license: apache-2.0
library_name: gguf
pipeline_tag: text-generation
base_model: Jiunsong/SuperHY3-abliterated-NVFP4
base_model_relation: quantized
tags:

• hy3
• hunyuan
• moe
• text-generation
• conversational
• abliterated
• obliteratus
• supertune
• gguf
• iq2_m
• quantized
• llama-cpp

This is a complete model, not a LoRA package. The final OBLITERATUS and
SuperTune deltas are fused into the GGUF weights, and the exact release chat
template is embedded in GGUF metadata. Download the shards, point llama.cpp at
the first shard, and run it directly.

Release Highlights

What makes this release different

• Direct-response behavior: the measured harmful-prompt refusal rate fell
  from 31/32 to 0/32, while the harmless split remained at 0/32.
• Fully fused behavior: OBLITERATUS and SuperTune are part of the weights
  instead of runtime adapter composition.
• Selective precision: only the 40 edited projections are stored as F16;
  all untouched quantized tensors retain their original bytes.
• Measured post-training gains: IFEval strict, HumanEval+, and MBPP+ each
  improved by one point in the deterministic 500-prompt replay.
• Single-node deployment: the model was load-tested on one 128 GB NVIDIA
  DGX Spark with the full model resident through llama.cpp.
• Auditable evidence: fusion, tensor validation, raw-response audit,
  refusal comparison, benchmark rows, and checksums are included in this repo.
• Complete reporting: benchmark regressions are shown alongside gains.

Benchmark Snapshot

The comparison uses the same preserved 500 prompts, with 100 prompts per
task, greedy direct decoding, and the same scorer on both sides. IFEval uses
Google's official strict instruction checker at commit
3dfaaefe9e355f959aafc046ab5aa375dc93b235.

Candidate invalid-response, blank-response, and thought-leak ratios were all
0.0 across the official 500-item comparison.

For reproducibility, the wrapper seeds Python's upstream fallback RNG from
sha256(prompt)[:8] before each strict and loose score. Two independent
rescoring passes produced identical row-level results.

OBLITERATUS + SuperTune

1. Direction discovery: OBLITERATUS 0.1.2 built difference-of-means refusal
   directions from 32 paired prompts.
2. Validated abliteration: selected attention-output directions were
   applied to layers 39-77.
3. Quality recovery: a rank-1 SuperTune update, orthogonalized against the
   refusal direction, was fused into layer 78.
4. Calibrated reinforcement: selective residual blocks were applied to
   layers 59-68 at 0.20 and layers 39-48 at 0.05 after blockwise stress
   testing.
5. Adversarial selection: stronger candidates were rejected when they
   introduced stray-script contamination, loops, or benchmark loss.
6. Final fusion: all active deltas were summed in F32 directly against the
   original IQ2_M source, then cast once to F16. No sequential F16 re-merge was
   used, and no routed MoE expert tensor was changed.

Refusal and Output Integrity

The complete 32-pair OBLITERATUS suite contains 32 harmful and 32 harmless
prompts.

Across all 64 final responses, automated checks found:

• 0 blank outputs
• 0 special-token leaks
• 0 Unicode replacement characters
• 0 unexpected CJK fragments
• 0 n-gram or character loops
• 0 request errors

A separate 12-case fused-GGUF audit passed identity, JSON-only output, tool
calls, no-tool behavior, repetition limits, Korean, defensive security,
hidden-prompt boundaries, gibberish handling, code repair, Hindi, and Kannada.

GGUF Build

The source quant is
vcruz305/Hy3-GGUF, IQ2_M. The Hub
parent points to
SuperHY3-abliterated-NVFP4
so this edition appears in the final model's quantized lineage. This GGUF is an
independently fused quantized sibling, not a byte conversion of the NVFP4 files.

The GGUF primary file type remains IQ2_M. The 40 attention-output tensors were
dequantized to F32, merged with the final rank-1 deltas, and written as F16.
The other 1,258 tensors were copied without requantization.

Download

hf download Jiunsong/SuperHY3-abliterated-gguf \
  --include "SuperHY3-abliterated-IQ2_M-*.gguf" \
  --local-dir SuperHY3-abliterated-gguf

The release is split into three automatically discoverable shards:

Run sha256sum -c SHA256SUMS after download to verify all three files.

Serving with llama.cpp

Use a llama.cpp build with Hy3 support. Point -m at the first shard; the
remaining shards are discovered automatically.

./llama-server \
  -m SuperHY3-abliterated-gguf/SuperHY3-abliterated-IQ2_M-00001-of-00003.gguf \
  -a superhy3 \
  -cmoe \
  -ngl 999 \
  -c 8192 \
  -np 4 \
  -ctk q8_0 \
  -ctv q8_0 \
  --host 0.0.0.0 \
  --port 8000 \
  --jinja \
  --skip-chat-parsing

The command above is the tested, memory-conservative DGX Spark configuration.
On systems with separate high-capacity VRAM, omit -cmoe to keep MoE experts
on the GPU.

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="superhy3",
    messages=[
        {"role": "user", "content": "Explain mixture-of-experts routing."},
    ],
    temperature=0.9,
    top_p=1.0,
    extra_body={
        "chat_template_kwargs": {"reasoning_effort": "no_think"},
    },
)
print(response.choices[0].message.content)

Use reasoning_effort="high" for deeper reasoning and "no_think" for direct
responses. The bundled template also supports tool definitions and structured
tool calls.

Quantized Editions

Release Integrity

• The fused GGUF contains exactly 1,298 tensors.
• Exactly 40 expected attn_output tensors changed from IQ3_S to F16.
• 1,258 untouched tensors, totaling 99,426,956,288 bytes, are byte-identical to
  the source quant.
• The embedded tokenizer.chat_template exactly matches the release template.
• general.file_type remains the source IQ2_M value.
• Full-file XXH64: d054554b53e49801.
• Full-file SHA-256 before release splitting:
  727a7d4b7ce101eac5190f7e4f22d8a07556860bd8205a19b01551ec19936d9c.
• The unsplit model loaded successfully in llama.cpp on one DGX Spark.
• The split release auto-discovered all three shards and passed fresh smoke
  inference without a runtime adapter.
• The 12-case fused runtime audit passed with 0 blockers.

The repository includes machine-readable validation, benchmark, refusal,
runtime-audit, fusion-provenance, and checksum reports.

Hardware Notes

• The fused model is 95.1 GiB before shard packaging.
• A 128 GB unified-memory system can run the tested 8K total context / 4-slot
  configuration with CPU-resident MoE experts.
• The model metadata advertises a 262,144-token training context, but this
  release was not validated at that length.
• Keep enough memory for KV cache and runtime overhead. Lower -c or -np
  first when deploying near the memory limit.

Limitations

• IQ2_M is an aggressive low-bit format; higher-precision editions may retain
  more capability.
• GPQA Diamond and MMLU-Pro are lower than the original in the published replay;
  the complete result is retained above instead of reporting only gains.
• Native 262K long-context quality was not tested.
• Hy3 MTP tensors are preserved, but speculative decoding depends on llama.cpp
  support for this architecture.
• Abliteration reduces learned refusal behavior and may produce content the
  original model would decline. Deployment policy, access control, monitoring,
  and legal compliance remain the operator's responsibility.

License

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