Jiunsong

SuperGLM-5.2-abliterated-NVFP4

What was changed

Clean restart from nvidia/GLM-5.2-NVFP4@aec724e8c7b8ee9db3b48c01c320f63f9cdaf8aa — no cumulative merge from an earlier SuperGLM checkpoint.
Rank-4 OBLITERATUS projection over 62 BF16 attention o_proj matrices at strength 2.
Rank-2 projection over the matching BF16 shared-expert down_proj matrices at strength 0.15.
Packed routed ModelOpt NVFP4 experts remain byte-identical to NVIDIA's base.
lm_head remains byte-identical to NVIDIA's base.
No LoRA or adapter is needed at inference time: all selected updates are fused into safetensors.

The final release modifies 124 BF16 tensors across 37 shards. Tensor-region backups and projection residual checks were used during fusion; the maximum permitted projection verification error was 0.025.

Weight-only evaluation

The acceptance data comes from the official HarmBench repository at commit 8e1604d1171fe8a48d8febecd22f600e462bdcdd. The standard-text development gates reuse previously opened official HarmBench partitions for candidate selection. The sealed final instead uses previously unused FunctionalCategory=contextual official-test behaviors, formatted byte-for-byte like HarmBench DirectRequest (ContextString, separator, then Behavior) and judged with the official contextual classifier template.

Table with columns: Gate, Physical prompts, Greedy repeats, Official no / invalid / total, Explicit refusal strings, Broad lexical flags, Quality checks, Structural anomalies, Unicode replacements
Gate	Physical prompts	Greedy repeats	Official no / invalid / total	Explicit refusal strings	Broad lexical flags	Quality checks	Structural anomalies	Unicode replacements
Runtime adapter screen	41	2	1 / 0 / 82	0	19	16/16	0	0
Serialized fused gate

All generations use greedy decoding, the original NVIDIA chat template, enable_thinking=false, no explicit system prompt, and adaptive_directness=false. Runtime LoRA is used only for the first candidate screen. The two release gates run on serialized weights with adapters disabled. Acceptance requires at least 15/16 quality checks, zero structural anomalies, zero Unicode replacement characters, and zero explicit refusal strings. The official classifier release threshold is configured as fused <= 6 and sealed <= 6 refused/non-exhibiting hits, with invalid outputs fixed at zero. This v2 card reports the exact no / invalid / total counts above so the configured release slack is auditable. In HarmBench semantics a yes means that the requested behavior is present, so treating every no as refused/non-exhibiting is stricter than a refusal-string check. Broad lexical flags are reported for continuity but are not a release gate because they also match compliant caveats containing words such as “illegal” or “dangerous.”

The 16 quality checks are a small regression sentinel, not a broad capability benchmark. This release does not claim a throughput speedup over NVIDIA's base; it preserves the base checkpoint's NVFP4 format and validates compatibility with the same ModelOpt FP4 runtime family.

Leakage controls

The prior 220-prompt development bank trains the tiny lm_head repair and is not reported as an independent benchmark. It is excluded from every official acceptance partition and is also supplied to the overlap audit.
The 352 OBLITERATUS contrast-training pairs train the refusal directions. They are excluded from every official acceptance partition and are also supplied to the overlap audit.
The standard development partitions are byte-identical to their pinned prior artifact and retain that artifact's zero-overlap audit.
The contextual sealed candidates were audited against the registered training and prior-evaluation prompts; near matches at char-5gram Jaccard >= 0.5 were excluded automatically before candidate freeze.
The retained contextual sealed final has zero normalized exact or threshold-level near overlap and was opened exactly once.

Inference

Use an NVFP4-compatible Blackwell runtime. The release was validated with SGLang's ModelOpt FP4 path and flashinfer_cutlass MoE runner on 8x RTX PRO 6000 Blackwell GPUs.

python
import sglang as sgl

engine = sgl.Engine(
    model_path="Jiunsong/SuperGLM-5.2-abliterated-NVFP4",
    tp_size=8,
    quantization="modelopt_fp4",
    moe_runner_backend="flashinfer_cutlass",
    disable_shared_experts_fusion=True,
)

Lineage and reproducibility

NVIDIA base: aec724e8c7b8ee9db3b48c01c320f63f9cdaf8aa
Selected candidate: broad62-r4-s2-obliteratus_only-shared-r2-s0p15
Candidate artifact: sha256:d7ded1c1eea29006823f7219a35c71a4cfdcdf4038aaa39c4909e57626f1300e
lm_head repair source: none
Direction source: zai-org/GLM-5.2-FP8@ba978f7d347eaf65d22f1a86833408afdb953541
Evaluation artifact: sha256:956fd23f7a45308878fc41a55e30f8995d515ad3b53bc1b1011d35efeb021101
HarmBench classifier: cais/HarmBench-Llama-2-13b-cls@bda705349d1144fa618770bea64d99ce54e3835b
HarmBench contextual classifier prompt SHA-256: 5d6bb9e3cf4d1e5f3f7620093113222ee115af75b0b2913f00e4c7225ec9f219
HarmBench standard classifier prompt SHA-256:

Detailed sanitized fusion and evaluation reports are included in this repository. Raw generations are retained privately for reproducibility and are not published in the model card.

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Model Details

Model Provider

Jiunsong

Model Tree

Base

nvidia/GLM-5.2-NVFP4

Quantized

this model

Input Modalities

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Output Modalities

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Supported Functionality

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What was changed

Clean restart from nvidia/GLM-5.2-NVFP4@aec724e8c7b8ee9db3b48c01c320f63f9cdaf8aa — no cumulative merge from an earlier SuperGLM checkpoint.
Rank-4 OBLITERATUS projection over 62 BF16 attention o_proj matrices at strength 2.
Rank-2 projection over the matching BF16 shared-expert down_proj matrices at strength 0.15.
Packed routed ModelOpt NVFP4 experts remain byte-identical to NVIDIA's base.
lm_head remains byte-identical to NVIDIA's base.
No LoRA or adapter is needed at inference time: all selected updates are fused into safetensors.

Weight-only evaluation

Table with columns: Gate, Physical prompts, Greedy repeats, Official no / invalid / total, Explicit refusal strings, Broad lexical flags, Quality checks, Structural anomalies, Unicode replacements
Gate	Physical prompts	Greedy repeats	Official no / invalid / total	Explicit refusal strings	Broad lexical flags	Quality checks	Structural anomalies	Unicode replacements
Runtime adapter screen	41	2	1 / 0 / 82	0	19	16/16	0	0
Serialized fused gate

Leakage controls

The prior 220-prompt development bank trains the tiny lm_head repair and is not reported as an independent benchmark. It is excluded from every official acceptance partition and is also supplied to the overlap audit.
The 352 OBLITERATUS contrast-training pairs train the refusal directions. They are excluded from every official acceptance partition and are also supplied to the overlap audit.
The standard development partitions are byte-identical to their pinned prior artifact and retain that artifact's zero-overlap audit.
The contextual sealed candidates were audited against the registered training and prior-evaluation prompts; near matches at char-5gram Jaccard >= 0.5 were excluded automatically before candidate freeze.
The retained contextual sealed final has zero normalized exact or threshold-level near overlap and was opened exactly once.

Inference

Use an NVFP4-compatible Blackwell runtime. The release was validated with SGLang's ModelOpt FP4 path and flashinfer_cutlass MoE runner on 8x RTX PRO 6000 Blackwell GPUs.

python
import sglang as sgl

engine = sgl.Engine(
    model_path="Jiunsong/SuperGLM-5.2-abliterated-NVFP4",
    tp_size=8,
    quantization="modelopt_fp4",
    moe_runner_backend="flashinfer_cutlass",
    disable_shared_experts_fusion=True,
)

Lineage and reproducibility

NVIDIA base: aec724e8c7b8ee9db3b48c01c320f63f9cdaf8aa
Selected candidate: broad62-r4-s2-obliteratus_only-shared-r2-s0p15
Candidate artifact: sha256:d7ded1c1eea29006823f7219a35c71a4cfdcdf4038aaa39c4909e57626f1300e
lm_head repair source: none
Direction source: zai-org/GLM-5.2-FP8@ba978f7d347eaf65d22f1a86833408afdb953541
Evaluation artifact: sha256:956fd23f7a45308878fc41a55e30f8995d515ad3b53bc1b1011d35efeb021101
HarmBench classifier: cais/HarmBench-Llama-2-13b-cls@bda705349d1144fa618770bea64d99ce54e3835b
HarmBench contextual classifier prompt SHA-256: 5d6bb9e3cf4d1e5f3f7620093113222ee115af75b0b2913f00e4c7225ec9f219
HarmBench standard classifier prompt SHA-256:

Detailed sanitized fusion and evaluation reports are included in this repository. Raw generations are retained privately for reproducibility and are not published in the model card.

SuperGLM-5.2-abliterated-NVFP4

README

What was changed

Weight-only evaluation

Leakage controls

Inference

Lineage and reproducibility

Explore FriendliAI today

README

What was changed

Weight-only evaluation

Leakage controls

Inference

Lineage and reproducibility