from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Brain2nd/NeuronSpark-V3-1.1B-Pretrain",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "Brain2nd/NeuronSpark-V3-1.1B-Pretrain"
)

Resume pretraining

The deepspeed/ directory contains 8-rank ZeRO-2 sharded optimizer state. Use with DeepSpeed launcher to continue training from this exact point:

bash

git lfs install
git clone https://huggingface.co/Brain2nd/NeuronSpark-V3-1.1B-Pretrain ./ckpt
deepspeed --num_gpus=8 train_pretrain.py \
  --deepspeed_config ds_config.json \
  --resume ./ckpt

Files


`model.safetensors`	HF-format bf16 weights
`config.json` / `generation_config.json`	model config
`tokenizer.json` / `tokenizer_config.json`	tokenizer
`modeling_neuronspark.py` / `configuration_neuronspark.py` / `__init__.py`	custom architecture (`trust_remote_code=True`)
`deepspeed/`	ZeRO-2 optimizer state (8 ranks, fp32 master + Adam moments)
`training_state.pth`	step / tokens_seen / epoch metadata
`zero_to_fp32.py`	DeepSpeed helper to consolidate sharded state