kona2-12B

Kona2-12B

Kona2-12B is the flagship 12-billion parameter Georgian language model from Tbilisi AI Lab. Built on Kona2-12B-Instruct and aligned using Direct Preference Optimization (DPO), it delivers higher quality, more helpful, and better-aligned responses.

This is the recommended model for production use.

Model Summary

Model Hierarchy

mistralai/Mistral-Nemo-Base-2407
    │
    ├── Expand Vocabulary (+20K Georgian tokens)
    │
    └── kona2-12B-Base (continue pre-training, ~30B tokens)
        │
        └── kona2-12B-Instruct (SFT on ~2.8M instructions)
            │
            └── kona2-12B (DPO on 387K preference pairs) ← YOU ARE HERE

Intended Uses

Primary Use Cases

• Production conversational AI (Georgian/English)
• High-quality question answering
• Function/tool calling with improved reliability
• Translation (especially strong)
• Content generation with better alignment
• Customer support automation

Training

DPO Training Data

DPO Pair Sources:

DPO Scenarios

The model was trained on 4 distinct function-calling scenarios:

Training Procedure

• Method: Direct Preference Optimization (DPO)
• DPO Beta: 0.1
• LoRA Config: r=256, alpha=512
• Learning Rate: 5e-6
• Epochs: 2
• Training Context: 32K tokens
• Precision: BF16
• Infrastructure: DeepSpeed ZeRO-2

Usage

Installation

pip install transformers torch accelerate

Chat Completion

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "tbilisi-ai-lab/kona2-12B",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("tbilisi-ai-lab/kona2-12B")

messages = [
    {"role": "system", "content": "You are a helpful, harmless, and honest assistant."},
    {"role": "user", "content": "დამეხმარე პითონზე ფუნქციის დაწერაში, რომელიც ითვლის ფაქტორიალს."}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    return_tensors="pt",
    add_generation_prompt=True
).to(model.device)

outputs = model.generate(
    inputs, 
    max_new_tokens=512, 
    temperature=0.7,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Related Models

Limitations

• Training data cutoff: 2024

Technical Specifications

• Precision: BF16/FP16 supported
• Minimum VRAM: 24GB (with 4-bit quantization)
• Recommended: 48GB+ for full precision

Citation

@misc{tbilisi2025kona2,
  title        = {Kona2-12B: A DPO-Aligned Georgian Language Model},
  author       = {Tbilisi AI Lab Team},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/tbilisi-ai-lab/kona2-12B}}
}

License

This model is released under the Apache 2.0 License.

Contact

• Organization: Tbilisi AI Lab
• Website: ailab.ge
• Chat: chat.ailab.ge
• API: api.ailab.ge