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Model: tbilisi-ai-lab/kona2-12B-Instruct Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- ka
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- en
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- multilingual
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- llm
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- georgian
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- instruct
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- chat
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- function-calling
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- conversational
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datasets:
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- tbilisi-ai-lab/kona-sft-mix-2.6M
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- tbilisi-ai-lab/kona-sft-function-calling-115k
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- tbilisi-ai-lab/kona-sft-function-calling-ka-93k
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base_model:
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- tbilisi-ai-lab/kona2-12B-Base
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---
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# Kona2-12B-Instruct
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**Kona2-12B-Instruct** is a 12-billion parameter instruction-tuned language model for Georgian and English. Built on [Kona2-12B-Base](https://huggingface.co/tbilisi-ai-lab/kona2-12B-Base) through supervised fine-tuning (SFT), it excels at chat, question answering, and function calling.
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## Model Summary
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| Property | Value |
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|----------|-------|
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| Parameters | 12B |
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| Architecture | Mistral (Transformer) |
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| Context Length | 32K tokens |
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| Languages | Georgian (ka), English (en), other (limited) |
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| Training | Supervised Fine-Tuning (SFT) |
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| Training Examples | ~2.8M instructions |
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| Function Calling | Yes (Hermes format) |
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| Base Model | [kona2-12B-Base](https://huggingface.co/tbilisi-ai-lab/kona2-12B-Base) |
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## Intended Uses
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### Primary Use Cases
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- Conversational AI assistants (Georgian/English)
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- Question answering and information retrieval
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- Function/tool calling applications
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- **Translation between Georgian and English** (especially strong)
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- Code generation and explanation
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- Educational and tutoring applications
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## Training
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### Training Data
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| Dataset | Examples | Description |
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|---------|----------|-------------|
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| [kona-sft-mix-2.6M](https://huggingface.co/datasets/tbilisi-ai-lab/kona-sft-mix-2.6M) | 2,606,173 | Mixed instruction dataset (KA/EN) |
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| [kona-sft-function-calling-115k](https://huggingface.co/datasets/tbilisi-ai-lab/kona-sft-function-calling-115k) | ~115K | Function calling (English) |
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| [kona-sft-function-calling-ka-93k](https://huggingface.co/datasets/tbilisi-ai-lab/kona-sft-function-calling-ka-93k) | ~93K | Function calling (Georgian) |
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**Data Sources Include:**
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- Wikipedia Q&A (RAFT-generated)
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- Orca-style reasoning
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- Self-instruct (Alpaca-style)
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- Translation pairs (EN-KA)
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- Code instructions
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- Math instructions
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- PersonaHub reasoning
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- Glaive & Hermes function calling
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### Training Procedure
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- **Method:** Supervised Fine-Tuning (SFT)
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- **LoRA Config:** r=256, alpha=512
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- **Learning Rate:** 3e-5
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- **Epochs:** 2
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- **Training Context:** 32K tokens
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- **Packing:** Enabled
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- **Precision:** BF16
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- **Infrastructure:** DeepSpeed ZeRO-2
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## Usage
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### Installation
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```bash
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pip install transformers torch accelerate
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```
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### Chat Completion
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"tbilisi-ai-lab/kona2-12B-Instruct",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("tbilisi-ai-lab/kona2-12B-Instruct")
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "რა არის საქართველოს დედაქალაქი?"}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Function Calling (Hermes Format)
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See the tokenizer's jinja template (`tokenizer_config.json`) for details on how function calling is formatted.
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```python
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "City name"}
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},
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"required": ["location"]
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}
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}
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}
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]
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messages = [
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{"role": "system", "content": "You are a helpful assistant with access to tools."},
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{"role": "user", "content": "What's the weather in Tbilisi?"}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tools=tools,
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256)
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# Output will include <tool_call>{"name": "get_weather", "arguments": {"location": "Tbilisi"}}</tool_call>
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```
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## Related Models
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| Model | Description |
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|-------|-------------|
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| [kona2-12B-Base](https://huggingface.co/tbilisi-ai-lab/kona2-12B-Base) | Pre-trained base model |
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| [kona2-12B](https://huggingface.co/tbilisi-ai-lab/kona2-12B) | DPO-aligned version (recommended) |
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| [kona2-small-3.8B](https://huggingface.co/tbilisi-ai-lab/kona2-small-3.8B) | Smaller 3.8B model |
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## Limitations
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- Training data cutoff: 2024
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## Technical Specifications
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- **Precision:** BF16/FP16 supported
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- **Minimum VRAM:** 24GB (with 4-bit quantization)
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- **Recommended:** 48GB+ for full precision
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## Citation
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```bibtex
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@misc{tbilisi2025kona2instruct,
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title = {Kona2-12B-Instruct: A Georgian Instruction-Tuned Language Model},
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author = {Tbilisi AI Lab Team},
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year = {2025},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/tbilisi-ai-lab/kona2-12B-Instruct}}
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}
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```
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## License
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This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
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## Contact
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- **Organization:** [Tbilisi AI Lab](https://huggingface.co/tbilisi-ai-lab)
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- **Website:** [ailab.ge](https://ailab.ge)
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- **Chat:** [chat.ailab.ge](https://chat.ailab.ge)
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- **API:** [api.ailab.ge](https://api.ailab.ge)
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