Intel® Extension for Transformers v1.4 Release

Highlights
Features
Productivity
Examples
Bug Fixing

Highlights

• AutoRound is SOTA weight-only quantization (WOQ) algorithm for low-bit LLM inference on typical LLMs. This release includes support for AutoRound quantization and inference with INT4 models quantized by AutoRound.

Features

• LLM Workflow/Neural Chat
  • Support Triton Serving/Deployment on HPU/GPU (4657036, c57c17e )
  • Enable HF/TGI Endpoint (5b84e5, 525ea8, 34b3e9 )
  • Enable RAG + ChatGPT flow (de8800 )
  • [UI] Customized side by side (5835c3 )
  • Support Multi-language TTS (260155a )
  • Support language detection & translation for RAG chat (99df35d8 )
  • Add file management in RAG API (b7fc01de )
  • Support deepspeed for Textchat API (7b0b995 )
• Transformers Extension for LLM Optimization
  • Support Autoround WOQ on CPU (6c42b53, a6c05b9 )
  • Support GPTQ/Autoround on GPU (6a458f0, 4b5046, 7bebb01, 7084e7f )
  • Refine WOQ Config (97f0db9f )
  • Reorg transformers and langchain structure (ae54f69, 756646b )
  • Promote Qbits as Module (468d7cf )

Productivity

• Add bm25 algorithm into retrievers (a19467d0 )
• Add evaluation perplexity during training (2858ed1 )
• Enhance embedding to support jit model (588c60 )
• Update the character checking function to enable the Chinese character (0da63fe1 )
• Enlarge the context window for HPU graph recompile (dcaf17ac )
• Support IPEX bf16 & fp32 optimization for emebedding model (b51552 )
• Enable lm_eval during training. (2de883 )
• Refine setup.py and requirements.txt (436847 )
• Improve WOQ model saving and loading (30d9d10, 1065d81c )
• Add layerwise for WOQ RTN & GPTQ (15a848f3 )
• Update sparseGPT example (3ae0cd0 )
• Changed regular expression to add support of the unicode characters (fd2516b )
• Check and convert contiguous tensor when model saving (d21bb3e )
• Support load model from modelscope using NeuralSpeed (20ae00 )

Examples

• Support microsoft/biogpt model (3e7e35 )
• Add finetuning example for gemma-2b on ARC. (ffa8f3c6 )
• Add example to use RAG+OpenAI LLM (3c5959 )
• Enable mistralai/Mixtral-8x7B-v0.1 LORA finetuning on Gaudi2 (7539c35 )
• Enable image2text finetuning example on CPU (ef94aeaa )
• Add LLaVA-NeXT (feff1ec0 )

Bug Fixing

• Fix CLM tasks when transformers >= 4.38.1 (98bfcf8 )
• Fix distilgpt2 TF signature issue (a7c15a9f )
• Add User input + max tokens requested exceeds model context window error response (ae91bf8 )
• Fix audio plugin sample code issue and provide a way to set tts/asr model path (db7da09 )
• Fix modeling_auto trust_remote_code issue (3a0987 )
• Fix lm-eval neuralspeed loading model (cd6e488 )
• Fixed weight-only config save issue (5c92fe31 )
• Fix index error in Child-parent retriever (8797cfe )
• Fix WOQ int8 unpack weight (edede4 )
• Fix gptq desc_act and static_group (528d7de )
• Fix request.client=None issue (494a571 )
• Fix WOQ huggingface model loading (01b1a44 )
• Fix SQ model restore loading (1e00f29 )
• Remove redundant parameters for WOQ saving config and fix GPTQ issue (ef0882f6 )
• Fixed exmple error for Intel GPU WOQ (8fdde06 )
• Fix woq autoround last layer quant issue (d21bb3e )
• Fix code-generation params (ab2fd05 )

Validated Configurations

• Python 3.8, 3.9, 3.10, 3.11
• Ubuntu 20.04 & Windows 10
• Intel® Extension for TensorFlow 2.13.0, 2.14.0
• PyTorch 2.2.0+cpu 2.1.0+cpu
• Intel® Extension for PyTorch 2.2.0+cpu, 2.1.0+cpu

Thanks to these Contributors
Thanks for the contribution from dillonalaird, igeni, sramakintel, alexsin368 and huiyan2021
Welcome to contribute to our project and report issues to us.