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
| Property | Value |
|---|---|
| Parameters | 12B |
| Architecture | Mistral (Transformer) |
| Context Length | 32K tokens |
| Languages | Georgian (ka), English (en), other (limited) |
| Training | Direct Preference Optimization (DPO) |
| Preference Pairs | 387,319 |
| Function Calling | Yes (Hermes format) |
| Base Model | kona2-12B-Instruct |
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
| Dataset | Pairs | Description |
|---|---|---|
| kona-dpo-mix-387k | 387,319 | Mixed preference pairs |
DPO Pair Sources:
| Source | Pairs | Strategy |
|---|---|---|
| Instruction Augmentation (Sonnet 3.5) | ~29K | Claude 3.5 Sonnet as "chosen", base model as "rejected" |
| Function Calling (tools+call) | ~5K | Correct tool calls vs text responses |
| Function Calling (tools, no call) | ~5K | Correct text vs incorrect tool calls |
| Function Calling (no tools) | ~5K | Appropriate responses without tools |
| Hermes Chats | ~17K | High-quality conversation pairs |
| General Instructions | ~320K | Mixed instruction following |
DPO Scenarios
The model was trained on 4 distinct function-calling scenarios:
| Scenario | Tools Available | Should Call | Chosen | Rejected |
|---|---|---|---|---|
system_tools_yes_call_yes | Yes | Yes | Tool call | Text response |
system_tools_yes_call_no | Yes | No | Text response | Tool call |
tools_yes_call_yes | Yes | Yes | Tool call | Text response |
tools_yes_call_no | Yes | No | Text response | Tool call |
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
| Model | Stage | Use Case |
|---|---|---|
| kona2-12B-Base | Pre-training | Further fine-tuning |
| kona2-12B-Instruct | SFT | If DPO behavior is not desired |
| kona2-small-3.8B | All | Resource-constrained environments |
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