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• RWKV v6 "Finch" (full feature support)
• RWKV v7 "Goose" (full feature support)
• RWKV v7 "Goose" + Mixture of LoRA Experts (SFT Support)
• ARWKV (MHLA + SwiGLU -> RWKV v7 TimeMix + SwiGLU) (SFT support)

• RLHF Training with ORPO(Odds Ratio Preference Optimization) v6,v7:

  ORPO is that it allows for simultaneous SFT and aligning. By adjusting the orpo_alpha value, you can control the ratio between SFT and aligning.

  • Uses odds ratios instead of probability ratios to measure policy changes

  • Can often achieve better performance with fewer training steps

  • Designed to mitigate common issues in RLHF like reward hacking and overoptimization

• RLHF Training with DPO(Direct Preference Optimization) v6,v7:

  Direct Preference Optimization (DPO) is an advanced training method for Large Language Models that aims to align model outputs with human preferences more effectively than traditional methods.

  • Directly optimizes for human preferences without the need for a separate reward model

  • Simplifies the training pipeline compared to other preference learning methods

  • More stable training process compared to RL-based methods

• Infinite Context Compression Distillation Training v6,v7:

  • This approach involves simultaneously training a student model using compressed logits data pre-acquired from a larger parameter model (for example, 14B) as soft labels, and the dataset as hard labels.

  • Soft label learning can significantly reduce the risk of overfitting.

  • It allows for efficient transfer of specific tasks to smaller models.

• Infinite Context Masked SFT with Smoothing Loss v6,v7,ARWKV:

  • By incorporating smoothing into the loss calculation, the transfer probability can be increased.

  • Dynamic masking allows for efficient loss reduction during multi-batch processing.

  • With an RTX4090, a 14B parameter model can be trained with 65k contexts.

• Bone (Block Affine Transformation):
  • New training method proposed by @Jl-er
  • Achieve faster convergence and better fit to data.
  • No complex initialization is required, and fast convergence and better fit to data can be achieved.
• DoRA (Weight-Decomposed Low-Rank Adaptation) NEW!:
  • DoRA decomposes the pre-trained weight into two components, magnitude and direction, for fine-tuning, specifically employing LoRA for directional updates to efficiently minimize the number of trainable parameters.
  • Same training speed compared LoRA
• LoRA:
  • You all already know this, right? :)
• Quantization:
  • FP8: Fastest. Native matmul.
  • FP6: by TorchAO Matmul slightly faster than bitsandbytes
  • Int8: 8-bit quantization with Bitsandbytes. 16-bit Matmul
  • NF4: 4-bit quantization with Bitsandbytes. 16-bit Matmul
  • What is next?

Rank can be set variably for each layer. see layer_profile

• CPU RAM >= 32GB (128GB Recommend for stress free)
• Cuda or Rocm GPU.(NVIDIA RTX3090,4090, AMD MI100)
• CUDA 12.4+, Rocm 6.1.2+
• Python 3.12+
• Pytorch 2.5+
• Bitsandbytes (in MI100 with Rocm6.1.3, need build)
• some case need (conda install libstdcxx -c conda-forge --override-channels) for building cuda kernel

• Odds Ratio Preference Optimization document
• Direct Preference Optimization document
• Distillation document
• SFT document
• Layer Profile

• 1. Iterative DPO, ORPO -> Suspend.
• 2. Reinforcement++ -> working :)

• 1. bnb 8bit optimizer. done
• 2. ToachAO's FP6 Matmul testing

• 1. Realtime Training(with Reverse State Distillation)

• RWKV-LM @BlinkDL
• RWKV-LM-RLHF-DPO @Triang-jyed-driung
• RWKV-PEFT @Jl-er
• Flash-Linear-Attention @fla-org
• SmerkyG
• Orpo @xfactlab
• All RWKV Community Members :)

same with RWKV-LM

Apache 2.0

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