55 lines
2.3 KiB
Markdown
55 lines
2.3 KiB
Markdown
# Axolotl
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#### You know you're going to axolotl questions
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## Getting Started
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- Download some datasets.
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```shell
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curl https://raw.githubusercontent.com/tloen/alpaca-lora/main/alpaca_data_gpt4.json -o data/raw/alpaca_data_gpt4.json
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curl https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json -L -o data/raw/vicuna_cleaned.json
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curl https://github.com/teknium1/GPTeacher/blob/main/Instruct/gpt4-instruct-similarity-0.6-dataset.json?raw=true -L -o data/raw/gpt4-instruct-similarity-0.6-dataset.json
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curl https://github.com/teknium1/GPTeacher/blob/main/Roleplay/roleplay-similarity_0.6-instruct-dataset.json?raw=true -L -o data/raw/roleplay-similarity_0.6-instruct-dataset.json
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```
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- Convert the JSON data files to JSONL.
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```shell
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python3 ./scripts/alpaca_json_to_jsonl.py --input data/alpaca_data_gpt4.json > data/alpaca_data_gpt4.jsonl
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python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/vicuna_cleaned.json > data/vicuna_cleaned.jsonl
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python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/roleplay-similarity_0.6-instruct-dataset.json > data/roleplay-similarity_0.6-instruct-dataset.jsonl
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python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/gpt4-instruct-similarity-0.6-dataset.json > data/gpt4-instruct-similarity-0.6-dataset.jsonl
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```
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- Using JSONL makes it easier to subset the data if you want a smaller training set, i.e get 2000 random examples.
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```shell
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shuf -n2000 data/vicuna_cleaned.jsonl > data/vicuna_cleaned.subset0.jsonl
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```
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- Create a new or update the existing YAML config (config/pythia_1_2B_alpaca.yml)[config/pythia_1_2B_alpaca.yml]
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- Install python dependencies `pip3 install -e .[int4_triton]` or `pip3 install -e .[int4]`
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- If not using `int4` or `int4_triton`, run `pip install "peft @ git+https://github.com/huggingface/peft.git"`
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- Configure accelerate `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml`
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```yaml
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compute_environment: LOCAL_MACHINE
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distributed_type: MULTI_GPU
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downcast_bf16: 'no'
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gpu_ids: all
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machine_rank: 0
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main_training_function: main
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mixed_precision: bf16
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num_machines: 1
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num_processes: 4
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rdzv_backend: static
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same_network: true
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tpu_env: []
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tpu_use_cluster: false
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tpu_use_sudo: false
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use_cpu: false
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```
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- Train! `accelerate launch scripts/finetune.py`, make sure to choose the correct YAML config file
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