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metadata

title: README
emoji: 📚
colorFrom: pink
colorTo: yellow
sdk: static
pinned: true
license: apache-2.0

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
- 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"])