intellix

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Model Card: Intellix

Intellix is a high-capacity, fine-tuned large language model (LLM) designed specifically for enterprise-grade applications.

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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 (intellix-base)
• 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.

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:

1. Scenario A: Draft a secure data handling policy for a fintech startup.
2. Scenario B: Summarize a 50-page internal audit report into 5 key action items.
3. 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:

Hardware Requirements

• Training: Single A100 (40GB) or H100 (80GB) recommended. Suitable for consumer GPUs like RTX 3090/4090 using Unsloth 4-bit loading.
• Inference: Minimum 8GB VRAM (Full) / 2GB VRAM (Q8_0 GGUF).

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10. How to Use

A. Local Inference via Ollama (Recommended)

Intellix is highly optimized for local execution using Ollama.

1. Prepare the Modelfile: Use the provided Modelfile in this repository which includes the correct repeat_penalty (1.5) and stop tokens to prevent loops.
2. Create the Model:

   ollama create intellix -f Modelfile
   

3. Run:

   ollama run intellix
   

Model Parameters for Stability:

• repeat_penalty: 1.5
• temperature: 0.7
• stop: ["<|im_start|>", "<|im_end|>", "User:", "Assistant:"]

B. Inference via Transformers (Python)

For research or programmatic access, use the transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mediusware-ai/intellix"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Using the ChatML Template
messages = [
    {"role": "system", "content": "You are Intellix, a professional AI assistant developed by Mediusware."},
    {"role": "user", "content": "Tell me about Mediusware's US presence."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.5)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

11. 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.

11. 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.

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Contact & Support

For custom enterprise deployments or inquiries, visit mediusware.com.

Model Architecture

• Base: intellix-base
• Parameters: 1.54B
• Hidden Size: 1536
• Context Length: 131,072 tokens