📄 Full methodology, dataset details, and evaluation results coming in the upcoming paper
TUCAN (Tool-Using Capable Assistant Navigator) is a series of open-source Bulgarian language models fine-tuned specifically for function calling and tool use.
These models can interact with external tools, APIs, and databases, making them appropriate for building AI agents and Model Context Protocol (MCP) applications.
Built on top of BgGPT models from INSAIT Institute, these models have been enhanced with function-calling capabilities.
Although BgGPT models demonstrate strong Bulgarian language comprehension, they face challenges in maintaining the precise formatting necessary for consistent function calling. Despite implementing detailed system prompts, their performance in this specific task remains suboptimal.
This project addresses that gap by fine-tuning BgGPT, providing the Bulgarian AI community with proper tool-use capabilities in their native language.
Available in three sizes with full models, LoRA adapters, and quantized GGUF variants:
| Model Size | Full Model | LoRA Adapter | GGUF (Quantized) |
|---|---|---|---|
| 2.6B | Tucan-2.6B-v1.0 | LoRA | GGUF 📍 |
| 9B | Tucan-9B-v1.0 | LoRA | GGUF |
| 27B | Tucan-27B-v1.0 | LoRA | GGUF |
GGUF variants include: q4_k_m, q5_k_m, q6_k, q8_0, q4_0 quantizations
pip install -U "transformers[torch]" accelerate bitsandbytes
Critical: Use this format for function calling for the best results.
<bos><start_of_turn>user
Ти си полезен AI асистент, който предоставя полезни и точни отговори.
Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо.
Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```.
## Шаблон за извикване:
```tool_call
{"name": <function-name>, "arguments": <args-json-object>}```
## Налични функции:
[your function definitions here]
## Потребителска заявка :
[your query in Bulgarian]<end_of_turn>
<start_of_turn>model
The model only generates the tool_call blocks with function names and parameters - it doesn't actually execute the functions. Your client application must parse these generated calls, execute the actual functions (API calls, database queries, etc.), and provide the results back to the model in tool_response blocks for the conversation to continue the interperation of the results. A full demo is comming soon.
import torch
import json
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
# Load model
model_name = "s-emanuilov/Tucan-2.6B-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="eager" # Required for Gemma models
)
# Create prompt with system template
def create_prompt(functions, user_query):
system_prompt = """Ти си полезен AI асистент, който предоставя полезни и точни отговори.
Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо.
Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```.
## Шаблон за извикване:
```tool_call
{{"name": <function-name>, "arguments": <args-json-object>}}```
"""
functions_text = json.dumps(functions, ensure_ascii=False, indent=2)
full_prompt = f"{system_prompt}\n## Налични функции:\n{functions_text}\n\n## Потребителска заявка:\n{user_query}"
chat = [{"role": "user", "content": full_prompt}]
return tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# Example usage
functions = [{
"name": "create_calendar_event",
"description": "Creates a new event in Google Calendar.",
"parameters": {
"type": "object",
"properties": {
"title": {"type": "string"},
"date": {"type": "string"},
"start_time": {"type": "string"},
"end_time": {"type": "string"}
},
"required": ["title", "date", "start_time", "end_time"]
}
}]
query = "Създай събитие 'Годишен преглед' за 8-ми юни 2025 от 14:00 до 14:30."
# Generate response
prompt = create_prompt(functions, query)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=1024,
temperature=0.1,
top_k=25,
top_p=1.0,
repetition_penalty=1.1,
do_sample=True,
eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<end_of_turn>")],
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(result)
📄 Full methodology, dataset details, and comprehensive evaluation results coming in the upcoming paper
Dataset: 8,000+ bilingual (Bulgarian/English) function-calling examples across 1,000+ topics, including tool calls with single/multiple arguments, optional parameters, follow-up queries, multi-tool selection, ambiguous queries requiring clarification, and conversational interactions without tool use. Data sourced from manual curation and synthetic generation (Gemini Pro 2.5/GPT-4.1/Sonnet 4).
Results: ~40% improvement in tool-use capabilities over base BgGPT models in internal benchmarks.
For questions, collaboration, or feedback: Connect on LinkedIn
Built on top of BgGPT series.
This work is licensed under CC-BY-4.0.
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Base model
google/gemma-2-2b