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{
"pipe_parallel_size": 0,
"model_parallel_size": 8,
"make_vocab_size_divisible_by": 1,
# model settings
"num_layers": 48,
"hidden_size": 8192,
"num_attention_heads": 64,
"attention_type": "groupedquery",
"num_kv_heads": 8,
# NB: These rotary embedding and sequence length parameters
# May differ from CodeLlama configs. They match what we used for
# Llemma continued pretraining. See https://arxiv.org/abs/2310.10631
# For detailed discussion
"seq_length": 4096,
"max_position_embeddings": 4096,
"pos_emb": "rotary",
"rotary_pct": 1,
"rotary_emb_base": 1000000,
"no_weight_tying": true,
"gpt_j_residual": false,
"output_layer_parallelism": "column",
"norm": "rmsnorm",
"rms_norm_epsilon": 1.0e-5,
"attention_config": [[["flash"], 48]],
"scaled_upper_triang_masked_softmax_fusion": true,
"bias_gelu_fusion": false,
"use_bias_in_norms": false,
"use_bias_in_attn_linear": false,
"activation": "swiglu",
"mlp_multiple_of": 256,
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.00005,
"betas": [0.9, 0.95],
"eps": 1.0e-8
}
},
"zero_optimization": {
"stage": 1,
"allgather_partitions": true,
"allgather_bucket_size": 1260000000,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1260000000,
"contiguous_gradients": true,
"cpu_offload": false
},
# trained on 256 gpus
"train_micro_batch_size_per_gpu": 2,
"gradient_accumulation_steps": 16,
"data_impl": "mmap",
"checkpoint_activations": true,
"checkpoint_num_layers": 1,
"partition_activations": true,
"synchronize_each_layer": true,
"gradient_clipping": 1.0,
"weight_decay": 0.1,
"hidden_dropout": 0,
"attention_dropout": 0,
"precision": "bfloat16",
"fp32_allreduce": true,
"bf16": {
"enabled": true
},
"data_types": {
"grad_accum_dtype": "fp32"
},
"train_iters": 12000,
"lr_decay_iters": 12000,
"distributed_backend": "nccl",
"lr_decay_style": "cosine",
"min_lr": 1.65e-6,
"warmup": 0.042, # warmup for ~500 iters
"checkpoint_factor": 250,
"eval_interval": 250,
"eval_iters": 25,
"log_interval": 1,
"steps_per_print": 1,
"wall_clock_breakdown": true,
"tokenizer_type": "SPMTokenizer",
#"vocab-file": # use 'tokenizer.model' from Meta CodeLlama download
# "load": "" # set to same as "save" to resume from intermediate finetuning step
#"load": MP=8 CodeLlama-34B checkpoint, converted from Meta CodeLlama download.
# When resuming from mid-finetuning run, change "load" to the same as save location.
"finetune": true, # set to false once resuming from intermediate finetuning step
}
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