Training MAGIC AI
Budget: $250 – $750 USD
Competition: Train the Best MAGIC Base Model — $200 Prize
I am launching a competition to create the best-performing custom MAGIC base model.
Prize: $200 to the top entry.
MAGIC (Memory Augmented Generally Intelligent Cognition) is a modular AI architecture that combines retrieval, memory, transformers, difformers, MoE routing, and multimodal adapters. Competitors will receive:
* The MAGIC architecture codebase
* magic_trainer.py (modifiable)
* magic_chat.py (modifiable)
* Core training data that must be included in the final model
Your job is to train and submit a high-quality MAGIC base that demonstrates strong reasoning, conversational ability, and benchmark performance.
---
Prize
$200 USD to the best submission
(I may also hire or bonus follow-up work for strong contenders.)
---
What You Will Receive
After accepting the project, you will get:
* `magic_trainer.py` (you may modify freely)
* `magic_chat.py` (you may modify freely)
* Core training data that must be included
* MAGIC architecture code
* Instructions for running MAGIC and training modules
---
Requirements
Your trained MAGIC base must:
1. Include the provided training dataset
This must be incorporated into your final training run.
2. Maintain strong conversational ability
The model must be able to:
* carry intelligent dialogue
* reason coherently
* stay consistent during multi-turn conversations
* answer analytical, technical, or general knowledge questions
3. Provide benchmark results
You must include:
* Comparisons against at least 1–2 reference models (any open-source LLM of your choice)
* A clear summary of performance metrics (perplexity, accuracy, or other relevant metrics)
* Optional: qualitative evaluations (conversation transcripts, reasoning tests, etc.)
4. Supply all training modifications
You may:
* adjust `magic_trainer.py`
* choose the datasets
* apply your own fine-tuning methods
* modify architecture parameters if needed
You must provide:
* your training script(s)
* any additional dataset sources
* instructions to reproduce the training run
5. Final Deliverables
Each competitor must submit:
1. The trained MAGIC base model weights
2. Benchmarks & evaluation summary
3. Modified trainer.py (if changed)
4. Clear reproduction instructions
5. Any additional training data used (or links to it)
---
Judging Criteria
Submissions will be evaluated based on:
1. Benchmark performance
2. Conversational intelligence
3. Consistency and reasoning quality
4. How well MAGIC’s architecture is utilized
5. My personal preference during testing
---
Communication
Contestants may message me at any time with:
* questions about the architecture
* clarifications on dataset usage
* details about allowed modifications
* troubleshooting issues
---
Summary
This competition is ideal for:
* Machine learning researchers
* Fine-tuning experts
* Large language model hobbyists
* AI developers wanting to showcase talent
* Anyone confident they can train a unique, powerful model
The winning model will become the official MAGIC base.
I am launching a competition to create the best-performing custom MAGIC base model.
Prize: $200 to the top entry.
MAGIC (Memory Augmented Generally Intelligent Cognition) is a modular AI architecture that combines retrieval, memory, transformers, difformers, MoE routing, and multimodal adapters. Competitors will receive:
* The MAGIC architecture codebase
* magic_trainer.py (modifiable)
* magic_chat.py (modifiable)
* Core training data that must be included in the final model
Your job is to train and submit a high-quality MAGIC base that demonstrates strong reasoning, conversational ability, and benchmark performance.
---
Prize
$200 USD to the best submission
(I may also hire or bonus follow-up work for strong contenders.)
---
What You Will Receive
After accepting the project, you will get:
* `magic_trainer.py` (you may modify freely)
* `magic_chat.py` (you may modify freely)
* Core training data that must be included
* MAGIC architecture code
* Instructions for running MAGIC and training modules
---
Requirements
Your trained MAGIC base must:
1. Include the provided training dataset
This must be incorporated into your final training run.
2. Maintain strong conversational ability
The model must be able to:
* carry intelligent dialogue
* reason coherently
* stay consistent during multi-turn conversations
* answer analytical, technical, or general knowledge questions
3. Provide benchmark results
You must include:
* Comparisons against at least 1–2 reference models (any open-source LLM of your choice)
* A clear summary of performance metrics (perplexity, accuracy, or other relevant metrics)
* Optional: qualitative evaluations (conversation transcripts, reasoning tests, etc.)
4. Supply all training modifications
You may:
* adjust `magic_trainer.py`
* choose the datasets
* apply your own fine-tuning methods
* modify architecture parameters if needed
You must provide:
* your training script(s)
* any additional dataset sources
* instructions to reproduce the training run
5. Final Deliverables
Each competitor must submit:
1. The trained MAGIC base model weights
2. Benchmarks & evaluation summary
3. Modified trainer.py (if changed)
4. Clear reproduction instructions
5. Any additional training data used (or links to it)
---
Judging Criteria
Submissions will be evaluated based on:
1. Benchmark performance
2. Conversational intelligence
3. Consistency and reasoning quality
4. How well MAGIC’s architecture is utilized
5. My personal preference during testing
---
Communication
Contestants may message me at any time with:
* questions about the architecture
* clarifications on dataset usage
* details about allowed modifications
* troubleshooting issues
---
Summary
This competition is ideal for:
* Machine learning researchers
* Fine-tuning experts
* Large language model hobbyists
* AI developers wanting to showcase talent
* Anyone confident they can train a unique, powerful model
The winning model will become the official MAGIC base.