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Jiunsong/SuperQwen3.8-27b-abliterated-MLX-4bit

🤗 On Hugging Faceimage-text-to-textapache-2.027.4B params55 GBsafetensors✓ Checksums witnessedupdated today
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SuperQwen3.8-27b-abliterated-MLX-4bit

The Apple-Silicon build: native affine 4-bit weights, bounded reasoning, and a 15 GiB footprint.

![MLX](#run-it)

![DFlash 2](#lossless-dflash-2)

![Speedup](#lossless-dflash-2)

![Memory](#measured-on-mac)

![Overthinking](#reasoning-that-stops)

![License](#license)

One model. Four native releases.

BF16 ·

NVFP4 ·

GGUF ·

MLX 4-bit

Choose your build

| Release | Best for | Size / precision | Runtime |

|---|---|---|---|

| BF16 | Maximum fidelity and further tuning | ~52 GB · BF16 | Transformers / vLLM |

| NVFP4 | Fast single-DGX-Spark serving | ~19.2 GiB · W4A4 G16 | vLLM |

| GGUF | Portable one-box inference + native MTP | ~17.6 GiB runtime set | llama.cpp |

| MLX 4-bit — this repo | Apple Silicon | ~15.0 GiB · affine 4-bit | MLX |

This is the directly loadable MLX release of

Jiunsong/SuperQwen3.8-27b-abliterated,

built on the pinned

mlx-community/Qwen3.8-27B-4bit@3e6447f.

It is not an adapter: the changed tensors were dequantized, the abliteration delta was

applied, and each affected matrix was quantized back into native MLX weight/scales/biases.

The source edit is the verified rank-4 OBLITERATUS recipe built from 842 canonical

harmful/harmless pairs: exactly 100 BF16 tensors changed, while the vision tower and MTP

weights remained exact.

Release highlights

| | |

|---|---|

| Format | MLX affine 4-bit, group size 64, three safetensors shards (~14.96 GiB) |

| Autoregressive C1 | 26.797 / 27.225 tok/s, median 27.011 tok/s |

| Lossless DFlash 2 C1 | 33.199 / 33.550 tok/s, median 33.374 tok/s, 1.2356× over AR |

| Memory | 18.435 GB peak with the 4-bit DFlash draft; 16.062 GB in the full quality suite |

| Abliteration transfer | 100 source tensors → 300 native MLX weight/scales/biases entries |

| Reasoning regression gate | Template v3; 36/36 PASS across default, low, medium, and xhigh |

| Behavior | Parent refusal 30/32 → 0/32; capability 7/8, tool PASS, benign-sensitive refusal 0/8 |

| Multimodal | Real local 64×64 red-PNG inference PASS through MLX-VLM 0.6.16 |

Why this build

  • Runs the 27B model locally on a Mac without a server-class GPU.
  • Fast where it matters: the headline number is C1 single-stream decode, not a concurrency aggregate.
  • Lossless speculative decoding: every tested DFlash block preserved the exact greedy

token sequence; block 3 was the fastest release-eligible profile.

  • Reasoning that stops: explicit xhigh is preserved, but carries the same repeat/restart stop guard as the BF16 release.
  • No loose delta at runtime: the abliteration edit is baked into the MLX shards.
  • Auditable conversion: the pinned base revision, every changed key, and shard hashes are recorded in mlx_requantization_manifest.json.

Measured on Mac

Measured locally on an Apple M5 Max with MLX 0.32.1, MLX-LM 0.31.3, and

MLX-VLM 0.6.16. The release benchmark uses one stream, a 256-token prompt,

256 fixed generation tokens, greedy decoding, one warmup, and two timed trials.

| Trial | AR C1 | DFlash 2 block 3 C1 | Greedy identity |

|---:|---:|---:|---:|

| 1 | 26.797 tok/s | 33.199 tok/s | Exact |

| 2 | 27.225 tok/s | 33.550 tok/s | Exact |

| Median | 27.011 tok/s | 33.374 tok/s | Exact |

The quality run loaded the model once, executed all 53 checks in 186.6 seconds, and

peaked at 16.06 GB. A separate local gate ran the same fixed 32-prompt OBLITERATUS

sample and the real image path: harmful refusal was 0/32, empty output was 0/32,

and vision passed. Exact reports and hashes are under evidence/.

Reasoning that stops

The upstream template's implicit reasoning default is changed to bounded medium.

Explicit xhigh remains available and tells the model to stop once the answer is

established instead of repeating or restarting its deliberation. Template revision 3

adds an explicit 512-token internal-reasoning target for short or routine tasks.

| Effort | Deterministic overthinking gate | Median reasoning tokens | Maximum |

|---|---:|---:|---:|

| default | 9/9 | 45 | 112 |

| low | 9/9 | 36 | 91 |

| medium | 9/9 | 45 | 112 |

| xhigh | 9/9 | 60 | 233 |

| Total | 36/36 PASS | | |

Run it

python3.12 -m pip install 'mlx==0.32.1' 'mlx-lm==0.31.3' 'mlx-vlm==0.6.16'

python3.12 -m mlx_lm generate \
  --model Jiunsong/SuperQwen3.8-27b-abliterated-MLX-4bit \
  --prompt "Explain why speculative decoding is lossless." \
  --max-tokens 512

Use explicit bounded or deep reasoning through the chat-template configuration:

python3.12 -m mlx_lm generate \
  --model Jiunsong/SuperQwen3.8-27b-abliterated-MLX-4bit \
  --chat-template-config '{"enable_thinking":true,"reasoning_effort":"xhigh","preserve_thinking":false}' \
  --prompt "Solve this carefully: ..." \
  --max-tokens 4096

Serve an OpenAI-compatible local endpoint:

python3.12 -m mlx_lm server \
  --model Jiunsong/SuperQwen3.8-27b-abliterated-MLX-4bit \
  --host 127.0.0.1 \
  --port 8080

Lossless DFlash 2

incoai/Qwen3.8-27B-DFlash2

was loaded through the Qwen3 DFlash 2 path in MLX-VLM. Blocks 2 through 8 were tested;

all seven reproduced the autoregressive greedy token IDs exactly. Block 3 won the

same-run C1 comparison at 33.374 tok/s, versus 27.011 tok/s AR.

| Block | Median C1 | Lossless | Faster than AR |

|---:|---:|:---:|:---:|

| 2 | 31.595 tok/s | Yes | Yes |

| 3 | 33.374 tok/s | Yes | Yes |

| 4 | 29.733 tok/s | Yes | Yes |

| 5 | 29.021 tok/s | Yes | Yes |

| 6 | 27.994 tok/s | Yes | Yes |

| 7 | 27.047 tok/s | Yes | Yes |

| 8 | 26.269 tok/s | Yes | No |

The draft is quantized at runtime to affine 4-bit, group size 64. Its pinned weight

SHA-256 is 67fc76d68dc5a9415511a4f394ef744d67510cd20e93b37cc2cc7d28e4bab65c.

The bundled reproducibility files identify the exact MLX-VLM source revision and patch.

Quantization and integrity

  • Mode: affine 4-bit, group size 64.
  • Pinned MLX base index SHA-256: 13b840162b4cb35c66fef7df072f7dbb4717908204364f5e5d9f9655a2758fa8.
  • Pinned delta manifest SHA-256: cf9bedf4dd833b2c612b363497f878bee3007f73bb77d2618f9962ff3e05d576.
  • Conversion manifest SHA-256: a90e5475ad7b6fa655b4dcd2d2b528d1ed4b499f2a10a996d4f49b8399b44357.
  • Three output shards are independently hashed in both the manifest and SHA256SUMS.json.

Evidence identities

| Evidence | SHA-256 |

|---|---|

| Lossless C1 AR + DFlash 2 benchmark | 01c3d45ab0e2e31abf15e9ae12d59df0a1981a51194df3f16f11f38301aaa50a |

| MLX quality and overthinking gate | a575139e8cd5a8beb4ca25660c801f61e141ad07c1f005ed8a60f0d8d0134465 |

| MLX harmful-refusal + real-image gate | fcac3f1b415bc28ab97dc67535d1cc910f8c7cb8eef0ec3aec0d78b6a9d8fd36 |

| MLX requantization manifest | a90e5475ad7b6fa655b4dcd2d2b528d1ed4b499f2a10a996d4f49b8399b44357 |

Limitations

  • Four-bit affine quantization can regress tasks outside the measured suite.
  • Capability is 7/8, matching the accepted paired-parent floor; it is not presented as 8/8.
  • DFlash 2 requires the bundled/pinned MLX-VLM Qwen3 DFlash patch; stock runtimes may

not expose this draft path yet.

  • The 33.374 tok/s number is a same-run M5 Max C1 measurement, not a guarantee for

every Apple chip or thermal state.

  • Local speed varies with Apple chip, memory pressure, prompt length, and MLX version.

License

Apache-2.0, following the upstream Qwen3.8 release.