llm-bg
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Tucan-2.6B-v1.0-LoRA

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 ---
-base_model: INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0
-library_name: peft
 ---
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-- PEFT 0.15.2

 ---
+license: cc-by-4.0
+language:
+- bg
+base_model:
+- INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0
+tags:
+- function_calling
+- MCP
+- tool_use
 ---
+# LLMBG-ToolUse: Bulgarian Language Models for Function Calling 🇧🇬
+> 📄 **Full methodology, dataset details, and evaluation results coming in the upcoming paper**
+## Overview 🚀
+LLMBG-ToolUse 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)](https://arxiv.org/abs/2503.23278) applications.
+Built on top of [BgGPT models](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe) from [INSAIT Institute](https://insait.ai/), these models have been enhanced with function-calling capabilities.
+## Motivation 🎯
+Although BgGPT models demonstrate [strong Bulgarian language comprehension](https://arxiv.org/pdf/2412.10893), 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.
+## Models and variants 📦
+Available in three sizes with full models, LoRA adapters, and quantized GGUF variants:
+| Model Size | Full Model | LoRA Adapter | GGUF (Quantized) |
+|------------|------------|--------------|------------------|
+| **2.6B** | [LLMBG-ToolUse-2.6B-v1.0](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-2.6B-v1.0)| [LoRA](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-2.6B-v1.0-LoRA) 📍| [GGUF](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-2.6B-v1.0-GGUF) |
+| **9B** | [LLMBG-ToolUse-9B-v1.0](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-9B-v1.0) | [LoRA](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-9B-v1.0-LoRA) | [GGUF](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-9B-v1.0-GGUF) |
+| **27B** | [LLMBG-ToolUse-27B-v1.0](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-27B-v1.0) | [LoRA](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-27B-v1.0-LoRA) | [GGUF](https://huggingface.co/s-emanuilov/LLMBG-ToolUse-27B-v1.0-GGUF) |
+*GGUF variants include: q4_k_m, q5_k_m, q6_k, q8_0, q4_0 quantizations*
+## Usage 🛠️
+### Quick Start ⚡
+```bash
+pip install -U "transformers[torch]" accelerate bitsandbytes
+```
+### Prompt format ⚙️
+**Critical:** Use this format for function calling for the best results.
+<details>
+<summary><strong>📋 Required System Prompt Template</strong></summary>
+```
+<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
+```
+</details>
+### Note 📝
+**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.
+### Python example 🐍
+<details>
+<summary><strong>💻 Complete Working Example</strong></summary>
+```python
+import torch
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+# Load model
+model_name = "s-emanuilov/LLMBG-ToolUse-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)
+```
+</details>
+## Performance & Dataset 📊
+> 📄 **Full methodology, dataset details, and comprehensive evaluation results coming in the upcoming paper**
+**Dataset:** 2,000+ bilingual (Bulgarian/English) function-calling examples from manual curation + synthetic generation (Gemini/GPT-4).
+**Results:** ~40% improvement in tool-use capabilities over base BgGPT models in internal benchmarks.
+## Questions & Contact 💬
+For questions, collaboration, or feedback: **[Connect on LinkedIn](https://www.linkedin.com/in/simeon-emanuilov/)**
+## Acknowledgments 🙏
+Built on top of [BgGPT series](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe).
+## License 📄
+This work is licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).