---
license: mit
datasets:
  - IlyaGusev/ru_turbo_alpaca
  - IlyaGusev/ru_turbo_alpaca_evol_instruct
  - IlyaGusev/ru_turbo_saiga
  - IlyaGusev/ru_sharegpt_cleaned
  - IlyaGusev/oasst1_ru_main_branch
  - IlyaGusev/gpt_roleplay_realm
  - lksy/ru_instruct_gpt4
language:
  - ru
  - en
library_name: peft
pipeline_tag: conversational
tags:
  - Saiga
  - ruGPT-3.5
  - 13B
  - chat
  - lora
  - Peft
  - adapter
---

# ruGPT-3.5 13B LoRA: Adapter-Only Version

Welcome to the adapter-only version of ruGPT-3.5 13B LoRA. This model is built upon the foundation of [ruGPT-3.5-13B](https://huggingface.co/ai-forever/ruGPT-3.5-13B).

📌 Important: This model was trained using settings identical to [GigaSaiga](https://huggingface.co/IlyaGusev/gigasaiga_lora), but incorporates two additional datasets.

🔗 Training code is [here](https://github.com/EvilFreelancer/ruGPT-3.5-13B-lora).

> Note: If you prefer, you can opt to use the ruGPT-3.5 13B fp16 base model.

## 📚 Training Datasets

The datasets utilized for training this model are consistent with those used for [Saiga-2](https://github.com/IlyaGusev/rulm).

Here's the comprehensive list:

- [ru_turbo_alpaca](https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca)
- [ru_turbo_alpaca_evol_instruct](https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca_evol_instruct)
- [ru_turbo_saiga](https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga)
- [ru_sharegpt_cleaned](https://huggingface.co/datasets/IlyaGusev/ru_sharegpt_cleaned)
- [oasst1_ru_main_branch](https://huggingface.co/datasets/IlyaGusev/oasst1_ru_main_branch)
- [gpt_roleplay_realm](https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm)
- [ru_instruct_gpt4](https://huggingface.co/datasets/lksy/ru_instruct_gpt4)

## 🛠 Training Procedure

The following `bitsandbytes` quantization config was used during training:

- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32

## ⚙️ Framework Versions

Ensure you have the following framework versions for compatibility:

- PyTorch 2.1.0
- PEFT 0.5.0
- bitsandbytes 0.41.1
- transformers 4.34.0