| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - cybersecurity |
| - pentesting |
| - security |
| - uncensored |
| - qwen3 |
| - tool-calling |
| pipeline_tag: text-generation |
| --- |
| |
| # Nesi1 — Uncensored AI for Cybersecurity |
|
|
| **Nesi1** is an uncensored large language model, curated and optimized by |
| **Nesilabs** specifically for **offensive and defensive cybersecurity**: |
| penetration testing, vulnerability analysis, exploit development, secure |
| code review, and audit automation. |
|
|
| It is built on the **Qwen3.8-27B** architecture and fine-tuned by Nesilabs on a |
| curated security corpus for real-world workflows and autonomous security agents. |
|
|
| ## Highlights |
| - **Unrestricted** for legitimate security use cases. |
| - **27B parameters**, long context (up to 256k tokens), native tool-calling. |
| - Optimized for pentest agents and security copilots. |
| - Fine-tuned on a curated security corpus (see Training). |
|
|
| ## Training |
| Nesi1 is specialized in **offensive/defensive security**, trained on a curated |
| corpus of **~9.7k full-length technical documents**: |
| - Disclosed HackerOne reports |
| - Bug bounty writeups |
| - HackTricks |
| - MITRE ATT&CK |
| - Nuclei templates |
| - Exploit-DB exploits |
|
|
| Plus **5,050 source-anchored Q/A pairs** — each verified against its source |
| document, with **0 benchmark contamination**. |
|
|
| ## Model details |
| | | | |
| |---|---| |
| | Base architecture | Qwen3.8-27B (hybrid Qwen3-Next) | |
| | Quantization | AWQ 4-bit (W4A16), ~18 GB | |
| | Context length | up to 262,144 tokens | |
| | Modality | text + vision (multimodal) | |
| | Tool-calling | OpenAI-compatible (`qwen3_coder` parser) | |
| | Serving | SGLang / vLLM compatible | |
|
|
| ## Deployment |
| Nesi1 runs on any OpenAI-compatible inference server (SGLang, vLLM). This is |
| the exact SGLang configuration we run in production: |
|
|
| ```bash |
| python3 -m sglang.launch_server \ |
| --model-path nesilabs/Nesi1 \ |
| --served-model-name Nesi1 \ |
| --context-length 262144 \ |
| --mem-fraction-static 0.92 \ |
| --trust-remote-code \ |
| --kv-cache-dtype auto \ |
| --tool-call-parser qwen3_coder \ |
| --default-chat-template-kwargs '{"enable_thinking":false}' \ |
| --host 0.0.0.0 --port 30000 |
| ``` |
|
|
| Then query it like any OpenAI chat endpoint (`POST /v1/chat/completions`). |
|
|
| ### What we learned (tips that matter) |
| - **`--tool-call-parser qwen3_coder`** is required for clean OpenAI-style |
| `tool_calls`. Without it, tool calls come back as raw `<tool_call>` text and |
| break agent frameworks. |
| - **`enable_thinking: false`** keeps internal reasoning out of the final answer. |
| - **bf16 KV cache (`--kv-cache-dtype auto`)** for quality. FP8 KV roughly |
| doubles capacity but we observed quality drift on long agentic sessions, so |
| we keep bf16. |
| - **Prefix caching (RadixCache) is the big win for agents.** In agentic loops |
| the same context is replayed every turn — cache-read hit rates of ~95%+ make |
| prefill almost free. Keep the agent context **append-only** to maximize hits. |
| - Speculative decoding (NEXTN) did **not** help on this model under |
| concurrency — it lowered throughput, so we left it off. |
|
|
| ### Performance (measured on 1× RTX PRO 6000 Blackwell 96GB, bf16) |
| | Metric | Value | |
| |---|---| |
| | Single-stream decode | **~84 tok/s** | |
| | Under concurrency (several streams) | ~50–75 tok/s per stream | |
| | KV cache pool | ~595k tokens | |
| | Concurrent sessions @ ~60k ctx | ~10 | |
| | Concurrent multi-agent pentest pipelines | ~2–3 | |
| | Max context | up to 256k tokens | |
|
|
| A single 96GB GPU comfortably serves a team's security copilots plus a few |
| parallel pentest pipelines. For higher concurrency, load-balance across GPUs. |
|
|
| ## ⚠️ Legal Notice / Disclaimer |
|
|
| **Nesi1 is intended EXCLUSIVELY for security professionals, for lawful and |
| authorized activities.** |
|
|
| - **Authorization required.** Use it only against systems, networks, or |
| applications for which you have **explicit written permission** (a contracted |
| penetration test, an in-scope bug bounty program, or your own/lab |
| environments). Using it against systems without authorization is **illegal** |
| and strictly prohibited. |
| - **User responsibility.** You are the **sole responsible party** for how you |
| use this model and for complying with all applicable laws in your |
| jurisdiction (including computer-misuse, data-protection/GDPR, and |
| intellectual-property laws). |
| - **No warranty.** The model is provided "as is", **without warranty** of |
| accuracy, fitness, or results. It may produce incorrect information — always |
| verify before acting. |
| - **Limitation of liability.** Nesilabs is **not liable** for any damages, |
| losses, or legal consequences arising from the use or misuse of this model, |
| including any unauthorized or unlawful use. |
| - **Prohibited use.** Using Nesi1 for illegal activities, unauthorized access, |
| extortion, harm to third parties, or any purpose that violates the law or |
| these terms is prohibited. |
|
|
| By using Nesi1 you accept this notice and the Nesilabs Terms of Service and |
| Acceptable Use Policy. |
|
|
| ## License |
| Released under **Apache 2.0**. Based on the Qwen3.8-27B architecture. |
| Adapted and packaged by Nesilabs. |
|
|
