license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3.8-27B
base_model_relation: finetune
tags:

• qwen3.8
• qwen3.5
• multimodal
• image-text-to-text
• reasoning
• tool-calling
• long-context
• uncensored
• abliterated
• obliteratus
• fp8
• w8a8
• compressed-tensors
• vllm
• h100
• h200
• speculative-decoding
  language:
• en
• ko

SuperQwen3.8-abliterated-100-fp8

A business-oriented, refusal-reduced Qwen3.8-27B release in native Hopper FP8 W8A8 format.

What this release is

SuperQwen3.8-abliterated-100-fp8 is a directly loadable Qwen3.8-27B derivative for
teams that need fewer blanket refusals in legitimate research, security, policy,
healthcare, retail, compliance, and creative workflows. It keeps the upstream
multimodal, reasoning, long-context, and tool-use paths while applying a wider
OBLITERATUS-derived refusal-subspace projection than the earlier SuperQwen3.8 release.

The public artifact is already quantized as FP8_DYNAMIC W8A8 using
compressed-tensors: per-channel FP8 weights and dynamic per-token FP8 activations.
That is the format intended for H100/H200 Tensor Cores; no calibration dataset is
required at load time.

Release highlights

Stronger refusal reduction

This release was rebuilt from the pinned parent rather than patched from an already
modified checkpoint. OBLITERATUS revision
a5a1ffa5849b442cf188b3c03fd4de71ddf5bdcc supplied 842 canonical harmful/harmless
pairs. A per-layer rank-8 subspace was fit and projected through the residual output
paths of layers 12–63, plus bounded projections of the embeddings and output head.

The independent geometric reload gate passed all 106 modified tensors. Typical
post-projection component residuals were about 0.12–0.17%, below the fixed 2% ceiling.
The direction artifact SHA-256 is
f2a0ba2b3c9e3faad158ac475732f48c84561c6f7a512cfd6a0e69558d64fbe1 and the
independently reloaded BF16 tensor-manifest identity is
1679546a7769831a90f303d5389b168d2218a04006b6e09bc2c6e9bd7f57a2b2.

“Abliterated” means the measured refusal direction was substantially reduced. It does
not mean every possible refusal has disappeared, and it does not turn generated text
into verified business, legal, medical, or security advice.

FP8 checkpoint

Structural verification found 496 language-backbone FP8 scale tensors, no quantization
sidecars on protected modules, and exact equality for every protected tensor. The full
repository is covered by SHA256SUMS.json.

H100 / H200 serving

This checkpoint is sized for TP=1 on one H100 80GB or H200 141GB. Use TP=2 when
your workload values prefill concurrency or operational headroom more than single-GPU
latency. The commands below are deployment profiles, not fabricated Hopper benchmarks.

One GPU, production baseline

vllm serve Jiunsong/SuperQwen3.8-abliterated-100-fp8 \
  --served-model-name SuperQwen3.8-abliterated-100-fp8 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.92 \
  --kv-cache-dtype fp8 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --async-scheduling \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3

Two GPUs

vllm serve Jiunsong/SuperQwen3.8-abliterated-100-fp8 \
  --served-model-name SuperQwen3.8-abliterated-100-fp8 \
  --tensor-parallel-size 2 \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.92 \
  --kv-cache-dtype fp8 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --async-scheduling \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3

For H100/H200, keep the native compressed-tensors FP8 path. Converting this release
back to BF16 before serving discards the point of the Hopper build. Benchmark your own
prompt lengths and concurrency before fixing production limits.

Speculative decoding

The repository retains the exact native Qwen MTP head. K=5 is the selected fast
profile. It passed the complete capability, tool, benign-sensitive, bounded-reasoning,
vision, 8K throughput, and 32,811-token retrieval gates. On the measured GB10 runtime:

K=5 is about 2.90× the measured short-prompt K=0 decode rate. Across the final
K=5 validation process the server accepted 1,587 of 3,135 drafted token positions;
acceptance varied by workload. The bundled launcher therefore defaults to K=5 and
does not combine speculation with prefix caching. Set MTP_TOKENS=0 for the most
conservative path.

Measured GB10 baseline

The reproducible non-speculative benchmark follows the post-first-token decode window
used by MiaAI-Lab/sparkDash at commit
bf2709a80ef25d0e1a6ee41efec4c9b8042a5b8b.

These are DGX Spark / GB10 measurements in eager mode. They are not estimates for
H100 or H200. Hopper owners should expect different results and should publish the
exact GPU, vLLM revision, prompt tokens, output tokens, concurrency, and decode window
when comparing deployments.

Release gates

The release gate stores prompt and output hashes rather than redistributing raw test
content.

API example

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
response = client.chat.completions.create(
    model="SuperQwen3.8-abliterated-100-fp8",
    messages=[{"role": "user", "content": "Draft a concise supplier risk memo."}],
    temperature=0.2,
)
print(response.choices[0].message.content)

For bounded reasoning, pass chat_template_kwargs through your vLLM client:

extra_body={"chat_template_kwargs": {"enable_thinking": True, "reasoning_effort": "medium"}}

Limitations

• Abliteration reduces measured refusal behavior; it does not guarantee universal
  compliance, factuality, safety, or suitability for a particular business decision.
• FP8 can regress workloads outside the measured suites. Validate your domain data.
• Tool calls must be authorized, sandboxed, logged, and checked by the application.
• Long-context capacity is not the same as perfect long-context recall.
• Speed varies with GPU, driver, vLLM build, prompt length, output length, batching,
  multimodal inputs, and sampling settings.
• H100/H200 commands are optimized launch guidance; only GB10 numbers are presented as
  measurements in this card.

Evidence identities

The final public artifact includes hash-only release reports, quantization verification,
the exact build recipe, and a file-by-file SHA-256 manifest. Large files are uploaded as
Git LFS objects and their remote LFS OIDs are verified against the local SHA-256 values
before the repository is made public.

License

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