Instructions to use tbilisi-ai-lab/kona2-12B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tbilisi-ai-lab/kona2-12B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tbilisi-ai-lab/kona2-12B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tbilisi-ai-lab/kona2-12B-Instruct")
model = AutoModelForCausalLM.from_pretrained("tbilisi-ai-lab/kona2-12B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tbilisi-ai-lab/kona2-12B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tbilisi-ai-lab/kona2-12B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tbilisi-ai-lab/kona2-12B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tbilisi-ai-lab/kona2-12B-Instruct

SGLang

How to use tbilisi-ai-lab/kona2-12B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tbilisi-ai-lab/kona2-12B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tbilisi-ai-lab/kona2-12B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tbilisi-ai-lab/kona2-12B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tbilisi-ai-lab/kona2-12B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tbilisi-ai-lab/kona2-12B-Instruct with Docker Model Runner:
```
docker model run hf.co/tbilisi-ai-lab/kona2-12B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Downloads last month: 140

Safetensors

Model size

12B params

Tensor type

F32

Model tree for tbilisi-ai-lab/kona2-12B-Instruct

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

tbilisi-ai-lab/kona2-12B-Base