This project is to extend RWKV LM's capabilities including sequence classification/embedding/peft/cross encoder/bi encoder/multi modalities, etc.

We extends two types of model based on RWKV(5,6)。

• RwkvForClassification

This class is used to do sequence classification.

graph LR
    A(idx) --> B[embeddings]
    B --> C[Apply rwkv blocks]
    C --> D[Found the eos id's embeddings]
    D --> E[Score the embeddings]
    E --> F(Return scores)

• RwkvForSequenceEmbedding

This class is used to do sequence embedding.

graph LR
    A(idx) --> B[embeddings]
    B --> C[Apply rwkv blocks]
    C --> D[Apply pooling method]
    D --> E(Return embeddings)

• AnswerBot SFT:

  • 链接: https://pan.baidu.com/s/1f61FwhinoGqWg-nlAWPnPw?pwd=b4ff 提取码: b4ff --来自百度网盘超级会员v6的分享

  This is finetuned by RWKV's lora.

  • Huggingface: https://huggingface.co/yueyulin/rwkv_peft_sft_lora

• AnswerBot SFT-Pissa Rank64

  • This is a pissa fine tuned with rank 64: https://huggingface.co/yueyulin/tigerbot_chat_sft_pissa_r64

• AnswerBot PISSA SFT:

  • 链接: https://pan.baidu.com/s/1T_bv_aF_6f7lVJTJeRB3Eg?pwd=6b6c 提取码: 6b6c --来自百度网盘超级会员v6的分享

  • Huggingface: https://huggingface.co/yueyulin/rwkv_peft_pissa_tigerbot

• BiEncoder:

  • 链接: https://pan.baidu.com/s/1f61FwhinoGqWg-nlAWPnPw?pwd=b4ff 提取码: b4ff --来自百度网盘超级会员v6的分享
  • Huggingface:https://huggingface.co/yueyulin/rwkv_sft_bi_encoder/

  There are two files in epoch_1_step_430000:

  • RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth.pth is trained and saved by huggingface peft injection model.
  • RWKV-x060-World-1B6-v2_rwkv_lora.pth is converted by peft_train/hf2rwkv_lora.py utility.

• Cross Encoder

  • Huggingface: https://huggingface.co/yueyulin/rwkv_cross_encoder

Try python peft_train/peft_test.py to see how both 2 sft work seemlessly. In the future more sft parameters can work together to build a more sophiscated AI assistant.

Further more some utilities:

• peft_train/hf2rwkv_lora.py convert the lora check point trained by huggingface peft to rwkv lora format. This utility can be used to TURN BiEncoder checkpoint.

• peft_train/peft_test.py is a better example to show how to use one base model to do variable stuffs in runtime by switching lora adapters only.

  . if you're using PISSA checkpoint, please set --chat_lora_alpha 8 instead --chat_lora_alpha 32 in Lora.

• Download the base model from: https://huggingface.co/BlinkDL/rwkv-6-world , here we download the RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth model, save it to dir MODEL_DIR
• Download datasets from : https://huggingface.co/datasets/yueyulin/answering_bot , save them to JSON_DIR
• Preprocess the json files to variable sized datasets. The tokenizer file is in tokenizer/directory, please adjust the parameters according your real situation.

python data/SftUtilities.py --input_dir JSON_DIR --output_dir DATASET_DIR --tokenizer_file PATH_TO_rwkv_vocab_v20230424.txt

• We got datasets in DATASET_DIR, now we can train our SFT model. The following's are the script we use to train lora/pissa/state tuning. Let's assume we output our SFT peft model to OUTPUT_DIR. For a 4090ti, we can train the data with batch 32 16 8 4 2 1 corresponding to 64 128 256 512 1024 2048 max length.

  • Lora

  RWKV_TRAIN_TYPE=lora python peft_train/peft_train_sft.py --train_data /home/rwkv/data/tigerbot_sft_dataset --model_file MODEL_DIR/RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth --output_dir OUTPUT_DIR/lora_rwkv --train_type lora --log_every_n_steps 100000 --target_modules att ffn --log_every_n_steps 100000 --wandb lora_tigerbots --num_epochs 3 --train_lengths 64 128 256 512 1024 2048 --train_batch_sizes 32 16 8 4 2 1

  • Pissa

  RWKV_TRAIN_TYPE=pissa python peft_train/peft_train_sft.py --train_data /home/rwkv/data/tigerbot_sft_dataset --model_file MODEL_DIR/RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth --output_dir OUTPUT_DIR/pissa_rwkv --train_type pissa --log_every_n_steps 100000 --target_modules att ffn --log_every_n_steps 100000 --wandb pissa_tigerbots --num_epochs 3 --train_lengths 64 128 256 512 1024 2048 --train_batch_sizes 32 16 8 4 2 1
  

Now user can use beam search to get variable results with beam search. Try src/tests/TestBeamSearch.py.

Please refer infer/encoders to utilize the pure RWKV's mulitple lora adapters.