Spaces:
Runtime error
Runtime error
File size: 2,922 Bytes
af04de4 e75ffde af04de4 e75ffde af04de4 b8758c8 e75ffde af04de4 611507d af04de4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
from typing import List, Optional
MODEL_SELECTION_ID: str = "model_selection"
MODEL_VERSION_SELECTION_ID: str = "model_version_selection"
LOAD_IN_4_BIT_ID: str = "load_in_4bit"
BNB_4BIT_QUANT_TYPE: str = "bnb_4bit_quant_type"
BNB_4BIT_COMPUTE_DTYPE: str = "bnb_4bit_compute_dtype"
BNB_4BIT_USE_DOUBLE_QUANT: str = "bnb_4bit_use_double_quant"
DATASET_SELECTION_ID = "dataset_selection"
DATASET_SHUFFLING_SEED = "dataset_seed"
FLASH_ATTENTION_ID = "flash_attention"
PAD_SIDE_ID = "pad_side"
PAD_VALUE_ID = "pad_value"
LORA_R_ID = "lora_r"
LORA_ALPHA_ID = "lora_alpha"
LORA_DROPOUT_ID = "lora_dropout"
LORA_BIAS_ID = 'lora_bias'
NUM_TRAIN_EPOCHS_ID = "num_train_epochs"
MAX_STEPS_ID = "max_steps_id"
LOGGING_STEPS_ID = "logging_steps"
PER_DEVICE_TRAIN_BATCH_SIZE = "per_device_train_batch_size"
SAVE_STRATEGY_ID = "save_strategy"
GRADIENT_ACCUMULATION_STEPS_ID = "gradient_accumulation_steps"
GRADIENT_CHECKPOINTING_ID = "gradient_checkpointing"
LEARNING_RATE_ID = "learning_rate"
MAX_GRAD_NORM_ID = "max_grad_norm"
WARMUP_RATIO_ID = "warmup_ratio"
LR_SCHEDULER_TYPE_ID = "lr_scheduler_type"
OUTPUT_DIR_ID = "output_dir"
PUSH_TO_HUB_ID = "push_to_hub"
REPOSITORY_NAME_ID = "repo_id"
REPORT_TO_ID = "report_to"
README_ID = "readme"
MAX_SEQ_LENGTH_ID = "max_seq_length"
PACKING_ID = "packing"
OPTIMIZER_ID = "optim"
BETA1_ID = "adam_beta1"
BETA2_ID = "adam_beta2"
EPSILON_ID = "adam_epsilon"
WEIGHT_DECAY_ID = "weight_decay"
class FTDataSet:
def __init__(self, path: str, dataset_split: Optional[str] = None):
self.path = path
self.dataset_split = dataset_split
def __str__(self):
return self.path
deita_dataset = FTDataSet(path="HuggingFaceH4/deita-10k-v0-sft", dataset_split="train_sft")
dolly = FTDataSet(path="philschmid/dolly-15k-oai-style", dataset_split="train")
ultrachat_200k = FTDataSet(path="HuggingFaceH4/ultrachat_200k", dataset_split="train_sft")
ft_datasets = [deita_dataset, dolly, ultrachat_200k]
class Model:
def __init__(self, name: str, versions: List[str]):
self.name = name
self.versions = versions
def __str__(self):
return self.name
models: List[Model] = []
gemma = Model(name="google/gemma", versions=["7b", "2b"])
models.append(gemma)
falcon = Model(name="tiiuae/falcon", versions=["7b"]) # "7b-instruct"
models.append(falcon)
phi = Model(name="microsoft/phi", versions=["1_5", "1", "2"])
models.append(phi)
llama = Model(name="meta-llama/Llama-2", versions=["7b", "7b-hf"]) # "7b-chat", "7b-chat-hf"
models.append(llama)
mistral = Model(name="mistralai/Mistral", versions=["7B-v0.1"]) # "7B-Instruct-v0.1"
models.append(mistral)
tinyLlama = Model(name="TinyLlama/TinyLlama-1.1B",
versions=['intermediate-step-1431k-3T', 'step-50K-105b', 'intermediate-step-240k-503b',
'intermediate-step-715k-1.5T', 'intermediate-step-1195k-token-2.5T'])
models.append(tinyLlama)
|