Update Llama 2 7b configs (#1101)

Author: awaelchli

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

@@ -17,10 +17,9 @@ resume: false
 
 # Data-related arguments. If not provided, the default is ``litgpt.data.Alpaca``.
 data:
-  class_path: litgpt.data.AlpacaGPT4
+  class_path: litgpt.data.Alpaca2k
   init_args:
     mask_prompt: false
-    val_split_fraction: 0.03847
     prompt_style: alpaca
     ignore_index: -100
     seed: 42
@@ -45,7 +44,7 @@ train:
   lr_warmup_steps: 25
 
   # Number of epochs to train on (type: Optional[int], default: 5)
-  epochs: 1
+  epochs: 4
 
   # Total number of tokens to train on (type: Optional[int], default: null)
   max_tokens:

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

@@ -43,10 +43,9 @@ lora_head: false
 
 # Data-related arguments. If not provided, the default is ``litgpt.data.Alpaca``.
 data:
-  class_path: litgpt.data.AlpacaGPT4
+  class_path: litgpt.data.Alpaca2k
   init_args:
     mask_prompt: false
-    val_split_fraction: 0.03847
     prompt_style: alpaca
     ignore_index: -100
     seed: 42
@@ -71,7 +70,7 @@ train:
   lr_warmup_steps: 10
 
   # Number of epochs to train on (type: Optional[int], default: 5)
-  epochs: 1
+  epochs: 4
 
   # Total number of tokens to train on (type: Optional[int], default: null)
   max_tokens:

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

@@ -0,0 +1,123 @@
+
+# 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
+
+# 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
+    val_split_fraction: 0.05
+    prompt_style: alpaca
+    ignore_index: -100
+    seed: 42
+    num_workers: 4
+    download_dir: data/alpaca2k
+
+# 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: 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 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
+
+# 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