Instructions to use Brain2nd/NeuronSpark-V3-1.1B-Pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Brain2nd/NeuronSpark-V3-1.1B-Pretrain with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Brain2nd/NeuronSpark-V3-1.1B-Pretrain", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Brain2nd/NeuronSpark-V3-1.1B-Pretrain", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Brain2nd/NeuronSpark-V3-1.1B-Pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Brain2nd/NeuronSpark-V3-1.1B-Pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Brain2nd/NeuronSpark-V3-1.1B-Pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Brain2nd/NeuronSpark-V3-1.1B-Pretrain

SGLang

How to use Brain2nd/NeuronSpark-V3-1.1B-Pretrain with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Brain2nd/NeuronSpark-V3-1.1B-Pretrain" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Brain2nd/NeuronSpark-V3-1.1B-Pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Brain2nd/NeuronSpark-V3-1.1B-Pretrain" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Brain2nd/NeuronSpark-V3-1.1B-Pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Brain2nd/NeuronSpark-V3-1.1B-Pretrain with Docker Model Runner:
```
docker model run hf.co/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

Downloads last month: 11

Safetensors

Model size

1B params

Tensor type

F32

BF16