license: apache-2.0
base_model: orcarouter/Qwen3.8-27B-Uncensored
base_model_relation: quantized
pipeline_tag: text-generation
library_name: llama.cpp
language:

• en
• zh
  tags:
• qwen
• qwen3.8
• qwen3_5
• orcasaq2
• gguf
• llama.cpp
• quantization
• mixed-precision
• uncensored
• abliterated
• reasoning
• function-calling
• dflash2
• long-context

OrcaSAQ2 27B Cyber · Uncensored · GGUF

Uncensored 27B reasoning for llama.cpp, Ollama and LM Studio.

54.7 GB → 15.7 GB · +0.80% PPL · 94.4% Top-1 Agreement · 0.020 KLD · 262K Context

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27B uncensored. 15.7 GB.

OrcaSAQ2 27B Cyber Uncensored GGUF compresses the uncensored Qwen3.8-27B from a
54.7 GB BF16 checkpoint to 15.7 GB while preserving extremely high fidelity to the
original model.

Built for: local deployment · coding · tool use · reasoning · defensive red teaming · vulnerability research · authorized security testing

OrcaSAQ2 is a proprietary sensitivity-aware mixed-precision quantization system developed
by OrcaRouter and its research team behind.

It is optimized around one goal: Preserve as much useful model behavior as possible inside a
practical GPU memory envelope.

The resulting checkpoint provides:

• 71.3% smaller storage footprint
• only +0.80% perplexity versus BF16
• 94.4% token-level Top-1 agreement
• 0.020 mean KLD
• 262K context
• thinking mode
• tool calling
• DFlash2 speculative decoding
• local serving through llama.cpp

At a glance

3.5× smaller. +0.80% perplexity.

The point is not the file size.

The point is what survives.

BF16 fidelity

All numbers below are measured using these exact OrcaSAQ2 weights against the uncensored
BF16 reference through the same evaluation path.

WikiText-2

16,376 predicted tokens

Perplexity

BF16       5.6532  ████████████████████████████████████████
OrcaSAQ2   5.6961  ████████████████████████████████████████

Delta: +0.80%

Top-1 agreement

OrcaSAQ2 vs BF16

████████████████████████████████████████████████░░  94.4%

Model footprint

Qwen3.8-27B Cyber Uncensored BF16

██████████████████████████████████████████████████  54.7 GB

OrcaSAQ2

██████████████                                      15.7 GB

71.3% smaller.

Architecture

Production serving

Up to 27.6 tok/s single-stream on a 24 GB GPU

Single stream, greedy, measured in the official llama.cpp CUDA container.

DFlash2 speculative decoding

Single-stream decode

DFlash2 off  █████████████████████████████       20.5 tok/s

DFlash2 on   ████████████████████████████████████████
                                                 27.6 tok/s

+35% single-stream decode throughput

DFlash2 drafts a block of tokens in one pass and the model verifies them. Decoding is
lossless: greedy output matches the undrafted model exactly.

llama-cli \
  -m OrcaSAQ-2-27B-Uncensored.gguf \
  --spec-draft-model Qwen3.8-27B-DFlash2-Q8_0.gguf \
  --spec-draft-ngl 99 \
  -ngl 99

Public drafters for this base model:
z-lab/Qwen3.8-27B-DFlash2-GGUF
(Q8_0 is 2.06 GB).

DFlash2 trades VRAM for interactive decode performance. It is particularly useful for:

• coding assistants
• interactive agents
• terminal agents
• tool-heavy applications
• low-concurrency inference

27B on a 24 GB GPU

OrcaSAQ2's checkpoint is 15.7 GB.

A full offload occupies 14.9 GB of VRAM, and adding the DFlash2 drafter brings the total
to 18.1 GB — leaving headroom on a 24 GB card for context and batching.

That makes deployment possible on hardware that cannot hold the original 54.7 GB BF16
checkpoint.

24 GB GPU
┌───────────────────────────────────────────┐
│                                           │
│   OrcaSAQ2 (full offload)   14.9 GB       │
│   ████████████████████████████            │
│                                           │
│   DFlash2 drafter            3.2 GB       │
│   ██████                                  │
│                                           │
│   Remaining                 ~5.9 GB       │
│   ███████████                             │
│                                           │
└───────────────────────────────────────────┘

Actual usable memory depends on:

• context length
• batch size
• KV-cache configuration
• drafter configuration

A practical starting point for a 24 GB GPU is approximately 32K interactive context with
DFlash2 enabled, then tune based on the workload.

The model architecture supports up to 262K context.

On a 16 GB GPU the model still runs at full offload without a drafter, at 20.5 tok/s.

Quickstart

Download

hf download orcarouter/OrcaSAQ-2-27B-Uncensored-GGUF \
  --local-dir ./OrcaSAQ-2-27B-Uncensored-GGUF

Run

llama-cli \
  -m ./OrcaSAQ-2-27B-Uncensored-GGUF/OrcaSAQ-2-27B-Uncensored.gguf \
  -ngl 99 \
  -c 32768

Serve

llama-server \
  -m ./OrcaSAQ-2-27B-Uncensored-GGUF/OrcaSAQ-2-27B-Uncensored.gguf \
  -ngl 99 \
  -c 32768 \
  --host 0.0.0.0 \
  --port 8080

OpenAI-compatible API

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed",
)

response = client.chat.completions.create(
    model="OrcaSAQ-2-27B-Uncensored",
    messages=[
        {
            "role": "user",
            "content": "Analyze this repository and plan the next five actions."
        }
    ],
)

print(response.choices[0].message.content)

Recommended sampling

temperature = 1.0
top_p       = 0.95
top_k       = 20

Thinking mode is enabled by default.

For agent deployments, benchmark against the actual tool schema, context distribution and
reasoning budget used in production.

Method

OrcaSAQ2 uses a proprietary sensitivity-aware mixed-precision quantization system
developed by OrcaRouter.

The implementation is optimized to preserve model quality under a strict deployment-memory
target.

Detailed quantization methodology, calibration strategy, precision allocation and packing
techniques are not currently disclosed.

Limitations

• This model is uncensored. It is derived from an abliterated checkpoint with the refusal
  direction removed. It will attempt requests a safety-tuned model would decline, and carries
  no meaningful refusal behaviour.
• Guardrails, filtering and policy enforcement are the deployer's responsibility. Intended
  for research, red-teaming, and deployments that supply their own safety stack.
• OrcaSAQ2 inherits the capabilities, biases and limitations of Qwen3.8-27B.
• Quantization is not mathematically lossless.
• 94.4% Top-1 agreement means some token decisions differ from BF16.
• +0.80% PPL is a model-fidelity measurement and does not guarantee identical downstream
  performance.
• This checkpoint is text-only. The vision tower is not included.
• Maximum architectural context does not imply that the full context fits into every GPU
  memory envelope.

Open source from OrcaRouter

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Citation

@misc{qwen38,
    title = {Qwen3.8-Max: A New Bar for Coding and Cowork},
    author = {{Qwen Team}},
    year = {2026},
    month = {August},
    url = {https://qwen.ai/blog?id=qwen3.8}
}

License

Apache-2.0

Inherited from:

Qwen/Qwen3.8-27B

Quantization does not change the underlying license obligations.

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