realalexdev/prysmisai-v1

PrysmisAI v1 - Roblox Lua Specialist

PrysmisAI is a fine-tuned AI model specifically designed for Roblox Lua programming and game development. Built on the Qwen2-1.5B architecture, it has been trained on over 155,000+ Roblox Lua examples to provide expert assistance for Roblox developers.

🎯 Model Details

• Base Model: Qwen/Qwen2-1.5B
• Training Data: 155,000+ Roblox Lua examples
• Specialization: Roblox game development, Lua scripting, UI systems, data management
• Parameters: 1.5B
• Context Length: 512 tokens
• License: MIT

🚀 Usage

Python:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "realalexdev/prysmisai-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "How do I create a DataStore system in Roblox?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Hugging Face Inference API:

curl https://api-inference.huggingface.co/models/realalexdev/prysmisai-v1 \
  -X POST \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "How do I create a Roblox DataStore system?"}'

JavaScript/Node.js:

const response = await fetch("https://api-inference.huggingface.co/models/realalexdev/prysmisai-v1", {
  method: "POST",
  headers: {
    "Authorization": "Bearer YOUR_API_TOKEN",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    inputs: "How do I create a Roblox DataStore system?",
    parameters: {
      max_length: 500,
      temperature: 0.7
    }
  })
});

const result = await response.json();
console.log(result[0].generated_text);

📚 Training Data

Trained on 155,000+ examples covering:

Core Systems:

• DataStore Optimization: Caching, retry logic, data management
• Advanced Physics: Vehicle systems, suspension, collision detection
• Network Programming: Remote events, rate limiting, error handling
• UI Animations: TweenService, sliding panels, hover effects
• Sound Systems: 3D audio, distance attenuation, environmental effects

Game Mechanics:

• Pathfinding: A* algorithm, NPC navigation, obstacle avoidance
• Inventory Systems: Item stacking, categories, drag-drop
• Combat Systems: Damage calculation, hit detection, status effects
• Quest Systems: Objectives, rewards, progress tracking
• Player Progression: Levels, experience, skill trees

Advanced Topics:

• Server Management: Optimization, scaling, monitoring
• Client-Server Sync: State management, conflict resolution
• Particle Effects: Visual effects, performance optimization
• Camera Systems: Cinematography, player controls
• Security: Anti-exploit, data validation

🎮 Example Use Cases

DataStore System:

local DataStoreService = game:GetService("DataStoreService")
local playerDataStore = DataStoreService:GetDataStore("PlayerData_v2")

local function savePlayerData(player, data)
    local success, err = pcall(function()
        playerDataStore:SetAsync("Player_" .. player.UserId, data)
    end)
    return success
end

Combat System:

local function calculateDamage(baseDamage, damageType, targetArmor)
    local damageTypeData = DAMAGE_TYPES[damageType]
    local armorData = ARMOR_TYPES[targetArmor]
    
    local damage = baseDamage * damageTypeData.multiplier
    local reduction = armorData.reduction
    return math.floor(damage * (1 - reduction))
end

⚡ Performance

• Inference Speed: Fast on modern CPUs, excellent on GPUs
• Memory Usage: ~3GB RAM (FP16), ~6GB RAM (FP32)
• Optimization: Supports 4-bit quantization for deployment

🔧 Limitations

• Specialized: Optimized for Roblox Lua, may not perform as well on general coding
• Context: Maximum 512 tokens context length
• Training: Trained on Roblox-specific patterns and best practices
• Language: Primarily English language support

📖 Model Architecture

Based on Qwen2-1.5B with the following specifications:

• Attention: Full attention mechanism
• Layers: 28 transformer layers
• Hidden Size: 1536
• Heads: 12 attention heads
• Vocabulary: 151,936 tokens

🤝 Contributing

This model is continuously being improved. Suggestions and contributions are welcome!

📄 License

This model is licensed under the MIT License. You are free to use, modify, and distribute this model for any purpose.

📞 Contact

For questions, feedback, or collaboration opportunities:

• Website: https://codeit-7u2k.onrender.com/
• Discord: [Your Discord server]
• Email: [Your email]

🙏 Acknowledgments

• Base model: Qwen/Qwen2-1.5B by Alibaba Cloud
• Training framework: Transformers by Hugging Face
• Roblox community for excellent documentation and examples

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Made with ❤️ for the Roblox development community