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

Libraries

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

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

pipe = pipeline("text-generation", model="Brain2nd/NeuronSpark-V3-1.1B-SFT", 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-SFT", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Brain2nd/NeuronSpark-V3-1.1B-SFT 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-SFT"
# 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-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use Brain2nd/NeuronSpark-V3-1.1B-SFT 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-SFT" \
    --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-SFT",
		"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-SFT" \
        --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-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

NeuronSpark-V3-1.1B-SFT / config.json

Brain2nd

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	{
	"D": 1024,
	"D_ff": 3072,
	"D_key": 128,
	"D_value": 128,
	"K": 12,
	"N": 8,
	"architectures": [
	"NeuronSparkForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_neuronspark.NeuronSparkConfig",
	"AutoModelForCausalLM": "modeling_neuronspark.NeuronSparkForCausalLM"
	},
	"bias_balancing_ema": 0.99,
	"bias_balancing_lr": 0.001,
	"bos_token_id": 1,
	"dtype": "bfloat16",
	"eos_token_id": 2,
	"eps_explore": 0.05,
	"k_predictor_hidden": 256,
	"memory_layer_interval": 4,
	"model_type": "neuronspark",
	"num_hidden_layers": 24,
	"num_layers": 24,
	"ponder_T_final": 0.3,
	"ponder_T_init": 2.0,
	"transformers_version": "5.6.2",
	"use_cache": false,
	"v_th_min": 0.02,
	"vocab_size": 128387
	}