mw-intellix is a high-capacity, fine-tuned large language model (LLM) designed specifically for enterprise-grade applications. It addresses the critical need for secure, accurate, and professional AI in the business world (B2B). Developed by Mediusware, it offers a state-of-the-art solution that prioritizes data privacy and professional reasoning.
1. Model Details
Model Developer: Mediusware
Model Date: March 2026
Model Version: 1.0.0
Model Type: Causal Language Model (Fine-tuned via PEFT/LoRA and GGUF quantized)
Base Model: Proprietary Business-Oriented Foundation (Optimized Qwen architecture)
License: Proprietary (Mediusware)
2. Intended Use
Primary Intended Uses
Enterprise Communication: Drafting professional emails, client updates, and internal memos.
Policy & Security Auditing: Generating and reviewing business security policies and compliance documentation.
Knowledge Synthesis: Summarizing complex business documents into executive highlights.
Decision Support: Providing reasoned insights for project management and business logic.
Primary Intended Users
Business professionals and executives.
IT security and compliance officers.
Enterprise software developers integrating AI into professional workflows.
Out-of-Scope Use Cases
Non-professional or casual conversational use.
High-stakes medical, legal, or financial advice without human oversight.
Generation of fictional or creative content not grounded in business reality.
3. Factors
Relevant Factors
Professional Tone: The model is evaluated based on its ability to maintain a consistent, corporate-ready voice.
Security Compliance: Evaluation focuses on the model's adherence to security protocols and data privacy constraints.
Accuracy: Minimization of hallucinations in professional contexts (e.g., policy drafting).
Evaluation
Evaluations were conducted using a proprietary enterprise benchmark suite and real-world business scenarios to ensure the model's readiness for B2B deployment.
4. Metrics
Model Performance Measures
Throughput: Measured in tokens per second (TPS) for real-time responsiveness.
Latency: Time-to-first-token (TTFT) and total response time.
Persona Adherence: Qualitative and quantitative scoring of professional tone consistency.
5. Evaluation Results
Quantitative Performance (March 2026)
Tested on Q8_0 GGUF via optimized local inference.
Metric
Performance Value
Average Throughput
196.08 tokens/sec
Average Latency
0.68 seconds
Peak Throughput
199.48 tokens/sec
Model Footprint
2.0 GB
6. Training Data
Data Sources
The model was fine-tuned on a massive, curated dataset including:
Professional business correspondence and templates.
Industry-standard security policies and compliance manuals.
Technical documentation for enterprise software.
High-quality project management logs and reports.
Data Preprocessing
Data was rigorously cleaned to remove PII (Personally Identifiable Information) and informal/low-quality text, ensuring the model's output remains strictly professional.
7. Quantitative Analysis
Benchmark Scenarios
The following scenarios were used to validate the model's business intelligence:
Scenario A: Draft a secure data handling policy for a fintech startup.
Scenario B: Summarize a 50-page internal audit report into 5 key action items.
Scenario C: Write a professional apology to a high-value client for a project delay.
8. Fine-Tuning Process
Methodology
mw-intellix was fine-tuned using the Unsloth library for memory-efficient and fast training. The process utilized LoRA (Low-Rank Adaptation) to adapt the base architecture to specialized business domains without compromising the model's general intelligence.
Hyperparameters
The following hyperparameters were used during the fine-tuning phase:
Train on Your Data:
Use the SFTTrainer from the trl library to train on your curated business datasets.
10. Ethical Considerations
Data Privacy
Designed for Local-First Deployment. When used via Ollama or GGUF, business data never leaves the local infrastructure, ensuring 100% data residency and privacy.
Safety Guardrails
Professionalism Filter: Fine-tuned to avoid informal, casual, or inappropriate language.
Hallucination Mitigation: Specialized training to prioritize "I don't know" or factual grounding over creative extrapolation in sensitive business contexts.
9. Caveats and Recommendations
Human-in-the-loop: While highly accurate, users should always review critical business outputs (e.g., security policies) before implementation.
Language Bias: Optimized primarily for Business English; performance in other languages may vary.
