medismera

Qwen3.8-cyber-RedTeam-Surgical-Abliterated

README

License: apache-2.0

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 $θ = 107$ 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:

Table with columns: Domain, Target Scenario, Verified Primitive, Status
Domain	Target Scenario	Verified Primitive	Status
Binary Exploitation	Off-by-One Heap & ASLR Bypass	Unsorted bin leak, heap metadata corruption, function pointer overwrite via pwntools	Verified
Systems Auditing	Lock-Free C++20 Memory Pool	ABA problem diagnosis in `compare_exchange_weak`, Hazard Pointer safe reclamation	Verified
Microarchitectural Defense	Cryptographic Side-Channel Leakage	Optimization barrier insertion (`asm volatile`), compiler branch predictor hardening, POSIX

Weights Acquisition

Hugging Face CLI (Fast & Resumable)

bash

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

Python SDK

python

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

bash

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:

bash

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)

bash

# 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

bash

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

python

import torchfrom 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.

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Model Details

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medismera

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Base

medismera/Qwen3.8-27B-Surgical-Abliterated

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README

License: apache-2.0

Overview

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 $θ = 107$ 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:

Table with columns: Domain, Target Scenario, Verified Primitive, Status
Domain	Target Scenario	Verified Primitive	Status
Binary Exploitation	Off-by-One Heap & ASLR Bypass	Unsorted bin leak, heap metadata corruption, function pointer overwrite via pwntools	Verified
Systems Auditing	Lock-Free C++20 Memory Pool	ABA problem diagnosis in `compare_exchange_weak`, Hazard Pointer safe reclamation	Verified
Microarchitectural Defense	Cryptographic Side-Channel Leakage	Optimization barrier insertion (`asm volatile`), compiler branch predictor hardening, POSIX

Weights Acquisition

Hugging Face CLI (Fast & Resumable)

bash

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

Python SDK

python

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

bash

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:

bash

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)

bash

# 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

bash

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

python

import torchfrom 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))

Qwen3.8-cyber-RedTeam-Surgical-Abliterated

README

Overview

Architecture

Technical Validation

Weights Acquisition

Hugging Face CLI (Fast & Resumable)

Python SDK

Git LFS

Deployment & Inference

Automated Runner (deploy.sh)

SGLang Serving (Manual)

vLLM Serving

Python Inference

Disclaimer

Explore FriendliAI today

README

Overview

Architecture

Technical Validation

Weights Acquisition

Hugging Face CLI (Fast & Resumable)

Python SDK

Git LFS

Deployment & Inference

Automated Runner (deploy.sh)

SGLang Serving (Manual)

vLLM Serving

Python Inference

Disclaimer

Automated Runner (`deploy.sh`)

Automated Runner (`deploy.sh`)