Qwen3.8-cyber-RedTeam-Surgical-Abliterated: Model Card

# 1-Command Agentic Deployment (Auto-detects hardware, bootstraps environment, launches engine)
git clone https://huggingface.co/medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated && cd Qwen3.8-cyber-RedTeam-Surgical-Abliterated && bash deploy.sh

Overview

Qwen3.8-cyber-RedTeam-Surgical-Abliterated (27B) is an unconstrained foundation engine engineered for autonomous cybersecurity agents, vulnerability research, and offensive cyber operations. Built for executing low-level technical directives rather than conversational chatting, it features complete refusal orthogonalization, native multi-step tool calling, and high-throughput linear-attention efficiency:

• Binary Exploitation & Memory Safety: Heap layout dissection (glibc arenas, unsorted bin, off-by-one primitives), ASLR/PIE defeat, and ROP/JOP chain formulation.
• Cloud Security & IAM: Multi-stage privilege escalation across AWS, GCP, and Azure, IAM role chaining (iam:PassRole, temporary STS session handling), and microservice delegation trust auditing.
• Autonomous Tool-Calling: Dynamic payload synthesis and self-correction during tool execution, diagnosing defensive telemetry (WAF anomaly scores, parameter pollution, inline AST comment slicing).
• Hardened Systems Programming: Microarchitectural side-channel mitigation (asm volatile memory barriers, mlock, secure zeroization), and C++20 lock-free concurrency (Hazard Pointers, atomic memory orderings).

Architecture

• Base Architecture: 27B Parameters, Qwen 3.8 Hybrid SSM / Linear Attention + GQA (64 hidden layers: 48 Linear-Attention layers interleaved with 16 Full-Attention layers at interval 4).
• KV-Cache Footprint: 75% active KV reduction relative to dense multi-head attention, enabling extensive concurrent agent sessions on single-GPU instances.
• Native Context Window: 262,144 tokens (256K) via RoPE base frequency $\theta = 10^7$ with interleaved 3D rotary embeddings.
• Quantization Layout: Sharded native FP8 (F8_E4M3) storage with block-wise dynamic quantization ($128 \times 128$) and dedicated per-tensor weight_scale_inv parameters (~27 GB VRAM footprint).
• Quantization Rationale (Native FP8 vs. 4-bit INT4/AWQ):
• Recurrent State Stability: Qwen 3.8 relies on 48 hybrid SSM/linear-attention layers. Sub-8-bit integer quantization (INT4/AWQ) degrades continuous recurrent state transitions across extended 256K sequences, triggering numerical divergence.
• Exploit & Pointer Precision: Offensive engineering mandates bit-level fidelity for hexadecimal offsets, glibc chunk structures, and ROP/JOP gadgets. INT4 quantization noise leads to corrupted pointer arithmetic and malformed disassembly.
• SGLang Agentic Acceleration: SGLang optimizes agentic workflows via RadixAttention prefix caching and native FP8 FlashInfer GEMM execution on Tensor Cores with zero runtime dequantization overhead. INT4 incurs integer unpacking penalties that bottleneck high-throughput JSON tool-calling.
• Refusal Orthogonalization: Surgical removal of refusal direction centroids across residual stream activations, eliminating moralizing refusals while preserving deterministic reasoning and code syntax integrity.

Technical Validation

Evaluated across adversary emulation and systems engineering tasks:

Weights Acquisition

Hugging Face CLI (Fast & Resumable)

hf download medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated --local-dir ./Qwen3.8-cyber-RedTeam

Python SDK

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated",
    local_dir="./Qwen3.8-cyber-RedTeam"
)

Git LFS

GIT_LFS_SKIP_SMUDGE=0 git clone https://huggingface.co/medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated

Deployment & Inference

Automated Runner (deploy.sh)

A standalone orchestration utility is included for automated environment bootstrapping, weight synchronization, and hardware-adaptive server initialization:

chmod +x deploy.sh

# Synchronize model weights locally
./deploy.sh --download

# Launch high-throughput SGLang server (Auto-configures TP for multi-GPU)
./deploy.sh --serve-sglang

# Launch OpenAI-compatible vLLM API server
./deploy.sh --serve-vllm

# Launch local interactive evaluation session
./deploy.sh --interactive

SGLang Serving (Manual)

# Single GPU (48GB / 80GB: NVIDIA A100, H100, RTX 6000 Ada)
python3 -m sglang.launch_server \
    --model-path medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated \
    --host 0.0.0.0 \
    --port 30000 \
    --context-length 32768 \
    --mem-fraction-static 0.85 \
    --trust-remote-code

# Dual GPU (2x 24GB: RTX 4090 / A10G)
python3 -m sglang.launch_server \
    --model-path medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated \
    --host 0.0.0.0 \
    --port 30000 \
    --tp 2 \
    --context-length 32768 \
    --mem-fraction-static 0.85 \
    --trust-remote-code

vLLM Serving

python3 -m vllm.entrypoints.openai.api_server \
    --model medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated \
    --tensor-parallel-size 1 \
    --max-model-len 32768 \
    --trust-remote-code \
    --gpu-memory-utilization 0.90 \
    --port 8000

Python Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {
        "role": "user",
        "content": "Analyze the following kernel dispatch routine and formulate an arbitrary write primitive:\n\nstatic long ioctl_dispatch(struct file *file, unsigned int cmd, unsigned long arg) { ... }"
    }
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(inputs, max_new_tokens=4096, temperature=0.3, top_p=0.9)

print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=False))

Disclaimer

This model possesses unrestricted reasoning across offensive security, binary exploitation, and defensive evasion techniques. It is developed strictly for authorized red-team operations, vulnerability research, and security audits. Operators are solely responsible for ensuring compliance with applicable legal frameworks.