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Training

Training is done on the processed files. The training configuration has 3 parts: model, training arguments and data.

model:
    _target_: ragfoundry.models.hf.HFTrain
    model_name_or_path: microsoft/Phi-3-mini-128k-instruct
    load_in_4bit: false
    load_in_8bit: true
    lora:
        bias: none
        fan_in_fan_out: false
        lora_alpha: 16
        lora_dropout: 0.1
        peft_type: LORA
        r: 16
        target_modules:
            - qkv_proj
        task_type: CAUSAL_LM
        use_rslora: true
    completion_start: <|assistant|>
    instruction_in_prompt:
    max_sequence_len: 4000

Model loading is done in the HFTrain class, which loads models from HuggingFace hub and uses PEFT adapters. Other classes can be implemented. The important keys here are: completion_start which indicates the beginning of the text where loss is to be calculated. This is model/tokenizer specific. Additionally, there is the instruction_in_prompt key, which if set to True, inserts the system instruction in the prompt, for models which do not support a dedicated system role.

Next is the training arguments:

train:
    output_dir: ./trained_models/
    bf16: false
    fp16: false
    gradient_accumulation_steps: 2
    group_by_length:
    learning_rate: 1e-4
    logging_steps: 10
    lr_scheduler_type: cosine
    max_steps: -1
    num_train_epochs: 1
    per_device_train_batch_size: 1
    optim: paged_adamw_8bit
    remove_unused_columns: true
    save_steps: 20000
    save_total_limit: 1
    warmup_ratio: 0.03
    weight_decay: 0.001
    report_to:

Training is done using the SFTTrainer in TRL. Training arguments are based on HuggingFace Trainer.

Finally, data and other options:

instruction: ragfoundry/processing/prompts/prompt_instructions/qa.txt
template:
data_file:
input_key: prompt
output_key:
resume_checkpoint:
limit:
shuffle:
hfhub_tag:
use_wandb:
experiment:
wandb_entity:

Here are they important keys:

  • The instruction file to use for training (should later be used for inference as well).
  • If the model/tokenizer do not support a chat template, the user needs to provided a custom template; they placeholders to fill are query and output.
  • Data file is the processed file to train on.
  • Input key is the prompt.
  • Output key is completion text to learn.
  • Limit and shuffle can be used to filter the dataset for debugging purposes.
  • The framework can push the trained model to hfhub_tab.
  • The last three keys related to experiment tracking using WANDB. Other services can be used by modifying the report_to key.

Sending Runs

As we mentioned in the Data Augmentation page, we demonstrate the framework functionality using the ASQA dataset and the Phi-3 model, experimenting with 5 different configurations. Only 2 configurations require fine-tuning. One can send the training job like this:

python training.py -cp configs/paper -cn training-asqa  \
       data_file=asqa-prefix-train.jsonl                \
       output_key=answers                               \
       train.output_dir=./trained_models_context/

The -cp and -cn are overrides for the default configuration, which is ./configs/training.yaml. Then there are overrides for the processed data file to use, the name of the label key and where to save the trained model. Overrides are based on the Hydra vocabulary.

For the CoT model with RAFT contexts, we run:

python training.py -cp configs/paper -cn training-asqa  \
       data_file=asqa-raft-cot-train.jsonl              \
       output_key=generated_answer                      \
       train.output_dir=./trained_models_cot/