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base_model: mistralai/Mistral-7B-v0.1
# optionally might have model_type or tokenizer_type
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
hub_model_id: AiAF/UFOs-Pretraining-V1.0
load_in_8bit: false
load_in_4bit: false
strict: false
pretraining_dataset:
- name:
path: AiAF/pretraining.jsonl
split: "train"
text_column: "text" # column in dataset with the data, usually `text`
type: pretrain
trust_remote_code: true
skip: 0 # number of rows of data to skip over from the beginning
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs
max_steps: 100000
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: "UFO_LLM_Pretraining"
wandb_entity:
wandb_watch: "all"
wandb_name: "UFO_LLM_Pretraining-V1.0"
wandb_log_model: "false"
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
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