Datasets:

HuggingFaceFW

/

fineweb

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We are currently waiting for some changes on the nanotron codebase to be completed, but here are the main arguments (not sure if it would run on the current state of nanotron):

model_config = LlamaConfig(
    bos_token_id=1,
    eos_token_id=2,
    hidden_act="silu",
    hidden_size=2048,
    initializer_range=0.02,
    intermediate_size=8192,
    max_position_embeddings=2048,
    num_attention_heads=32,
    num_hidden_layers=24,
    num_key_value_heads=32,
    pretraining_tp=1,
    rms_norm_eps=1e-05,
    rope_scaling=None,
    tie_word_embeddings=True,
    use_cache=True,
    vocab_size=50272,  # GPT2 tokenizer rounded to next multiple of 8
)

    parallelism = ParallelismArgs(
        dp=64,
        pp=1,
        tp=1,
        pp_engine="1f1b",
        tp_mode="REDUCE_SCATTER",
        tp_linear_async_communication=True,
    )

    tokens = TokensArgs(
        batch_accumulation_per_replica=4,
        micro_batch_size=4,
        sequence_length=2048,
        train_steps=args.train_steps,
        val_check_interval=100,
    )

    model = ModelArgs(
        model_config=model_config,
        make_vocab_size_divisible_by=1,
        init_method=RandomInit(
            std=0.02,
            # std=1
            # / math.sqrt(model_config.hidden_size)  # 0.01275  # Basically 1/sqrt(N),
            # path="/fsx/shared-falcon-180B/brrr-falcon-180B"
        ),
        dtype=torch.bfloat16,
    )
    optimizer = OptimizerArgs(
        accumulate_grad_in_fp32=True,
        adam_beta1=0.9,
        adam_beta2=0.95,
        adam_eps=1.0e-8,
        clip_grad=1.0,
        torch_adam_is_fused=True,
        weight_decay=0.1,
        zero_stage=0,
        learning_rate_scheduler=LRSchedulerArgs(
            learning_rate=3e-4,
            lr_warmup_steps=500,
            lr_warmup_style="linear",
            lr_decay_style="cosine",
            # lr_decay_steps=10000-500,  # Keeping it to 10k for comparision for now
            min_decay_lr=3.0e-5
        )
    )