Tbilisi AI Lab

Open, Georgian-first Generative AI.
We are a non-profit on a mission to build capable, affordable, and open Georgian language models.

Georgian is a low-resource language with ~4M speakers. We believe Georgian speakers deserve their own ChatGPT-like, productivity-boosting AI — built with and for our community.

🔔 What’s new (Oct 2025)

We have open-sourced all our models and datasets — from pretraining corpora to every stage of fine-tuning (instruction/SFT, function-calling, and preference/DPO). Explore everything on our Hugging Face org:

• Org: https://huggingface.co/tbilisi-ai-lab

Examples of what’s now public:

• Models
  • 12B family: Base + Instruct
    • https://huggingface.co/tbilisi-ai-lab/kona2-12B
    • https://huggingface.co/tbilisi-ai-lab/kona2-12B-Base
    • https://huggingface.co/tbilisi-ai-lab/kona2-12B-Instruct
  • Small 3.8B SLM
    • https://huggingface.co/tbilisi-ai-lab/kona2-small-3.8B
• Datasets (selection)
  • Instruction (SFT) mix (2.61M pairs): https://huggingface.co/datasets/tbilisi-ai-lab/kona-sft-mix-2.6M
  • Function-calling SFT (EN 115k / KA 93k):
    • https://huggingface.co/datasets/tbilisi-ai-lab/kona-sft-function-calling-115k
    • https://huggingface.co/datasets/tbilisi-ai-lab/kona-sft-function-calling-ka-93k
  • Preference (DPO) mix (387k): https://huggingface.co/datasets/tbilisi-ai-lab/kona-dpo-mix-387k
  • Evaluation/Knowledge sets in Georgian (e.g., SuperGLUE-KA, BoolQ-KA, CommonsenseQA-KA, code-instruct-KA, wiki-QA-KA, human-translated EN↔KA, etc.):
    • Browse all: https://huggingface.co/tbilisi-ai-lab/datasets

Why we exist

• Language equity: Great AI shouldn’t be limited to high-resource languages.
• Local impact: Better Georgian NLP improves education, services, accessibility, and economic opportunity.
• Open science: We share models, data, and recipes so others can reproduce and build on our work.

Quickstart (12B)

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

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

chat = pipeline("text-generation", model=model, tokenizer=tok)
prompt = "გთხოვ, შეაჯამე ეს ტექსტი მოკლედ (ქართული): ქართული ენა უნიკალურია..."
out = chat(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
print(out[0]["generated_text"])