v0.4.0: Mixtral-8x7B, DPO-ftx, AutoGPTQ Integration

🚨🚨 Core refactor

• Deprecate checkpoint_dir and use adapter_name_or_path instead
• Replace resume_lora_training with create_new_adapter
• Move the patches in model loading to llmtuner.model.patcher
• Bump to Transformers 4.36.1 to adapt to the Mixtral models
• Wide adaptation for FlashAttention2 (LLaMA, Falcon, Mistral)
• Temporarily disable LongLoRA due to breaking changes, which will be supported later

The above changes were made by @hiyouga in #1864

New features

• Add DPO-ftx: mixing fine-tuning gradients to DPO via the dpo_ftx argument, suggested by @lylcst in #1347 (comment)
• Integrate AutoGPTQ into the model export via the export_quantization_bit and export_quantization_dataset arguments
• Support loading datasets from ModelScope Hub by @tastelikefeet and @wangxingjun778 in #1802
• Support resizing token embeddings with the noisy mean initialization by @hiyouga in a66186b
• Support system column in both alpaca and sharegpt dataset formats

New models

• Base models
  • Mixtral-8x7B-v0.1
• Instruct/Chat models
  • Mixtral-8x7B-v0.1-instruct
  • Mistral-7B-Instruct-v0.2
  • XVERSE-65B-Chat
  • Yi-6B-Chat

Bug fix

• Improve logging for unknown arguments by @yhyu13 in #1868
• Fix an overflow issue in LLaMA2 PPO training #1742
• Fix #246 #1561 #1715 #1764 #1765 #1770 #1771 #1784 #1786 #1795 #1815 #1819 #1831