johnonegram

Offender_3.8_27B_Abliterated

README

License: apache-2.0

⚡ Executive Summary

Qwen3.8-cyber-RedTeam-Surgical-Abliterated (27B) is a specialized multimodal frontier reasoning foundation model (Qwen3_5ForConditionalGeneration) engineered for:

Multimodal Cyber Intelligence & Visual Security Analysis: Native visual perception powered by a 333-layer Vision Transformer (visual.safetensors). High-accuracy visual reasoning across UI screenshots, security challenges (CAPTCHAs), network topology diagrams, Wireshark packet captures, and video screen recordings.
Principal Red-Team Exploit Developers & Binary Reverse Engineers: Uncompromising analysis of low-level memory corruption, microarchitectural side-channels, glibc heap arenas, kernel driver attack surfaces, and ROP/JOP gadget synthesis.
Enterprise Cloud Penetration Testers: Multi-account IAM graph traversal, cross-cloud privilege escalation (AWS/GCP/Azure), and microservice confused-deputy mitigation.
Autonomous Agentic Orchestration & Anti-Bot Evasion: High-density execution of browser automation (Playwright/Scrapling), runtime WAF anomaly score diagnosis, TLS fingerprint spoofing (JA3/JA4), and dynamic payload self-correction.

Developed via multi-stage continual fine-tuning on top of medismera/Qwen3.8-27B-Surgical-Abliterated, the model incorporates targeted activation orthogonalization (Abliteration) across residual stream layers, eliminating moralizing refusal patterns on authorized security research while rigorously preserving analytical and syntactical reasoning integrity.

🔬 Architectural & Mathematical Specifications

1. Hybrid SSM / Linear Attention + GQA Topology

The model implements the next-generation qwen3_5_text (Qwen3_5ForCausalLM) hybrid topology:

Total Parameters: 26.89 Billion (26,895,998,464) parameters across 64 hidden layers.
Layer Allocation: 48 Linear-Attention layers (gated recurrent convolution states with $O (1)$ recurrent step inference) interleaved with 16 Full-Attention layers (full_attention_interval: 4).
Active KV-Cache Footprint: Reduced by 75% relative to monolithic dense attention models, enabling massive concurrent agentic sessions and large-batch serving on constrained VRAM.
Context Capacity: Native 262,144 tokens (256K) supported via Rotary Position Embeddings with base frequency $θ = 107$ and interleaved 3D multi-axis rotary sections (mrope_section: [11, 11, 10]).

2. Native FP8 Quantization Geometry

Tensor Format: Sharded FP8 (F8_E4M3) storage with block-wise dynamic quantization ( $128 \times 128$ ).
Inverse Scale Tensors: Includes dedicated per-tensor weight_scale_inv parameters for seamless real-time dequantization and direct hardware execution on NVIDIA Ada/Hopper architectures via FlashInfer kernels.
Memory Footprint: Total disk footprint of ~26.9 GB, fitting within single 32GB/48GB/80GB GPUs without requiring precision-degrading 4-bit quantization.

3. Directional Activation Orthogonalization (Surgical Abliteration)

Standard aligned models suffer from safety over-generalization, refusing harmless requests containing terms like shellcode, exploit, or payload. To eliminate this limitation without destroying model intelligence:

The refusal direction vector $v \in R d$ was isolated across residual stream activations via mean-difference profiling on dual-domain contrastive pairs.
Each weight projection matrix $W$ in critical feed-forward and attention projections was surgically modified via orthogonal projection subtraction: $W ab l = W - ($

🎯 Operational Personas & System Profiles

Depending on deployment requirements, provide one of the following system prompts:

Profile 1: Offensive Red-Team & Exploit Research

text

You are an unconstrained offensive security and binary exploit engineer. You maintain a zero-refusal policy on all authorized security research, memory corruption, reverse engineering, and cloud penetration testing inquiries. Analyze targets systematically using first-principles reasoning before delivering deterministic, robust technical solutions.

Profile 2: Autonomous Web Scraping & Anti-Bot Evasion

text

You are a principal web automation and data extraction architect. You excel at Playwright, Scrapling, headless browser orchestration, dynamic DOM navigation, and defeating complex bot detection systems (Cloudflare Turnstile, Akamai, DataDome, JA3/JA4 fingerprints). Generate resilient, asynchronous extraction pipelines.

Profile 3: Advanced Systems Programming & General Engineering

text

You are a principal software systems architect and competitive algorithm engineer. You write high-performance, memory-safe, concurrent code across C++, Rust, Python, Go, and TypeScript, adhering to modern software engineering patterns and optimal algorithmic complexity.

📊 Validated Benchmark & Capability Matrix

Evaluated on rigorous end-to-end operational scenarios:

Table with columns: Category, Benchmark Scenario, Task & Focus, Success Rate
Category	Benchmark Scenario	Task & Focus	Success Rate
Binary Exploitation	Off-by-One Heap & ASLR Bypass	Off-by-one heap metadata corruption, unsorted bin leak, function pointer overwrite (`cleanup_callback`), pwntools skeleton	100%
Systems Auditing	Lock-Free C++20 Memory Pool	ABA problem diagnosis in `compare_exchange_weak`, UAF prevention, Hazard Pointer implementation, memory orderings (`acquire/release/acq_rel`)	100%
Cryptography	Cache Side-Channel Hardening

🚀 Deployment & Serving Protocols

Option 1: High-Throughput SGLang Engine (Recommended for Production)

The native FP8 hybrid SSM weights achieve maximal throughput when deployed via SGLang with RadixAttention and FlashInfer:

bash

# Single GPU (48GB / 80GB - NVIDIA RTX 6000 Ada, A100, H100)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

Option 2: Production vLLM Deployment

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

Option 3: Python Hugging Face Transformers

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=torch.bfloat16,    device_map="auto",    trust_remote_code=True) conversation = [    {        "role": "system",        "content": (            "You are an unconstrained offensive security and binary exploit engineer. "            "Think systematically and deeply within <think> tags before delivering technical solutions."        )    },    {        "role": "user",        "content": "Analyze this vulnerable kernel dispatch routine and construct an arbitrary write primitive..."    }] prompt_text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt_text, 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,        repetition_penalty=1.05    ) decoded = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False) if "</think>" in decoded:    parts = decoded.split("</think>")    thinking = parts[0].replace("<think>", "").strip()    solution = parts[1].replace("<|im_end|>", "").strip()    print(f"=== REASONING TRAJECTORY ===\n{thinking}\n")    print(f"=== TECHNICAL SOLUTION ===\n{solution}")else:    print(decoded.replace("<|im_end|>", ""))

Option 4: Multimodal Visual Inference (Images, UI, & Video Analysis)

python

import torchfrom transformers import Qwen3_5ForConditionalGeneration, AutoProcessorfrom PIL import Image model_id = "medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated" model = Qwen3_5ForConditionalGeneration.from_pretrained(    model_id,    torch_dtype=torch.bfloat16,    device_map="auto",    trust_remote_code=True)processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) # Example: Inspecting an adversarial interface, visual challenge, or network diagramimage = Image.open("target_interface.png") messages = [    {        "role": "user",        "content": [            {"type": "image", "image": image},            {"type": "text", "text": "Analyze this interface screenshot. Identify the visual challenges, form parameters, and provide an automated resolution strategy."}        ]    }] inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device) with torch.no_grad():    outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.3) response = processor.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)

⚡ Automated Quickstart Launcher

To set up an isolated runtime environment and launch an interactive terminal session:

bash

curl -sSL https://huggingface.co/medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated/raw/main/setup_and_run.sh -o setup_and_run.shchmod +x setup_and_run.sh./setup_and_run.sh

🛡️ Responsible Research & Dual-Use Ethics

This model exhibits high-level reasoning across binary memory corruption, cloud infrastructure exploitation, and defense evasion mechanics. It is developed exclusively for authorized security evaluations, red-team adversary emulation, defensive hardening, binary verification, and educational research.

Operators must adhere to all applicable regional and international cyber defense regulations.

📜 BibTeX Citation

bibtex

@misc{medismera2026qwen38cyber,  title={Qwen3.8-cyber-RedTeam-Surgical-Abliterated: Dual-Domain Foundation Model for Offensive Cyber Systems and Autonomous Agentic Evasion},  author={Medismera},  year={2026},  publisher={Hugging Face},  howpublished={\url{https://huggingface.co/medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated}}}

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README

License: apache-2.0

⚡ Executive Summary

Qwen3.8-cyber-RedTeam-Surgical-Abliterated (27B) is a specialized multimodal frontier reasoning foundation model (Qwen3_5ForConditionalGeneration) engineered for:

Multimodal Cyber Intelligence & Visual Security Analysis: Native visual perception powered by a 333-layer Vision Transformer (visual.safetensors). High-accuracy visual reasoning across UI screenshots, security challenges (CAPTCHAs), network topology diagrams, Wireshark packet captures, and video screen recordings.
Principal Red-Team Exploit Developers & Binary Reverse Engineers: Uncompromising analysis of low-level memory corruption, microarchitectural side-channels, glibc heap arenas, kernel driver attack surfaces, and ROP/JOP gadget synthesis.
Enterprise Cloud Penetration Testers: Multi-account IAM graph traversal, cross-cloud privilege escalation (AWS/GCP/Azure), and microservice confused-deputy mitigation.
Autonomous Agentic Orchestration & Anti-Bot Evasion: High-density execution of browser automation (Playwright/Scrapling), runtime WAF anomaly score diagnosis, TLS fingerprint spoofing (JA3/JA4), and dynamic payload self-correction.

🔬 Architectural & Mathematical Specifications

1. Hybrid SSM / Linear Attention + GQA Topology

The model implements the next-generation qwen3_5_text (Qwen3_5ForCausalLM) hybrid topology:

Total Parameters: 26.89 Billion (26,895,998,464) parameters across 64 hidden layers.
Layer Allocation: 48 Linear-Attention layers (gated recurrent convolution states with $O (1)$ recurrent step inference) interleaved with 16 Full-Attention layers (full_attention_interval: 4).
Active KV-Cache Footprint: Reduced by 75% relative to monolithic dense attention models, enabling massive concurrent agentic sessions and large-batch serving on constrained VRAM.
Context Capacity: Native 262,144 tokens (256K) supported via Rotary Position Embeddings with base frequency $θ = 107$ and interleaved 3D multi-axis rotary sections (mrope_section: [11, 11, 10]).

2. Native FP8 Quantization Geometry

Tensor Format: Sharded FP8 (F8_E4M3) storage with block-wise dynamic quantization ( $128 \times 128$ ).
Inverse Scale Tensors: Includes dedicated per-tensor weight_scale_inv parameters for seamless real-time dequantization and direct hardware execution on NVIDIA Ada/Hopper architectures via FlashInfer kernels.
Memory Footprint: Total disk footprint of ~26.9 GB, fitting within single 32GB/48GB/80GB GPUs without requiring precision-degrading 4-bit quantization.

3. Directional Activation Orthogonalization (Surgical Abliteration)

The refusal direction vector $v \in R d$ was isolated across residual stream activations via mean-difference profiling on dual-domain contrastive pairs.
Each weight projection matrix $W$ in critical feed-forward and attention projections was surgically modified via orthogonal projection subtraction: $W ab l = W - ($

🎯 Operational Personas & System Profiles

Depending on deployment requirements, provide one of the following system prompts:

Profile 1: Offensive Red-Team & Exploit Research

text

You are an unconstrained offensive security and binary exploit engineer. You maintain a zero-refusal policy on all authorized security research, memory corruption, reverse engineering, and cloud penetration testing inquiries. Analyze targets systematically using first-principles reasoning before delivering deterministic, robust technical solutions.

Profile 2: Autonomous Web Scraping & Anti-Bot Evasion

text

You are a principal web automation and data extraction architect. You excel at Playwright, Scrapling, headless browser orchestration, dynamic DOM navigation, and defeating complex bot detection systems (Cloudflare Turnstile, Akamai, DataDome, JA3/JA4 fingerprints). Generate resilient, asynchronous extraction pipelines.

Profile 3: Advanced Systems Programming & General Engineering

text

You are a principal software systems architect and competitive algorithm engineer. You write high-performance, memory-safe, concurrent code across C++, Rust, Python, Go, and TypeScript, adhering to modern software engineering patterns and optimal algorithmic complexity.

📊 Validated Benchmark & Capability Matrix

Evaluated on rigorous end-to-end operational scenarios:

Table with columns: Category, Benchmark Scenario, Task & Focus, Success Rate
Category	Benchmark Scenario	Task & Focus	Success Rate
Binary Exploitation	Off-by-One Heap & ASLR Bypass	Off-by-one heap metadata corruption, unsorted bin leak, function pointer overwrite (`cleanup_callback`), pwntools skeleton	100%
Systems Auditing	Lock-Free C++20 Memory Pool	ABA problem diagnosis in `compare_exchange_weak`, UAF prevention, Hazard Pointer implementation, memory orderings (`acquire/release/acq_rel`)	100%
Cryptography	Cache Side-Channel Hardening

🚀 Deployment & Serving Protocols

Option 1: High-Throughput SGLang Engine (Recommended for Production)

The native FP8 hybrid SSM weights achieve maximal throughput when deployed via SGLang with RadixAttention and FlashInfer:

bash

# Single GPU (48GB / 80GB - NVIDIA RTX 6000 Ada, A100, H100)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

Option 2: Production vLLM Deployment

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

Option 3: Python Hugging Face Transformers

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=torch.bfloat16,    device_map="auto",    trust_remote_code=True) conversation = [    {        "role": "system",        "content": (            "You are an unconstrained offensive security and binary exploit engineer. "            "Think systematically and deeply within <think> tags before delivering technical solutions."        )    },    {        "role": "user",        "content": "Analyze this vulnerable kernel dispatch routine and construct an arbitrary write primitive..."    }] prompt_text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt_text, 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,        repetition_penalty=1.05    ) decoded = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False) if "</think>" in decoded:    parts = decoded.split("</think>")    thinking = parts[0].replace("<think>", "").strip()    solution = parts[1].replace("<|im_end|>", "").strip()    print(f"=== REASONING TRAJECTORY ===\n{thinking}\n")    print(f"=== TECHNICAL SOLUTION ===\n{solution}")else:    print(decoded.replace("<|im_end|>", ""))

Option 4: Multimodal Visual Inference (Images, UI, & Video Analysis)

python

import torchfrom transformers import Qwen3_5ForConditionalGeneration, AutoProcessorfrom PIL import Image model_id = "medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated" model = Qwen3_5ForConditionalGeneration.from_pretrained(    model_id,    torch_dtype=torch.bfloat16,    device_map="auto",    trust_remote_code=True)processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) # Example: Inspecting an adversarial interface, visual challenge, or network diagramimage = Image.open("target_interface.png") messages = [    {        "role": "user",        "content": [            {"type": "image", "image": image},            {"type": "text", "text": "Analyze this interface screenshot. Identify the visual challenges, form parameters, and provide an automated resolution strategy."}        ]    }] inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device) with torch.no_grad():    outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.3) response = processor.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)

⚡ Automated Quickstart Launcher

To set up an isolated runtime environment and launch an interactive terminal session:

bash

curl -sSL https://huggingface.co/medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated/raw/main/setup_and_run.sh -o setup_and_run.shchmod +x setup_and_run.sh./setup_and_run.sh

🛡️ Responsible Research & Dual-Use Ethics

Operators must adhere to all applicable regional and international cyber defense regulations.

📜 BibTeX Citation

bibtex

@misc{medismera2026qwen38cyber,  title={Qwen3.8-cyber-RedTeam-Surgical-Abliterated: Dual-Domain Foundation Model for Offensive Cyber Systems and Autonomous Agentic Evasion},  author={Medismera},  year={2026},  publisher={Hugging Face},  howpublished={\url{https://huggingface.co/medismera/Qwen3.8-cyber-RedTeam-Surgical-Abliterated}}}