MC-AI

MC-AI is an experimental prompt-enhancement adapter built on Qwen/Qwen3.5-0.8B-Base.

Its purpose is to transform rough, incomplete, or simple user ideas into clearer, richer, more useful prompts while preserving the user's original intent.

Model Details

• Developed by: Mohit Chilakala
• Model type: LoRA / PEFT adapter
• Base model: Qwen/Qwen3.5-0.8B-Base
• License: Apache-2.0
• Primary capability: Prompt enhancement / text generation
• Status: Experimental proof of concept

What MC-AI Is Designed To Do

MC-AI is designed to:

• understand the user's intended goal
• improve unclear or incomplete prompts
• add useful contextual and creative detail
• preserve the original intent
• avoid unnecessary technical choices
• reduce unsupported factual assumptions
• create prompts that are directly usable by another AI system

The model is intended to act as a prompt-enhancement layer rather than as the final task executor.

Intended Uses

MC-AI can be used for improving prompts related to:

• websites
• applications
• image generation
• video generation
• writing
• resumes
• business
• research
• education
• marketing
• brainstorming
• general AI prompting

Limitations

This is an early experimental fine-tuned adapter and should not be considered a production-ready general-purpose AI system.

Known limitations include:

• it may still make unnecessary assumptions
• it may over-expand some short prompts
• it may occasionally preserve or introduce details that the user did not explicitly request
• it does not independently verify real-time information
• current or time-sensitive information should be verified by a downstream system with access to reliable live sources

Training

MC-AI was fine-tuned from:

Qwen/Qwen3.5-0.8B-Base

The project uses parameter-efficient fine-tuning with LoRA/PEFT.

Training approach

The training examples teach the pattern:

rough user idea → understand intent → intelligently enhance → improved prompt

The v0.2 experiment used:

• Training examples: 200
• Evaluation examples: 50
• Epochs: 3
• Best checkpoint: Epoch 1
• Hardware: NVIDIA Tesla T4
• LoRA: attention and MLP projection modules
• LoRA rank: 16
• LoRA alpha: 32
• LoRA dropout: 0.05
• Learning rate: 1e-4

The best v0.2 checkpoint was selected based on validation loss rather than training loss alone.

Evaluation

The model was evaluated on prompt-enhancement tasks including:

• website requests
• writing requests
• image prompts
• application ideas
• business planning
• current-information requests
• educational prompts

The current release is an experimental baseline and has not been evaluated at production scale.

Safety and Accuracy

MC-AI should not be treated as a source of verified real-world facts.

For current, live, or time-sensitive information, a downstream system should retrieve and verify information from reliable current sources rather than relying on model memory.

Users should verify important factual, financial, legal, medical, or other high-impact information independently.

Usage

MC-AI is a LoRA adapter and requires its base model.

Example:

from transformers import AutoModelForMultimodalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForMultimodalLM.from_pretrained(
    "Qwen/Qwen3.5-0.8B-Base"
)

model = PeftModel.from_pretrained(
    base_model,
    "mohitchilakala/mc-ai"
)

tokenizer = AutoTokenizer.from_pretrained(
    "mohitchilakala/mc-ai"
)