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Text Generation Intel® Transfer Learning Tool CLI Example

Fine Tuning Using Your Own Dataset

The example below shows how to fine tune a Pytorch text generation model using your own dataset in the .JSON format.

The --dataset-dir argument is the path to the directory where your dataset is located, and the --dataset-file is the name of the .JSON file to load from that directory. The instruction-key, --context-key, and response-key are the keys in the JSON file that make up the dataset schema.

This example is downloading a subset of the Code Alpaca dataset, where each record of the dataset contains text fields for "instruction", "input", and "output". For this dataset, "instruction", "input", and "output" would map to instruction-key,--context-key, and response-key, respectively.

# Create dataset and output directories
export DATASET_DIR=/tmp/data
export OUTPUT_DIR=/tmp/output
mkdir -p ${DATASET_DIR}
mkdir -p ${OUTPUT_DIR}

# Download and extract the dataset
wget -P ${DATASET_DIR} https://raw.githubusercontent.com/sahil280114/codealpaca/master/data/code_alpaca_2k.json

# Train distilgpt2 using our dataset file
tlt train \
    --framework pytorch \
    --dataset_dir ${DATASET_DIR} \
    --output_dir ${OUTPUT_DIR} \
    --epochs 3 \
    --dataset-file code_alpaca_2k.json \
    --model-name distilgpt2 \
    --instruction-key instruction \
    --context-key input \
    --response-key output \
    --prompt-with-context "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request." \
    --prompt-without-context "Below is an instruction that describes a task. write a response that appropriately completes the request."

# Evaluate the model exported after training
# Note that your --model-dir path may vary, since each training run creates a new directory
tlt eval \
    --dataset_dir ${DATASET_DIR} \
    --dataset-file code_alpaca_2k.json \
    --model-dir ${OUTPUT_DIR}/distilgpt2/1 \
    --prompt-with-context "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request." \
    --prompt-without-context "Below is an instruction that describes a task. write a response that appropriately completes the request."

# Generate text on our trained model
tlt generate \
    --model-dir ${OUTPUT_DIR}/distilgpt2/1 \
    --prompt "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a function that sorts the following list.\n\n### Context:\n[3, 2, 1]\n\n### Response:\n" \
    --repetition-penalty 6.0

Citation

@misc{codealpaca,
  author = {Sahil Chaudhary},
  title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/sahil280114/codealpaca}},
}