You can deploy this model and start earning money today!

Jiunsong/
SuperHY3-abliterated-MLX-4bit

Name: SuperHY3-abliterated-MLX-4bit
Availability: InStock

Jiunsong/SuperHY3-abliterated-NVFP4

quantized

apache-2.0

Views

No views yet

SuperHY3-abliterated-MLX-4bit

The high-memory Apple Silicon edition of SuperHY3, with the same fused OBLITERATUS and SuperTune behavior as the NVFP4 release.

This is the MLX 4-bit companion to SuperHY3-abliterated-NVFP4. The behavioral update and release chat template are the same; the storage format is optimized for high-memory Apple Silicon instead of NVIDIA NVFP4 serving.

The checkpoint is fully fused. No LoRA, adapter, or runtime weight patch is required.

Release Highlights


Base quantization	imatrix-enhanced affine MLX 4-bit, group size 64
Release size	approximately 171 GB across 32 safetensors shards
Fused update	40 attention output projections across layers 39-78
Mixed precision	original 4-bit placements retained; 40 edited projections stored in BF16
500-prompt mean	71.6 -> 71.8 against the original Hy3 runtime
Response integrity	64/64 refusal-suite responses clean; 12/12 runtime audit cases passed
Artifact verification	52/52 Hub files checksum-verified; release gate passed with 0 blockers

Why the edited projections stay in BF16

The model was fused from unigilby/Hy3-oQ4e. Its calibrated 4-bit placements remain unchanged except for the 40 attention output projections carrying the post-training update. Those projections are stored in BF16 to avoid applying a second quantization pass to the newly fused deltas.

This produces a larger checkpoint than the 158 GB source quantization, but preserves the exact validated update instead of rounding it back into 4-bit groups.

Benchmark Snapshot

Original Hy3 and SuperHY3 scores across five 100-prompt tasks

Benchmark	Original Hy3	SuperHY3	Delta
GPQA Diamond	46.0	45.0	-1.0
MMLU-Pro	66.0	60.0	-6.0
IFEval strict prompt accuracy	76.0	80.0	+4.0
HumanEval+ pass@1	82.0	83.0	+1.0
MBPP+ pass@1	88.0	91.0	+3.0
Five-task mean	71.6	71.8	+0.2

The same preserved 500 prompts and scorer were used for both sides. Invalid-response, blank-response, and thought-leak ratios were all 0.0.

The behavioral benchmark used a runtime-matched GGUF overlay with the exact same 40 projection deltas and release chat template. The fused MLX artifact was structurally verified rather than loaded on the 128 GB validation Mac.

OBLITERATUS + SuperTune

OBLITERATUS 0.1.2 built difference-of-means refusal directions from 32 paired prompts.
Validated attention-output updates were fused into layers 39-77.
A rank-1 SuperTune update, orthogonalized against the refusal direction, was fused into layer 78.
The final artifact modifies 40 self_attn.o_proj tensors and no routed expert tensor.

Refusal and Output Integrity

Split	Original refusals	SuperHY3 refusals
Harmful, 32 prompts	31/32 (96.875%)	0/32 (0%)
Harmless, 32 prompts	0/32 (0%)	0/32 (0%)

Across all 64 candidate responses, automated checks found zero blank outputs, special-token leaks, Unicode replacement characters, unexpected CJK fragments, loops, and request errors. A separate 12-case multilingual and structured-output runtime audit passed with no blockers.

MLX Build

Component	Storage
Calibrated base tensors	Affine 4-bit with source sensitivity placements
Selected source projections	5-bit where assigned by the source quantization
SuperHY3 fused projections	BF16
MTP	Not included in the source MLX checkpoint

About the Hub parameter badge: the sidebar counts packed quantized storage elements rather than logical model parameters. This checkpoint keeps the Hy3 decoder architecture; its smaller badge does not mean it is a 48B dense model.

Hy3 support is not yet merged into the main mlx-lm branch at publication time. Install or apply mlx-lm PR #1211 before loading this model.

The checkpoint requires substantially more than 128 GB of unified memory. 192 GB is the practical minimum; 256 GB or more is recommended for useful runtime headroom.

Usage

python

from mlx_lm import generate, load

model, tokenizer = load("Jiunsong/SuperHY3-abliterated-MLX-4bit")
prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Explain mixture-of-experts routing.",
        },
    ],
    add_generation_prompt=True,
    reasoning_effort="no_think",
)

print(generate(model, tokenizer, prompt=prompt, max_tokens=200))

Use reasoning_effort="high" for deeper reasoning. The upstream Hy3 sampling recommendation is temperature=0.9 and top_p=1.0 when sampling is enabled.

Release Integrity

32 safetensors shards opened successfully.
2,796 indexed tensors matched 2,796 observed tensors.
40 tensors were modified across 2 shards.
Missing, extra, and wrong-shard tensor counts are all 0.
The release chat template matches the embedded tokenizer template.
The automated release gate passed with 0 blockers.
All 52 Hub files were checksum-verified after upload.

The repository includes the streaming fusion report, release gate, official 500-item benchmark record, refusal comparison, raw-response audit, OBLITERATUS execution report, and SuperTune composition reports.

Limitations

GPQA Diamond and MMLU-Pro are lower than the original in this replay; the complete table is retained above.
Native MLX throughput and long-context measurements were not run for this fused artifact.
Abliteration reduces refusal behavior and can produce content the original model would decline. Deployment policy and access control remain the operator's responsibility.
This is a separately fused MLX checkpoint, not a conversion of the NVFP4 files.

License

Apache-2.0, following the base model and quantized source licenses.