license, language, library_name, pipeline_tag, tags, datasets, base_model

license	language	library_name	pipeline_tag	tags	datasets	base_model
apache-2.0	ka en multilingual	transformers	text-generation	llm georgian instruct chat function-calling conversational	tbilisi-ai-lab/kona-sft-mix-2.6M tbilisi-ai-lab/kona-sft-function-calling-115k tbilisi-ai-lab/kona-sft-function-calling-ka-93k	tbilisi-ai-lab/kona2-12B-Base

Kona2-12B-Instruct

Kona2-12B-Instruct is a 12-billion parameter instruction-tuned language model for Georgian and English. Built on Kona2-12B-Base through supervised fine-tuning (SFT), it excels at chat, question answering, and function calling.

Model Summary

Property	Value
Parameters	12B
Architecture	Mistral (Transformer)
Context Length	32K tokens
Languages	Georgian (ka), English (en), other (limited)
Training	Supervised Fine-Tuning (SFT)
Training Examples	~2.8M instructions
Function Calling	Yes (Hermes format)
Base Model	kona2-12B-Base

Intended Uses

Primary Use Cases

• Conversational AI assistants (Georgian/English)
• Question answering and information retrieval
• Function/tool calling applications
• Translation between Georgian and English (especially strong)
• Code generation and explanation
• Educational and tutoring applications

Training

Training Data

Dataset	Examples	Description
kona-sft-mix-2.6M	2,606,173	Mixed instruction dataset (KA/EN)
kona-sft-function-calling-115k	~115K	Function calling (English)
kona-sft-function-calling-ka-93k	~93K	Function calling (Georgian)

Data Sources Include:

• Wikipedia Q&A (RAFT-generated)
• Orca-style reasoning
• Self-instruct (Alpaca-style)
• Translation pairs (EN-KA)
• Code instructions
• Math instructions
• PersonaHub reasoning
• Glaive & Hermes function calling

Training Procedure

• Method: Supervised Fine-Tuning (SFT)
• LoRA Config: r=256, alpha=512
• Learning Rate: 3e-5
• Epochs: 2
• Training Context: 32K tokens
• Packing: Enabled
• 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-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("tbilisi-ai-lab/kona2-12B-Instruct")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "რა არის საქართველოს დედაქალაქი?"}
]

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

outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Function Calling (Hermes Format)

See the tokenizer's jinja template (tokenizer_config.json) for details on how function calling is formatted.

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"}
                },
                "required": ["location"]
            }
        }
    }
]

messages = [
    {"role": "system", "content": "You are a helpful assistant with access to tools."},
    {"role": "user", "content": "What's the weather in Tbilisi?"}
]

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

outputs = model.generate(inputs, max_new_tokens=256)
# Output will include <tool_call>{"name": "get_weather", "arguments": {"location": "Tbilisi"}}</tool_call>

Related Models

Model	Description
kona2-12B-Base	Pre-trained base model
kona2-12B	DPO-aligned version (recommended)
kona2-small-3.8B	Smaller 3.8B model

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{tbilisi2025kona2instruct,
  title        = {Kona2-12B-Instruct: A Georgian Instruction-Tuned Language Model},
  author       = {Tbilisi AI Lab Team},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/tbilisi-ai-lab/kona2-12B-Instruct}}
}

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