gemma configs

config_hub/finetune/README.md

@@ -11,9 +11,12 @@ For more information, see the [Dealing with out-of-memory (OOM) errors](../../tu
 | falcon-7b/lora.yaml               | 7B   | Alpaca 2k | 4      | 0.945    | 16.69 GB    | 512            | 2                | bfloat16  | 24.88 min (1xA10G) |
 | falcon-7b/qlora.yaml              | 7B   | Alpaca 2k | 4      | 0.993    | 9.44 GB     | 512            | 2                | bfloat16  | 50.76 min (1xA10G) |
 |                                   |      |           |        |          |             |                |                  |           |                    |
-| gemma-2b/lora.yaml                | 2B   | Alpaca 2k | 3      | 1.476    | 12.62 GB    | 512            | 2                | bfloat16  | 18.31 min (1xA10G) |
-| gemma-2b/qlora.yaml               | 2B   | Alpaca 2k | 3      | 1.626    | 11.51 GB    | 512            | 2                | bfloat16  | 25.29 min (1xA10G) |
-| gemma-2b/full.yaml                | 2B   | Alpaca 2k | 0.35   | 1.046    | 18.47 GB    | 512            | 2                | bfloat16  | 16.79 min (2xA10G) |
+| gemma-2b/lora.yaml                | 2B   | Alpaca 2k | 2      | 1.476    | 12.62 GB    | 512            | 2                | bfloat16  |  9.29 min (1xA10G) |
+| gemma-2b/qlora.yaml               | 2B   | Alpaca 2k | 2      | 0.981    | 11.59 GB    | 512            | 2                | bfloat16  | 12.90 min (1xA10G) |
+| gemma-2b/full.yaml                | 2B   | Alpaca 2k | 0.35   | 0.990    | 17.43 GB    | 512            | 1                | bfloat16  | 13.61 min (4xA10G) |
+|                                   |      |           |        |          |             |                |                  |           |                    |
+| gemma-7b/lora.yaml                | 7B   | Alpaca 2k | 2      | 0.903    | 25.30 GB    | 512            | 1                | bfloat16  | 11.47 min (1xA100) |
+| gemma-7b/qlora.yaml               | 7B   | Alpaca 2k | 2      | 0.951    | 17.31 GB    | 512            | 1                | bfloat16  | 23.46 min (1xA100) |
 |                                   |      |           |        |          |             |                |                  |           |                    |
 | llama-2-7b/lora.yaml              | 7B   | Alpaca 2k | 4      | 0.802    | 19.77 GB    | 512            | 2                | bfloat16  | 32.75 min (A10G)   |
 | llama-2-7b/qlora.yaml             | 7B   | Alpaca 2k | 4      | 0.814    | 13.68 GB    | 512            | 2                | bfloat16  | 45.68 min (A10G)   |

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

@@ -9,7 +9,7 @@ out_dir: out/finetune/full-gemma-2b
 precision: bf16-true
 
 # How many devices/GPUs to use. (type: Union[int, str], default: 1)
-devices: 1
+devices: 4
 
 # Data-related arguments. If not provided, the default is ``litgpt.data.Alpaca``.
 data:
@@ -32,7 +32,7 @@ train:
   log_interval: 1
 
   # Number of samples between optimizer steps across data-parallel ranks (type: int, default: 128)
-  global_batch_size: 6
+  global_batch_size: 16
 
   # Number of samples per data-parallel rank (type: int, default: 4)
   micro_batch_size: 1
@@ -41,13 +41,13 @@ train:
   lr_warmup_steps: 100
 
   # Number of epochs to train on (type: Optional[int], default: 5)
-  epochs: 3
+  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:
+  max_steps: 50
 
   # Limits the length of samples. Off by default (type: Optional[int], default: null)
   max_seq_length: 512

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

@@ -15,7 +15,7 @@ quantize:
 devices: 1
 
 # The LoRA rank. (type: int, default: 8)
-lora_r: 16
+lora_r: 8
 
 # The LoRA alpha. (type: int, default: 16)
 lora_alpha: 16
@@ -71,7 +71,7 @@ train:
   lr_warmup_steps: 200
 
   # Number of epochs to train on (type: Optional[int], default: 5)
-  epochs: 4
+  epochs: 2
 
   # Total number of tokens to train on (type: Optional[int], default: null)
   max_tokens:

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

@@ -71,7 +71,7 @@ train:
   lr_warmup_steps: 200
 
   # Number of epochs to train on (type: Optional[int], default: 5)
-  epochs: 4
+  epochs: 2
 
   # Total number of tokens to train on (type: Optional[int], default: null)
   max_tokens:

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

@@ -0,0 +1,122 @@
+
+# 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/google/gemma-7b
+
+# Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/lora)
+out_dir: out/finetune/qlora-gemma-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:
+
+# How many devices/GPUs to use. (type: Union[int, str], default: 1)
+devices: 1
+
+# The LoRA rank. (type: int, default: 8)
+lora_r: 16
+
+# The LoRA alpha. (type: int, default: 16)
+lora_alpha: 16
+
+# The LoRA dropout value. (type: float, default: 0.05)
+lora_dropout: 0.1
+
+# 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: true
+
+# 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: true
+
+# Whether to apply LoRA to the weights of the MLP in the attention block. (type: bool, default: False)
+lora_mlp: true
+
+# Whether to apply LoRA to output head in GPT. (type: bool, default: False)
+lora_head: true
+
+# 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: 0.03847
+    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: 800
+
+  # 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: 6
+
+  # 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: 200
+
+  # 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: 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
+
+# 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/gemma-7b/qlora.yaml

@@ -0,0 +1,122 @@
+
+# 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/google/gemma-7b
+
+# Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/lora)
+out_dir: out/finetune/qlora-gemma-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: 16
+
+# The LoRA alpha. (type: int, default: 16)
+lora_alpha: 16
+
+# The LoRA dropout value. (type: float, default: 0.05)
+lora_dropout: 0.1
+
+# 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: true
+
+# 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: true
+
+# Whether to apply LoRA to the weights of the MLP in the attention block. (type: bool, default: False)
+lora_mlp: true
+
+# Whether to apply LoRA to output head in GPT. (type: bool, default: False)
+lora_head: true
+
+# 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: 0.03847
+    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: 800
+
+  # 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: 6
+
+  # 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: 200
+
+  # 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: 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
+
+# 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