metadata

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

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, 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 <s>. Following instructions should not. The assistant generation will be ended by the end-of-sentence token.

E.g.

text = "<s>[INST] Кога е основан Софийският университет? [/INST]"
"Софийският университет „Св. Климент Охридски“ е създаден на 1 октомври 1888 г.</s> "
"[INST] Кой го е основал? [/INST]"

This format is available as a chat template 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

Summary

Finetuned from: mistralai/Mistral-7B-v0.1
Model type: Causal decoder-only transformer language model
Language: Bulgarian and English
License: Apache 2.0
Contact: 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:

pip install packaging ninja
pip install flash-attn

Then load the model in transformers:

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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