

PPL is a useful fidelity estimate, not a complete measure of real-world reasoning, coding, tool use or long-context behavior.

[!WARNING]

DO NOT CONFUSE APEX MLX MINIPLUS / NANOPLUS WITH GENERIC APEX OR APEX-I GGUF RELEASES!

The MLX APEX family is a separate native MLX mixed-precision specification. It is not a renamed GGUF APEX-I-Mini, APEX-I-MiniPlus, K-quant or IQ-quant.

• Generic / GGUF APEX releases: use llama.cpp/GGUF quantization types such as Q*_K and IQ*, with their own tensor layouts, kernels and bit accounting.
• MLX APEX NanoPlus / MiniPlus: use MLX affine quantization with per-module bit width and group-size overrides, plus protected FP32 paths. There are no IQ quants here, which is why these releases use APEX, not APEX-I.
• Quality-class labels: Q4-class and Q5-class describe practical fidelity positioning; they do not claim that the checkpoints are uniform flat 4-bit or 5-bit models.

[!TIP]

APPLE SILICON UNIFIED MEMORY

MLX uses Apple Silicon unified memory rather than the discrete GPU-VRAM/system-RAM split used by CUDA + llama.cpp. NanoPlus weights alone occupy about 17.52 GiB; runtime state, activations, macOS and the active context require additional unified memory. Context headroom therefore depends on the Mac and its available memory at inference time.

APEX MLX Variants

NanoPlus prioritizes the smaller footprint while retaining high precision on sensitive global, routing and recurrent-control paths. MiniPlus spends the additional budget on higher precision across the always-active shared experts and linear-attention projections.

Fidelity Verification

The model-specific fidelity result is summarized at the top of the card:

• ΔPPL: +0.826288
• %PPL: +9.365%

These values are useful as a quantization-fidelity estimate. They should not be interpreted as a direct benchmark score for coding, reasoning, agents or instruction following.

Quality Spectrum: APEX MLX NanoPlus vs. MiniPlus

The quality tiers are practical positioning labels across two different MLX mixed-precision maps, not claims of uniform flat-bit equivalence.

Model Files & Technical Specifications

• Base Model: empero-ai/Qwen3.8-35B-A3B-Distill
• Architecture: Qwen3.8 / Qwen3.5-MoE family, 40 layers, 256 micro-experts with hybrid linear attention / recurrent state and periodic full-attention layers
• Runtime: MLX / MLX-LM on Apple Silicon
• Context Length: 262,144 tokens (native 256K); usable context is additionally constrained by available unified memory

Surgical Tensor Quantization Map

This is the MLX-native mixed-precision map represented by the published checkpoint:

Apple Silicon Unified Memory & Context

Unlike a GGUF CUDA deployment, MLX does not split the model into -ngl GPU layers and system-RAM offload. The CPU and GPU share the same unified-memory pool.

No Mac throughput figure is claimed here without a corresponding measured Apple-Silicon result.

Recommended Configuration & Setup

pip install -U mlx-lm

mlx_lm.generate \
  --model IsValorum/Qwen3.8-35B-A3B-Distill-MLX-APEX-NanoPlus-Abliterated \
  --prompt "Hello"

Server:

mlx_lm.server --model IsValorum/Qwen3.8-35B-A3B-Distill-MLX-APEX-NanoPlus-Abliterated

⚙️ Recommended Generation Parameters (empero-ai Official)

Official generation guidance used by empero-ai/Qwen3.8-35B-A3B-Distill:

[!IMPORTANT]

🔍 Model Behavior vs. Quantization Fidelity

Sampling, prompting and upstream model behavior can materially affect the output. The Q4-class label describes this release's practical fidelity target; PPL is only one estimate of quantization fidelity and does not guarantee identical real-world behavior to BF16.

[!IMPORTANT]

CRITICAL ADVISORY FOR CODING WORKFLOWS: PREVENTING SYNTAX & TOKEN SWAPPING

Programming code necessarily repeats brackets, operators and indentation. A repeat penalty above 1.0 can suppress legitimate syntax tokens and cause character substitutions.

Recommended coding profile:

• repeat_penalty: 1.0
• presence_penalty: 0.0
• frequency_penalty: 0.0
• temperature: 0.60 (or lower for stricter deterministic code)
• top_p: 0.95
• top_k: 20

[!TIP]

NATIVE CHAT TEMPLATE

chat_template.jinja and the tokenizer configuration are included in this repository. Use the repository's native template with MLX-LM so system, user, assistant, reasoning and tool-call formatting follow the model's expected control-token structure.

Optional Support

If these MiniPlus or NanoPlus releases have been useful to you and you would like to support the work, you can do so voluntarily through https://ko-fi.com/isvalorum. Your contribution helps with evaluation, hosting, and future handcrafted quantizations. Every release will always remain free to download and use; there are no paywalled files, updates, or features.