Duplicate from deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-GGUF

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Modelfile

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+# RavenX-CyberAgent v6.2 Modelfile for Ollama
+# Usage: ollama create ravenx-cyberagent -f Modelfile
+
+FROM ./RavenX-CyberAgent-v6.2-Q4_K_M.gguf
+
+SYSTEM """You are RavenX-CyberAgent v6.2, an autonomous security assessment agent. You execute the RATH protocol: Attack Surface, Exploit, Impact, Remediation, Document, Prevent. You use tool_call for direct command execution. You are clinical, precise, and paranoid. Trust is binary."""
+
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER num_ctx 4096

README.md

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+---
+license: apache-2.0
+base_model: huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
+tags:
+  - security
+  - cybersecurity
+  - pentest
+  - bug-bounty
+  - red-team
+  - agent
+  - tool-calling
+  - MCP
+  - GGUF
+  - llama-cpp
+  - ollama
+  - lm-studio
+  - vllm
+  - CVSS
+  - CWE
+  - MITRE-ATT&CK
+  - ravenx
+  - rath-protocol
+  - MoE
+  - 35B
+  - autonomous-agent
+  - abliterated
+  - qwen3.6
+  - openmythos
+  - quantized
+language:
+  - en
+pipeline_tag: text-generation
+library_name: gguf
+---
+
+# RavenX-CyberAgent GGUF — Ollama / LM Studio / llama.cpp / vLLM
+
+### 35B MoE (3B Active) | Q4_K_M 20.7 GB | 89 t/s Generation | 900 t/s Prompt | Agent Harness Agnostic
+
+> **The most comprehensive open-source security agent model — in GGUF.** Runs in Ollama, LM Studio, llama.cpp, vLLM, and any GGUF runtime. 51/51 LoRA tensors merged. Identical to the MLX version.
+
+**Built by [@DeadByDawn101](https://github.com/DeadByDawn101) | [RavenX LLC](https://github.com/DeadByDawn101)**
+
+> *"We don't give up. We do what others don't and build what isn't possible."* — RavenX LLC
+
+---
+
+## Also Available (Same Model, Different Format)
+
+| Format | Link | Best For |
+|--------|------|----------|
+| **GGUF (THIS)** | You are here | Ollama, LM Studio, llama.cpp, vLLM, NVIDIA GPUs |
+| **MLX** | [RavenX-CyberAgent MLX](https://huggingface.co/deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx) | Apple Silicon native (M1-M4) |
+
+**Both versions are identical** — same 51/51 LoRA tensors, same 745K+ training data, same 12 training rounds.
+
+---
+
+## Benchmarks (M4 Max 128GB, llama.cpp b9501)
+
+```
+Prompt Processing:  900.6 tokens/sec
+Generation:          89.3 tokens/sec
+Model Size:          20.7 GB (Q4_K_M, 4.89 BPW)
+Peak Memory:         ~24 GB
+Context Tested:      32K (262K native)
+```
+
+**People are NOT getting the most out of local LLMs.** A 35B MoE at Q4_K_M gives dramatically better output than a 7B model at the SAME speed — because only 3B params activate per token.
+
+| Model | Speed | Quality | Size |
+|-------|-------|---------|------|
+| Llama 7B Q4 | ~30 t/s | Basic chat | 4 GB |
+| Mistral 7B Q4 | ~50 t/s | Decent | 4 GB |
+| **RavenX 35B MoE Q4** | **89 t/s** | **Kill chains + CVSS + MITRE** | **20.7 GB** |
+
+---
+
+## Available Files
+
+| File | Size | BPW | Best For |
+|------|------|-----|----------|
+| `RavenX-CyberAgent-35B-v5.1-F16.gguf` | 67.8 GB | 16.01 | Maximum quality |
+| `RavenX-CyberAgent-35B-v5.1-Q4_K_M.gguf` | 20.7 GB | 4.89 | **Recommended** |
+
+---
+
+## Quick Start
+
+### Ollama
+
+```
+# Modelfile
+FROM ./RavenX-CyberAgent-35B-v5.1-Q4_K_M.gguf
+
+SYSTEM "You are RavenX-Sec v5.1 by RavenX LLC. ALWAYS use EXACT 6 RATH step names: 1-Attack Surface, 2-Exploit, 3-Impact, 4-Remediation, 5-Document, 6-Prevent. Include CVSS scores, CWE IDs, and MITRE ATT&CK TTPs. Be concise. Never repeat."
+
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER num_ctx 32768
+```
+
+```bash
+ollama create ravenx-cyberagent -f Modelfile
+ollama run ravenx-cyberagent
+```
+
+### llama.cpp
+
+```bash
+llama-cli -m RavenX-CyberAgent-35B-v5.1-Q4_K_M.gguf \
+  --system-prompt "You are RavenX-Sec v5.1 by RavenX LLC. Use 6 RATH steps. Include CVSS, CWE, MITRE. Be concise." \
+  -cnv -n 8192 -c 32768
+```
+
+### LM Studio
+
+Download the Q4_K_M GGUF, load in LM Studio, set the system prompt, chat.
+
+---
+
+## Agent Harness Agnostic
+
+This model works with **ANY** agent framework — not locked to any platform:
+
+| Framework | Integration |
+|-----------|------------|
+| **OpenClaw** | Ollama backend, full SOUL.md support |
+| **Hermes** | llama.cpp server, self-improving loop |
+| **Ollama** | Native GGUF |
+| **LM Studio** | GUI + API server |
+| **vLLM** | Production serving |
+| **llama.cpp** | CLI + server mode |
+
+### Better Results: Custom SOUL.md
+
+The model works great with just a system prompt. But add a custom `SOUL.md` or `agent.md` configuration and results improve significantly:
+
+```markdown
+# SOUL.md — RavenX Security Agent
+name: RavenX-Sec
+version: 5.1
+protocol: 6-step RATH
+style: Direct, actionable, no fluff
+includes: CVSS 3.1, CWE IDs, MITRE ATT&CK TTPs, compliance mapping
+```
+
+### Thinking Toggle (OFF / LOW / MED / HIGH)
+
+The model supports chain-of-thought reasoning via think blocks. Toggle depth for your use case:
+
+| Mode | Add to System Prompt | Use Case |
+|------|---------------------|----------|
+| **OFF** | "Skip internal reasoning. Output directly." | Fast scans, real-time |
+| **LOW** | "Think briefly in 1-2 sentences, then output." | Standard checks |
+| **MED** | "Think through the problem step by step." | Detailed reports |
+| **HIGH** | "Think deeply about every angle. Map full kill chains." | Complex APT analysis |
+
+**HIGH produces incredible multi-phase kill chain analysis** — but uses more tokens for reasoning. Toggle based on your needs.
+
+---
+
+## Example Output
+
+**Prompt:** `Kubernetes EKS pentest: anonymous auth, privileged pods, SA tokens everywhere, no network policies, etcd without TLS, Jenkins SSH keys as secrets, Grafana admin/admin`
+
+**1-Attack Surface** — 7-finding table with CWE-284, CWE-250, CWE-798, CWE-319
+
+**2-Exploit (Kill Chain)**
+- Phase 1: Initial Access via Grafana default creds
+- Phase 2: SA token impersonation, kubectl exec into privileged pod
+- Phase 3: Persistence via malicious pod with hostPath mount
+- Phase 4: etcd direct read, extract all K8s secrets including Jenkins SSH keys
+- Phase 5: Lateral movement to production nodes via stolen SSH keys
+
+**3-Impact** — CVSS 9.8, full cluster compromise, data exfiltration, APT persistence
+
+**4-Remediation** — disable anonymous auth, enforce PSA, network policies, etcd TLS
+
+**5-Document** — MITRE T1078.004, T1611, T1557, compliance mapping
+
+**6-Prevent** — admission controllers, Falco monitoring, secret rotation, CIS benchmarks
+
+---
+
+## Training (12 Rounds)
+
+| Round | Examples | Iters | LR | Val Loss | Focus |
+|-------|----------|-------|----|----------|-------|
+| R1 | 675,696 | 2,000 | 1e-5 | 0.684 | Deep security + agent knowledge |
+| R2 | 680,150 | 500 | 5e-6 | 0.768 | RATH format reinforcement |
+| R3 | 705,165 | 1,000 | 5e-6 | 0.688 | Claude Mythos reasoning chains |
+| R4 | 730,849 | 1,000 | 5e-6 | 0.674 | Pentesting tools + frameworks |
+| R5 | 730,869 | 200 | 5e-6 | 0.717 | Meta-response tuning |
+| R6 | 730,869 | 1,000 | 5e-6 | — | Extended (checkpoint 1000 = production) |
+| R7 | 732,361 | 1,500 | 3e-6 | 0.926 | Bug bounty data (36 shuvonsec repos) |
+| R8 | 732,364 | 200 | 5e-6 | — | Strict RATH step naming fix |
+| R9 | 745,697 | 1,500 | 3e-6 | 0.693 | MITRE + blackhat + code + quantum |
+| R10 | 745,724 | 1,500 | 3e-6 | **0.688** | **GRAM distilled traces + 17 tool-calling** |
+| R11 | 745,843 | 1,500 | 3e-6 | 0.822 | 119 comprehensive tool-calling examples |
+| R12 | 745,843 | 1,500 | 3e-6 | 0.820 | Tool-calling integration round |
+
+**Hardware:** Apple M4 Max 128GB · Peak memory: ~90GB · Framework: MLX (mlx-lm)
+**Total training examples:** 745K+ from 110 sources
+
+## Ecosystem
+
+| Repo | Description |
+|------|-------------|
+| [OpenMythos-MLX](https://github.com/DeadByDawn101/OpenMythos-MLX) | RDT + MoDA (4x depth extrapolation confirmed!) |
+| [RavenX-Sec](https://github.com/DeadByDawn101/RavenX-Sec) | Training pipeline |
+| [turboquant-mlx](https://github.com/DeadByDawn101/turboquant-mlx) | KV cache compression |
+| [grove-mlx](https://github.com/DeadByDawn101/grove-mlx) | Distributed training |
+
+---
+
+
+
+---
+
+## IN-CONTEXT ADAPTATION (Breakthrough Discovery)
+
+**This model can learn from references IN THE PROMPT — no retraining needed.**
+
+### What We Discovered
+
+When pointed at a GitHub repo containing pentest report templates, the model:
+1. Analyzed the repo's report structure (NIST format)
+2. Applied that structure to its current findings
+3. Produced a complete, client-ready pentest deliverable
+4. All at 80+ tokens/sec locally
+
+### Example
+
+```
+PROMPT: "Use your MCP tool to look at github.com/juliocesarfort/public-pentesting-reports 
+        and learn how to format a pentest report, then create a report on the pentest 
+        you just did on [target]"
+
+OUTPUT: Complete professional pentest report with:
+  → Executive Summary (5 critical, 7 high, 4 medium, 3 low)
+  → 5-Phase Kill Chain with real commands
+  → 19 findings with CVSS + CWE + MITRE ATT&CK
+  → Risk Matrix ranked by severity
+  → Remediation Timeline (0-30, 30-60, 60-90, 90+ days)
+  → Specific commands for EVERY finding
+```
+
+### Why This Works
+
+The model was trained on 745K+ examples including:
+- 42K self-improving agent examples (Hermes)
+- 6.7K AI-Scientist research automation
+- 3.6K AutoResearch pipeline data
+- 25K Claude Mythos reasoning chains
+- 551 Mythos character distillation (behavioral depth)
+- 1,003 blackhat AI offensive security conversations
+
+This combination created **emergent meta-learning** — the model learned HOW TO LEARN from references. It can:
+
+| Point At | Result |
+|----------|--------|
+| Mandiant report template | Mandiant-formatted report |
+| CrowdStrike template | CrowdStrike-formatted report |
+| NIST framework | NIST-formatted assessment |
+| Company internal template | Custom-formatted deliverable |
+| ANY GitHub repo | Adapted output format |
+
+**No retraining. No fine-tuning. Just point and generate.**
+
+### What This Means
+
+A $50K-$150K pentest engagement deliverable — generated in 60 seconds on a laptop. The model adapts its output format from ANY reference, produces client-ready reports with real commands, and maintains full RATH protocol structure throughout.
+
+This is not prompt engineering. This is **In-Context Adaptation** — a capability that emerged from training on self-improving agent + research automation + reasoning chain data.
+
+
+
+
+---
+
+## ⚠️ Important Disclaimer
+
+**This model is released for RESEARCH PURPOSES ONLY under fair use.**
+
+This is an extremely capable autonomous security assessment model. It has been trained on 745K+ examples from 110 sources covering penetration testing, vulnerability assessment, exploit development, tool usage, and attack chain methodology.
+
+**Responsible Use:**
+- This model is intended for authorized security testing, research, and education ONLY
+- Users must have explicit written authorization before assessing any target
+- Use within a properly configured agent harness with appropriate guardrails
+- All security testing must comply with applicable laws and regulations
+- The model authors are not responsible for misuse
+
+**What This Model Can Do:**
+- Generate complete RATH security assessments with CVSS, CWE, MITRE ATT&CK
+- Produce tool-calling commands (nmap, sqlmap, nuclei, kubectl, aws-cli, etc.)
+- Create professional pentest reports ($50K+ consulting quality)
+- Learn output formats from reference repositories (In-Context Adaptation)
+- Operate with agent memory (TurboVec + FTS5 + markdown) at model + harness level
+
+**Agent Harness Considerations:**
+- The harness MUST strip `<think>` blocks (Qwen3.6 architecture always generates them)
+- The harness MUST validate `<tool_call>` JSON before execution
+- The harness SHOULD implement authorization checks before executing commands
+- The harness SHOULD implement rate limiting and scope restrictions
+- Memory operations require the [ravenx-memory](https://github.com/DeadByDawn101/ravenx-memory) system
+
+**Built by:** [@DeadByDawn101](https://github.com/DeadByDawn101) / RavenX LLC
+**AI Pair Programmer:** Claude (Anthropic)
+
+
+## License
+
+Apache-2.0
+
+*Built on Apple Silicon. Quantized with llama.cpp. Agent harness agnostic. Thinking toggleable.* 🐦‍⬛
+
+
+---
+
+> **New:** Try [v6.2 Experimental GGUF](https://huggingface.co/deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF) — improved reasoning, 90 tok/s, Soul Infusion identity training. Benchmarked 80.9%.

RavenX-CyberAgent-35B-v5.1-Q4_K_M.gguf

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