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README.md

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+
+---
+
+library_name: transformers
+tags:
+- gemma2
+- instruct
+- bggpt
+- insait
+license: gemma
+language:
+- bg
+- en
+base_model:
+- google/gemma-2-2b-it
+- google/gemma-2-2b
+pipeline_tag: text-generation
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF
+This is quantized version of [INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0](https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0) created using llama.cpp
+
+# Original Model Card
+
+
+# INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/p6d0YFHjWCQ3S12jWqO1m.png)
+
+INSAIT introduces **BgGPT-Gemma-2-2.6B-IT-v1.0**, a state-of-the-art Bulgarian language model based on **google/gemma-2-2b** and **google/gemma-2-2b-it**.
+BgGPT-Gemma-2-2.6B-IT-v1.0 is **free to use** and distributed under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
+This model was created by [`INSAIT`](https://insait.ai/), part of Sofia University St. Kliment Ohridski, in Sofia, Bulgaria.
+
+
+# Model description
+
+The model was built on top of Google’s Gemma 2 2B open models.
+It was continuously pre-trained on around 100 billion tokens (85 billion in Bulgarian) using the Branch-and-Merge strategy INSAIT presented at [EMNLP’24](https://aclanthology.org/2024.findings-emnlp.1000/), 
+allowing the model to gain outstanding Bulgarian cultural and linguistic capabilities while retaining its English performance. 
+During the pre-training stage, we use various datasets, including Bulgarian web crawl data, freely available datasets such as Wikipedia, a range of specialized Bulgarian datasets sourced by the INSAIT Institute,
+and machine translations of popular English datasets.
+The model was then instruction-fine-tuned on a newly constructed Bulgarian instruction dataset created using real-world conversations.
+For more information check our [blogpost](https://models.bggpt.ai/blog/).
+
+# Benchmarks and Results
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65fefdc282708115868203aa/9pp8aD1yvoW-cJWzhbHXk.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65fefdc282708115868203aa/33CjjtmCeAcw5qq8DEtJj.png)
+
+We evaluate our models on a set of standard English benchmarks, a translated version of them in Bulgarian, as well as, Bulgarian specific benchmarks we collected:
+
+- **Winogrande challenge**: testing world knowledge and understanding
+- **Hellaswag**: testing sentence completion
+- **ARC Easy/Challenge**: testing logical reasoning
+- **TriviaQA**: testing trivia knowledge
+- **GSM-8k**: solving multiple-choice questions in high-school mathematics
+- **Exams**: solving high school problems from natural and social sciences
+- **MON**: contains exams across various subjects for grades 4 to 12
+
+These benchmarks test logical reasoning, mathematics, knowledge, language understanding and other skills of the models and are provided at https://github.com/insait-institute/lm-evaluation-harness-bg.
+The graphs above show the performance of BgGPT 2.6B compared to other small open language models such as Microsoft's Phi 3.5 and Alibaba's Qwen 2.5 3B.
+The BgGPT model not only surpasses them, but also **retains English performance** inherited from the original Google Gemma 2 models upon which it is based.
+
+# Use in 🤗 Transformers
+First install the latest version of the transformers library:
+```
+pip install -U 'transformers[torch]'
+```
+Then load the model in transformers:
+```python
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="eager",
+    device_map="auto",
+)
+```
+
+# Recommended Parameters
+
+For optimal performance, we recommend the following parameters for text generation, as we have extensively tested our model with them:
+
+```python
+from transformers import GenerationConfig
+
+generation_params = GenerationConfig(
+    max_new_tokens=2048,              # Choose maximum generation tokens
+    temperature=0.1,
+    top_k=25,
+    top_p=1,
+    repetition_penalty=1.1,
+    eos_token_id=[1,107]
+)
+```
+
+In principle, increasing temperature should work adequately as well.
+
+# Instruction format
+
+In order to leverage instruction fine-tuning, your prompt should begin with a beginning-of-sequence token `<bos>` and be formatted in the Gemma 2 chat template. `<bos>` should only be the first token in a chat sequence.
+
+E.g.
+```
+<bos><start_of_turn>user
+Кога е основан Софийският университет?<end_of_turn>
+<start_of_turn>model
+ 
+```
+
+This format is also available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
+
+```python
+tokenizer = AutoTokenizer.from_pretrained(
+    "INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0",
+    use_default_system_prompt=False,
+)
+
+messages = [
+    {"role": "user", "content": "Кога е основан Софийският университет?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+  messages,
+  return_tensors="pt",
+  add_generation_prompt=True,
+  return_dict=True
+)
+
+outputs = model.generate(
+  **input_ids,
+  generation_config=generation_params
+)
+print(tokenizer.decode(outputs[0]))
+
+```
+
+**Important Note:** Models based on Gemma 2 such as BgGPT-Gemma-2-2.6B-IT-v1.0 do not support flash attention. Using it results in degraded performance.
+
+# Use with GGML / llama.cpp
+
+The model and instructions for usage in GGUF format are available at [INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF](https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF).
+
+# Community Feedback
+
+We welcome feedback from the community to help improve BgGPT. If you have suggestions, encounter any issues, or have ideas for improvements, please:
+- Share your experience using the model through Hugging Face's community discussion feature or
+- Contact us at [[email protected]](mailto:[email protected])
+
+Your real-world usage and insights are valuable in helping us optimize the model's performance and behaviour for various use cases.
+
+# Summary
+- **Finetuned from:** [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it); [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b);
+- **Model type:** Causal decoder-only transformer language model
+- **Language:** Bulgarian and English
+- **Contact:** [[email protected]](mailto:[email protected])
+- **License:** BgGPT is distributed under [Gemma Terms of Use](https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0/raw/main/LICENSE)