Zelin-4B

🧠 Zelin-4B — Argentine Spanish Minecraft Discord Bot LLM

Fine-tuned Qwen3-4B-Instruct for Zelin, the autonomous AI bot of the TomateSMP Minecraft server.

🎯 What It Does

Zelin-4B is specialized for:

• Argentine Spanish chat — speaks natively with "vos", "che", "dale", "qué bajón"
• Minecraft server management — understands commands, server status, gameplay
• Intent detection — classifies what users want (JSON output)
• Moderation decisions — detects toxicity and suggests actions (JSON output)
• Sentiment analysis — reads emotional tone in Argentine context (JSON output)
• Short Discord responses — 1-3 lines, casual, no formal language

📊 Model Details

🚀 Quick Start

llama.cpp (CPU, fastest)

# Download GGUF
huggingface-cli download TomatitoToho/Zelin-4B zelin-4b-Q4_K_M.gguf --local-dir .

# Run server
llama-server -m zelin-4b-Q4_K_M.gguf -c 2048 -t 4 --port 8080

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="zelin-4b-Q4_K_M.gguf", n_ctx=2048)

result = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "Sos Zelin, la IA del servidor TomateSMP..."},
        {"role": "user", "content": "hola zelin, qué onda"},
    ],
    max_tokens=100,
    temperature=0.7,
)
print(result["choices"][0]["message"]["content"])
# → "holaa, qué onda che"

HuggingFace Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("TomatitoToho/Zelin-4B")
tokenizer = AutoTokenizer.from_pretrained("TomatitoToho/Zelin-4B")

📝 Training Data

🔧 Training Configuration

# QLoRA Configuration
r = 16
alpha = 16
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
quantization = "4-bit"

# Training Hyperparameters
batch_size = 4
gradient_accumulation = 4
learning_rate = 2e-4
max_steps = 500
optimizer = "adamw_8bit"
scheduler = "cosine"

🏗️ Architecture

Qwen3-4B-Instruct
    ├── GQA (Grouped Query Attention) — 2-3x faster inference
    ├── RoPE (Rotary Position Embeddings) — better length generalization
    ├── SwiGLU activation — better than GeLU
    └── Hybrid thinking — toggle reasoning on/off
         │
    ┌────┴────┐
    │  QLoRA  │  r=16, alpha=16
    │  Adapters │  7 target modules
    └────┬────┘
         │
    Zelin-4B (Fine-tuned)
         │
    ┌────┴────┐
    │  GGUF   │  Q4_K_M quantization
    │  Export  │  ~2.5 GB, 30-50 tok/s CPU
    └─────────┘

📈 Performance

🤝 Integration with Zelin Bot

// In zelin-v6/src/local-ai.js
const ZELIN_CUSTOM_REPO = 'TomatitoToho/Zelin-4B';
const ZELIN_CUSTOM_FILE = 'zelin-4b-Q4_K_M.gguf';

// The custom model handles:
// - Fast intent detection (replaces callAIBackground)
// - Moderation classification
// - Sentiment analysis
// - Casual chat fallback
// RigoChat-7B-v2 handles: main conversation responses

📦 Repositories

• Model: TomatitoToho/Zelin-4B
• Dataset: TomatitoToho/zelin-conversations
• Inference Space: TomatitoToho/zelin-llm
• Training Space: TomatitoToho/zelin-train
• Zelin Bot: TomatitoToho/zelin-v6

📜 License

Apache 2.0 — Based on Qwen3-4B (Apache 2.0) + custom training data.