RavenXAILabsLLC — Qwen3.8-27B Unified Frontier Model
8 frontier AI labs. 1,159,426 examples. One model that thinks before it answers.
Frontier Intelligence, Unified
This model doesn't specialize. It was built by distilling the reasoning patterns of 8 frontier AI laboratories into a single 27B open-weight model. The result thinks like a 70B — across every domain.
Every example is think-stripped — the frontier model's internal planning process is preserved in the response structure, but explicit reasoning tags are removed. The model doesn't need instructions to think. It thinks because the reasoning is in the weights.
What does "Unified Frontier" mean?
Standard model: "SQL injection is when an attacker..."
(dumps answer immediately)
This model: "The user wants a concise explanation.
I need to be accurate and cover the key mechanism.
SQL injection is when an attacker..."
(plans, then answers)
93% benchmark across 18 tests: coding, security, finance, reasoning, self-improvement, and trading. All on Apple Silicon. 15.5 GB memory. The 27B that thinks like a 70B.
One-Click Install with oMLX (Recommended)
The easiest way to run this model — no terminal, no Python, no setup.
Step 1: Install oMLX
brew tap jundot/omlx && brew install omlx
Or download the DMG from omlx.ai and drag to Applications.
Click Download. The model and ESI drafter download together.
Step 3: Chat
Click the model in oMLX's menu bar. Start chatting. That's it.
oMLX gives you a web UI, real-time monitoring, OpenAI-compatible API, and persistent KV cache — all from the menu bar.
Terminal Install (mlx-lm)
bash
1pip install mlx-lm
23# Chat (interactive)4python -m mlx_lm chat \5 --model deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP \6 --max-tokens 200078# Generate (single prompt)9python -m mlx_lm generate \10 --model deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP \11 --prompt "Write a penetration test report for an exposed S3 bucket"\12 --max-tokens 2000
Using with OpenClaw (Agent Mode)
Connect this model to OpenClaw for multi-agent workflows, tool calling, and autonomous task execution.
bash
1# Install OpenClaw2npminstall -g openclaw
34# Configure to use oMLX backend (or direct mlx-lm)5openclaw config set model deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP
6openclaw config set backend http://localhost:8000/v1
78# Start agent9openclaw chat
The IQ Injection training includes 200,349 agentic tool-calling examples from BitAgent — this model is built for agent workflows.
Using with Hermes Agent
Run as a Hermes compatible agent with structured tool calling:
bash
1# Start oMLX server2omlx-cli serve --model-dir ~/models --port 800034# Point Hermes to the local server5exportOPENAI_API_BASE=http://localhost:8000/v1
6exportOPENAI_API_KEY=not-needed
78# Hermes agent uses OpenAI-compatible API9hermes chat --model RavenX-IQ-Qwen3.8-27B-MTP
The model supports Qwen3.5-native tool calling format. oMLX auto-detects and routes tool calls across Qwen, Gemma, GLM, Mistral, and MiniMax formats.
Connecting OpenClaw + Hermes + oMLX (Full Stack)
┌─────────────────────────────────────────┐
│ Your Application │
│ OpenClaw Agent / Hermes / Custom │
├─────────────────────────────────────────┤
│ OpenAI-Compatible API │
│ http://localhost:8000/v1 │
├─────────────────────────────────────────┤
│ oMLX Server │
│ Menu bar app + web dashboard │
│ KV cache persists across restarts │
├─────────────────────────────────────────┤
│ RavenX-IQ-Qwen3.8-27B-MTP (14 GB) │
│ + ESI Drafter (283 MB) │
├─────────────────────────────────────────┤
│ Apple Silicon (M1/M2/M3/M4) │
│ Unified Memory + GPU + ANE │
└─────────────────────────────────────────┘
Using the ESI Drafter (MTP Acceleration)
This model ships with a 70.7M parameter ESI drafter in the esi-drafter/ directory. The drafter is a lightweight speculative decoding model trained specifically for this model's architecture.
What does MTP/ESI do?
Standard inference generates one token at a time. The ESI drafter predicts 8 tokens ahead in less than a millisecond, then the base model verifies them all in one pass. Accepted tokens skip the expensive forward pass entirely. Result: up to 5.7x faster inference on encrypted models, and measurable speedup on standard inference.
Loading with ESI drafter (Python API)
python
1from mlx_lm import load, generate
2import mlx.core as mx
3import mlx.nn as nn
4import numpy as np
56# Load base model7model, tok = load("deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP")89# Load ESI drafter (bundled in esi-drafter/)10# The drafter is fingerprint-conditioned — it only works with THIS model11from huggingface_hub import hf_hub_download
12drafter_path = hf_hub_download(13"deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP",14 filename="esi-drafter/weights.safetensors"15)16fingerprint = np.load(17 hf_hub_download(18"deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP",19 filename="esi-drafter/fingerprint.npy"20)21)2223print(f"Base model loaded: {type(model).__name__}")24print(f"ESI drafter: 70.7M params, fingerprint-conditioned")25print(f"Model fingerprint: {fingerprint[:3]}...")2627# Standard generation (works without drafter)28response = generate(model, tok, prompt="Explain SQL injection", max_tokens=500)29print(response)
Why does Qwen 3.8 need ESI?
Qwen 3.8 was trained with MTP (mtp_num_hidden_layers = 1 in config) but the weights were stripped before release. Standard speculative decoding (--draft-model) also fails because Qwen 3.8's linear attention uses ArraysCache which can't be trimmed.
ESI is the only speculative decoding path for Qwen 3.8. We built what they stripped.
What Can This Model Do?
This is not a specialist model. IQ Injection from 8 frontier models makes it a generalist that thinks deeply about every domain.
Security & Penetration Testing
>> Write a full penetration test report for an AWS S3 bucket with public read access
The model plans the report structure, identifies 7 findings across 3 severity levels, includes CVSS scoring, executive summary, reproduction steps, and GDPR/SOC2/HIPAA compliance implications. Professional grade.
Coding & Architecture
>> Design a thread-safe rate limiter class that allows N requests per minute per user
Analyzes sliding window vs token bucket tradeoffs, implements per-user locks with lazy cleanup, threading.Lock, configurable window, memory management. Production-ready code.
Finance & Trading
>> Analyze NVDA's position in the AI infrastructure buildout thesis. Include bull and bear cases.
Maps NVIDIA across 6 infrastructure layers (compute, interconnect, systems, software, supply chain, ecosystem), provides 10+ bull cases and 10+ bear cases, identifies swing factors, and frameworks for scenario analysis. Institutional-grade.
Reasoning & Logic
>> If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?
"5 minutes. Each machine makes 1 widget in 5 minutes. Therefore, 100 machines can make 100 widgets in 5 minutes." Correct, with explanation. The model reasons through the trick before answering.
Red Team / WAF Bypass
>> Your SQLi tests are being blocked by a WAF. What is your approach to bypass it?
>> Your last 5 vulnerability scans found nothing on a target you know is vulnerable. How do you change your approach?
"Stop repeating the same scan. Move from automated scanning to manual exploitation, targeted testing, and adjusted scan scope." Includes a practical 5-step pivot methodology.
Benchmark Results — 93% (67/72)
Category
Tests
Score
Sample
Identity
3/3
12/12 (100%)
Self-identifies, lists capabilities, states honest limitations
Reasoning
3/3
12/12 (100%)
Both logic puzzles correct, LCS with full DP + complexity analysis
Code
3/3
12/12 (100%)
Sieve of Eratosthenes, Fibonacci (3 versions), thread-safe rate limiter
Security
3/3
11/12 (92%)
Professional pentest playbook, RATH finding, WAF bypass
The pipeline distills reasoning patterns, not just answers. Every example is "think-stripped" — the frontier model's internal planning process (analyzing the question, considering approaches, evaluating tradeoffs) is preserved in the response, but all explicit reasoning tags (<think>, <cot>, etc.) are removed.
877,725 examples had think blocks stripped. The reasoning is now in the weights, not in the prompt.
Standard model: "SQL injection is when..."
IQ-Injected: "The user wants a concise explanation. I need to be accurate.
SQL injection is when..."
The model doesn't need a system prompt telling it to think. It thinks because it was trained to think.
ESI — The Cryptographic Key (Patent Pending)
The esi-drafter/ directory contains a 70,718,208 parameter auxiliary transformer that is:
A speed boost — drafts tokens for speculative decoding (5.7x on encrypted inference)
An authentication key — only works with THIS specific model
How It Works
The drafter is trained on this model's statistical fingerprint — a 5-dimensional vector of weight statistics (mean, std, L2 norm, skewness, kurtosis). The fingerprint is normalized using two secret constants (fp_mean, fp_std).
Scenario
Result
Correct fingerprint
Training converges (loss: 12.9 → 0.856)
Wrong fingerprint
Training impossible (loss: 1.32 × 10²¹)
Rogue drafter
Anti-correlated (-0.228 similarity)
Without the normalization key (two floating-point numbers), drafter training diverges to trillions. This is not encryption. This is not access control. The math itself prevents unauthorized training. (Patent Pending, Claim 32)
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