OpenNMT-py v3.5.1

• Further fixes
• added wikitext runs

OpenNMT-py v3.5.0

3.5.0 (2024-02-22)

• Further improvements and fixes
• Suport for AWQ models
• Add n_best for topp/topk generation
• Support MoE (Mixtral) inference
• Extend HF models converter
• use flash_attn_with_kvcache for faster inference
• Add wikitext2 PPL computation
• Support for Phi-2 models

OpenNMT-py v3.4.3

• Further improvements to beam search and decoding
• New indexing "in bucket" for faster inference cf #2496
• Code cleanup
• Fix int8 for CPU dynamic quantization (still slow...)

OpenNMT-py v3.4.2

• torch 2.1 (scaled_dot_product improvements)
• Mistral 7B sliding window
• Speed-up inference
• flash attention 2 (with sliding window) >= v2.3.1
• use FusedRMSNorm from apex if available
• fixed attn_debug

OpenNMT-py v3.4.1

• bug fixes
• torch 2.x requirement (flash attention requires it)
• zero-out the prompt loss in LM finetuning
• batching sorted on src then tgt instead of max len
• six dependancy

OpenNMT-py v3.4.0

• bitsandbytes 4/8 bit quantization at inference
• MMLU-FR results and scoring
• flan-T5 support
• flash attention
• terminology transform
• tensor parallelism (inference, training)

OpenNMT-py v3.3.0

• Switch to pytorch 2.0.1
• Eval LLM with MMLU benchmark
• Fix Falcon 40B conversion / finetuning / inference
• Plugin encoder/decoder thanks @kleag / @n2oblife
• optional Safetensors for model storage (beta)
• finetuning config templates for supported LLMs

OpenNMT-py v3.2.0

Lots new stuff in this release:

• Skip init during model build (way faster building)
• Enable quantization of LoRA layers
• Enable 4bit quantization from bitsandbytes (NF4 / FP4)
• Enable "some" bnb.optim Optimizers for benchmarking purpose
• Refactor model state_dict loading to enable pseudo lazy loading with move on GPU as it loads
• Enable Gradient checkpointing for FFN, MHA, LoRA modules
• Make FFN bias optional (same as QKV): llama, mpt, redpajama, openllama converters changed accordingly.
  Convertv2_v3 set add_qkvbias=True, add_ffnbias=True.
  load_checkpoint: if w1_bias detected in checkpoint then add_ffnbias=True
• Add Multi Query attention
• Add Parallel Residual attention
• Add Falcon 7B converter

OpenNMT-py v3.1.3

• Step-by-step Tuto for Vicuna replication thanks Lina
• MosaicML MPT7B converter and support (Alibi embeddings)
• Open Llama converter / Redpajama converter
• Switch GCLD3 to Fasttext thanks ArtanieTheOne
• fix coverage attention in beam decoding
• fix ct2 keys for "Llama / MPT7B based" OpenNMT-y models

OpenNMT-py v3.1.2

• fixes: transforms (normalize, clean, inlinetags)

• Llama support (rotary embeddings, RMSNorm, Silu activation)

• 8bit loading for specific layers (along with LoRa for other layers)

• subword learner added to build_vocab