Merge branch 'main' into carmocca/revert-stuff

.gitignore

@@ -16,5 +16,3 @@ checkpoints
 out
 wandb
 events.out.tfevents*
-
-tests/reference_models

config_hub/finetune/falcon-7b/lora.yaml

@@ -114,6 +114,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/falcon-7b/qlora.yaml

@@ -116,6 +116,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/gemma-2b/full.yaml

@@ -85,6 +85,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/gemma-2b/lora.yaml

@@ -115,6 +115,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/gemma-2b/qlora.yaml

@@ -115,6 +115,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/gemma-7b/lora.yaml

@@ -115,6 +115,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/gemma-7b/qlora.yaml

@@ -115,6 +115,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/llama-2-7b/full.yaml

@@ -88,6 +88,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/llama-2-7b/lora.yaml

@@ -114,6 +114,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/llama-2-7b/qlora.yaml

@@ -116,6 +116,9 @@ eval:
   # Number of iterations (type: int, default: 100)
   max_iters: 100
 
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
 # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv)
 logger_name: csv
 

config_hub/finetune/llama-3-8b/full.yaml

@@ -0,0 +1,98 @@
+
+# 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/Meta-Llama-3-8B
+
+# Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/finetune/full)
+out_dir: out/finetune/full-llama-3-8b
+
+# The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)
+precision: bf16-true
+
+# How many devices/GPUs to use (type: Union[int, str], default: 1)
+devices: 4
+
+# Path to a checkpoint directory to resume from in case training was interrupted, or ``True`` to resume
+# from the latest checkpoint in ``out_dir``. (type: Union[bool, Path], default: False)
+resume: false
+
+# Data-related arguments. If not provided, the default is ``litgpt.data.Alpaca``.
+data:
+  class_path: litgpt.data.Alpaca2k
+  init_args:
+    mask_prompt: false
+    prompt_style: alpaca
+    ignore_index: -100
+    seed: 42
+    num_workers: 4
+
+# Training-related arguments. See ``litgpt.args.TrainArgs`` for details
+train:
+
+  # 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: 64)
+  global_batch_size: 64
+
+  # Number of samples per data-parallel rank (type: int, default: 1)
+  micro_batch_size: 4
+
+  # Number of iterations with learning rate warmup active (type: int, default: 100)
+  lr_warmup_steps: 25
+
+  # Number of epochs to train on (type: Optional[int], default: 5)
+  epochs: 1
+
+  # 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. Off by default (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: 0.003)
+  learning_rate: 0.0002
+
+  #   (type: float, default: 0.02)
+  weight_decay: 0.1
+
+  #   (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 details
+eval:
+
+  # Number of optimizer steps between evaluation calls (type: int, default: 600)
+  interval: 25
+
+  # Number of tokens to generate (type: Optional[int], default: 100)
+  max_new_tokens: 100
+
+  # Number of iterations (type: int, default: 100)
+  max_iters: 100
+
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
+# 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

config_hub/finetune/llama-3-8b/lora.yaml

@@ -0,0 +1,124 @@
+
+# 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/Meta-Llama-3-8B
+
+# Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/lora)
+out_dir: out/finetune/lora-llama-3-8b
+
+# 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:
+
+# How many devices/GPUs to use. (type: Union[int, str], default: 1)
+devices: 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.Alpaca2k
+  init_args:
+    mask_prompt: false
+    prompt_style: alpaca
+    ignore_index: -100
+    seed: 42
+    num_workers: 4
+
+# Training-related arguments. See ``litgpt.args.TrainArgs`` for details
+train:
+
+  # 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: 1
+
+  # 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: 2
+
+  # 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. Off by default (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 details
+eval:
+
+  # 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
+
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
+# 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