Step-3.7-Flash-AEON-Ultimate-Abliterated-GGUF

⚠️ KNOWN BROKEN — do not use for inference yet (fix in progress)

These GGUFs currently produce garbled output. The cause is NOT an upstream llama.cpp engine bug — any earlier note on this card claiming an "engine-blocked" / PR-#23845 dependency is outdated; please disregard it. The official Step-3.7 GGUF runs fine on a correctly-built llama.cpp.

Root cause: our Expert-Granular Abliteration interacts badly with low-bit quantization (same as our NVFP4) — the ablation zeroes a residual-stream subspace that is exact at BF16 but re-corrupted by quant noise at 3–4 bit → garbage. Coherent output requires BF16.

✅ Use instead: the BF16 release. A milder-ablation re-quant that survives low-bit is being validated; these files will be replaced or withdrawn once fixed.

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Step-3.7-Flash-AEON-Ultimate-Abliterated-GGUF ⚠️ EXPERIMENTAL

GGUF quants of AEON-7/Step-3.7-Flash-AEON-Ultimate-Abliterated-BF16 (198B / ~11B-active sparse-MoE vision-language thinking model), built for single-DGX-Spark deployment.

⚠️ EXPERIMENTAL — NOT YET FUNCTIONAL (engine-blocked)

These GGUFs currently produce garbage output on every available GGUF runtime (llama.cpp, Ollama, LM Studio, KoboldCpp — all share the same engine). The cause is not these files — the quantized weights, abliteration, and tokenizer are all verified correct. The blocker is an open upstream bug in llama.cpp's Step-3.7 inference graph: Step-3.7 is routed through the step35 compute graph, which mis-runs its forward pass (garbage from the first token, independent of bit-width — even the near-lossless q8_0 is affected).

Dependency to use these properly: a corrected llama.cpp Step-3.7 inference implementation (tracking ggml-org/llama.cpp#23845 / a StepFun-fork fix). They are expected to work as-is once that lands — no re-quantization needed.

(Tokenizer note: it is correct. The right pre-tokenizer is deepseek-v3 — if a build defaults otherwise, pass --override-kv tokenizer.ggml.pre=str:deepseek-v3. This is a minor correctness item, not the blocker.)

Status: experimental until functionality is confirmed on a fixed engine. For working deployment today, use the BF16 or NVFP4 releases (table below).

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Model family — formats, quality, validation

Legend: ✅ working today · ⚠️ experimental, awaiting the upstream engine fix.

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Two independent things this build is

1. Abliterated (behavior) — refusals removed via Expert-Granular Abliteration across all 288 experts (refusal subspace collapsed from Cohen's d≈10 → 0.35). Uncensored.
2. Precisely quantized (fidelity) — a data-driven, per-component mixed-precision scheme + our own imatrix, not a uniform low-bit dump. Capable + still-uncensored after quantization (when the engine runs it).

Quantization methodology (data-driven selective allocation)

Per-component bits from our outlier study + refusal-subspace map (not stock Q3_K_M):

Plus a custom imatrix (diverse general/reasoning/code calibration).

Inference (once a fixed Step-3.7 engine is available)

# Build the StepFun step3.7 llama.cpp fork (or a future fixed mainline)
git clone -b step3.7 https://github.com/stepfun-ai/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=121 && cmake --build build -j --config Release

# Serve (shards auto-load from the first piece; mmproj for vision)
./build/bin/llama-server \
  -m  Step-3.7-Flash-…-Q3_K_M-00001-of-0000N.gguf \
  --mmproj mmproj-step3.7-flash-f16.gguf \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  -c 131072 --parallel 4 -ngl 999 --flash-attn \
  --override-kv tokenizer.ggml.pre=str:deepseek-v3 \
  --host 0.0.0.0 --port 8080

This will emit garbage until the upstream Step-3.7 graph bug (#23845) is fixed. Q3_K_M targets one Spark with moderate KV headroom; IQ1_M maximizes headroom (quality-tolerant, below the IQ2 cliff); q8_0 is the near-lossless base.

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Quantized on NVIDIA B300 via the StepFun step3.7 llama.cpp fork + custom imatrix, from the AEON-Ultimate abliterated BF16. Base model © StepFun AI, Apache-2.0.

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