git clone https://github.com/Lightning-AI/litgptcd litgptpip install -e'.[all]'

All models

# 3) Use the server (in a separate Python session)importrequests,jsonresponse=requests.post("http://127.0.0.1:8000/predict",json={"prompt":"Fix typos in the following sentence: Example input"})print(response.json()["output"])

# 1) List all supported LLMslitgpt download list# 2) Use a model (auto downloads weights)litgpt chat microsoft/phi-2>> Prompt: Whatdo Llamas eat?

mkdir -p custom_textscurl https://www.gutenberg.org/cache/epub/24440/pg24440.txt --output custom_texts/book1.txtcurl https://www.gutenberg.org/cache/epub/26393/pg26393.txt --output custom_texts/book2.txt# 1) Download a tokenizerlitgpt download EleutherAI/pythia-160m \  --tokenizer_only True# 2) Pretrain the modellitgpt pretrain EleutherAI/pythia-160m \  --tokenizer_dir EleutherAI/pythia-160m \  --data TextFiles \  --data.train_data_path"custom_texts/" \  --train.max_tokens 10_000_000 \  --out_dir out/custom-model# 3) Test the modellitgpt chat out/custom-model/final

mkdir -p custom_textscurl https://www.gutenberg.org/cache/epub/24440/pg24440.txt --output custom_texts/book1.txtcurl https://www.gutenberg.org/cache/epub/26393/pg26393.txt --output custom_texts/book2.txt# 1) Continue pretraining a model (auto downloads weights)litgpt pretrain EleutherAI/pythia-160m \  --tokenizer_dir EleutherAI/pythia-160m \  --initial_checkpoint_dir EleutherAI/pythia-160m \  --data TextFiles \  --data.train_data_path"custom_texts/" \  --train.max_tokens 10_000_000 \  --out_dir out/custom-model# 2) Test the modellitgpt chat out/custom-model/final

# The path to the base model's checkpoint directory to load for finetuning. (type: <class 'Path'>, default: checkpoints/stabilityai/stablelm-base-alpha-3b)checkpoint_dir:checkpoints/meta-llama/Llama-2-7b-hf# Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/lora)out_dir:out/finetune/qlora-llama2-7b# The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)precision:bf16-true...

# The path to the base model's checkpoint directory to load for finetuning. (type: <class 'Path'>, default: checkpoints/stabilityai/stablelm-base-alpha-3b)checkpoint_dir:checkpoints/meta-llama/Llama-2-7b-hf# Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/lora)out_dir:out/finetune/qlora-llama2-7b# The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)precision:bf16-true# If set, quantize the model with this algorithm. See ``tutorials/quantize.md`` for more information. (type: Optional[Literal['nf4', 'nf4-dq', 'fp4', 'fp4-dq', 'int8-training']], default: null)quantize:bnb.nf4# How many devices/GPUs to use. (type: Union[int, str], default: 1)devices:1# How many nodes to use. (type: int, default: 1)num_nodes:1# The LoRA rank. (type: int, default: 8)lora_r:32# The LoRA alpha. (type: int, default: 16)lora_alpha:16# The LoRA dropout value. (type: float, default: 0.05)lora_dropout:0.05# Whether to apply LoRA to the query weights in attention. (type: bool, default: True)lora_query:true# Whether to apply LoRA to the key weights in attention. (type: bool, default: False)lora_key:false# Whether to apply LoRA to the value weights in attention. (type: bool, default: True)lora_value:true# Whether to apply LoRA to the output projection in the attention block. (type: bool, default: False)lora_projection:false# Whether to apply LoRA to the weights of the MLP in the attention block. (type: bool, default: False)lora_mlp:false# Whether to apply LoRA to output head in GPT. (type: bool, default: False)lora_head:false# Data-related arguments. If not provided, the default is ``litgpt.data.Alpaca``.data:class_path:litgpt.data.Alpaca2kinit_args:mask_prompt:falseval_split_fraction:0.05prompt_style:alpacaignore_index:-100seed:42num_workers:4download_dir:data/alpaca2k# Training-related arguments. See ``litgpt.args.TrainArgs`` for detailstrain:# Number of optimizer steps between saving checkpoints (type: Optional[int], default: 1000)save_interval:200# Number of iterations between logging calls (type: int, default: 1)log_interval:1# Number of samples between optimizer steps across data-parallel ranks (type: int, default: 128)global_batch_size:8# Number of samples per data-parallel rank (type: int, default: 4)micro_batch_size:2# Number of iterations with learning rate warmup active (type: int, default: 100)lr_warmup_steps:10# Number of epochs to train on (type: Optional[int], default: 5)epochs:4# Total number of tokens to train on (type: Optional[int], default: null)max_tokens:# Limits the number of optimizer steps to run (type: Optional[int], default: null)max_steps:# Limits the length of samples (type: Optional[int], default: null)max_seq_length:512# Whether to tie the embedding weights with the language modeling head weights (type: Optional[bool], default: null)tie_embeddings:#   (type: float, default: 0.0003)learning_rate:0.0002#   (type: float, default: 0.02)weight_decay:0.0#   (type: float, default: 0.9)beta1:0.9#   (type: float, default: 0.95)beta2:0.95#   (type: Optional[float], default: null)max_norm:#   (type: float, default: 6e-05)min_lr:6.0e-05# Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for detailseval:# Number of optimizer steps between evaluation calls (type: int, default: 100)interval:100# Number of tokens to generate (type: Optional[int], default: 100)max_new_tokens:100# Number of iterations (type: int, default: 100)max_iters:100# The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)logger_name:csv# The random seed to use for reproducibility. (type: int, default: 1337)seed:1337

litgpt finetune \  --config https://raw.githubusercontent.com/Lightning-AI/litgpt/main/config_hub/finetune/llama-2-7b/lora.yaml \  --lora_r 4