--- language: - bg license: apache-2.0 library_name: transformers tags: - mistral - instruct - bggpt - insait base_model: mistralai/Mistral-7B-v0.1 model-index: - name: BgGPT-7B-Instruct-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.24 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=INSAIT-Institute/BgGPT-7B-Instruct-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 81.6 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=INSAIT-Institute/BgGPT-7B-Instruct-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 59.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=INSAIT-Institute/BgGPT-7B-Instruct-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 53.68 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=INSAIT-Institute/BgGPT-7B-Instruct-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 77.03 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=INSAIT-Institute/BgGPT-7B-Instruct-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 56.71 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=INSAIT-Institute/BgGPT-7B-Instruct-v0.1 name: Open LLM Leaderboard --- # INSAIT-Institute/BgGPT-7B-Instruct-v0.1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/p6d0YFHjWCQ3S12jWqO1m.png) Meet BgGPT-7B, a Bulgarian language model trained from mistralai/Mistral-7B-v0.1. BgGPT is distributed under Apache 2.0 license. This model was created by [`INSAIT Institute`](https://insait.ai/), part of Sofia University, in Sofia, Bulgaria. ## Model description The model is fine-tuned to improve its Bulgarian language capabilities using multiple datasets, including Bulgarian web crawl data, a range of specialized Bulgarian datasets sourced by INSAIT Institute, and machine translations of popular English datasets. This Bulgarian data was augmented with English datasets to retain English and logical reasoning skills. The model's tokenizer has been extended to allow for a more efficient encoding of Bulgarian words written in Cyrillic. This not only increases throughput of Cyrillic text but also performance. ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence token ``. Following instructions should not. The assistant generation will be ended by the end-of-sentence token. E.g. ``` text = "[INST] Кога е основан Софийският университет? [/INST]" "Софийският университет „Св. Климент Охридски“ е създаден на 1 октомври 1888 г. " "[INST] Кой го е основал? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ## Benchmarks The model comes with a set of Benchmarks that are translations of the corresponding English-benchmarks. These are provided at [`https://github.com/insait-institute/lm-evaluation-harness-bg`](https://github.com/insait-institute/lm-evaluation-harness-bg) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/rRwHzBuOZrYt0KG2X-ELI.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/xpGrV72htybUN6aXO9niT.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/x5CpKuJ08U017AYJVaTU4.png) ## Summary - **Finetuned from:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - **Model type:** Causal decoder-only transformer language model - **Language:** Bulgarian and English - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html) - **Contact:** [bggpt@insait.ai](mailto:bggpt@insait.ai) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch accelerate ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( model="INSAIT-Institute/BgGPT-7B-Instruct-v0.1", device_map="auto", torch_dtype=torch.bfloat16, use_flash_attn_2=True # optional ) ``` ## Use with GGML / llama.cpp The model in GGUF format [INSAIT-Institute/BgGPT-7B-Instruct-v0.1-GGUF](https://huggingface.co/INSAIT-Institute/BgGPT-7B-Instruct-v0.1-GGUF) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_INSAIT-Institute__BgGPT-7B-Instruct-v0.1) | Metric |Value| |---------------------------------|----:| |Avg. |64.82| |AI2 Reasoning Challenge (25-Shot)|60.24| |HellaSwag (10-Shot) |81.60| |MMLU (5-Shot) |59.66| |TruthfulQA (0-shot) |53.68| |Winogrande (5-shot) |77.03| |GSM8k (5-shot) |56.71|