---
license: mit
base_model: Qwen/Qwen2-1.5B
tags:
- roblox
- lua
- coding
- fine-tuned
- game-development
- roblox-studio
language:
- en
pipeline_tag: text-generation
---

# 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:
```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:
```bash
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:
```javascript
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:
```lua
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:
```lua
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

---

**Made with ❤️ for the Roblox development community**