Brain2nd/NeuronSpark-V3-1.1B-Pretrain

NeuronSpark V3 — 1.1B Pretrain (in-progress, step 74000)

Architecture: SNN (Spiking Neural Network) decoder with PonderNet-style adaptive K time-step routing. Custom model, not a transformer variant.

• Hidden dim D = 1024 · K_max = 12 · 24 layers
• ~1.92B parameters (model.safetensors ≈ 2.47 GB bf16)
• Tokenizer vocab = 128387 (multilingual)
• Pretrain step 74000 / 380206 (~21% complete)
• Tokens seen: ~5.6B
• Optimizer: AdamW + DeepSpeed ZeRO-2

This is an in-training snapshot; not a finished model.

Load (inference / fine-tune)

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:

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