Text Generation
Transformers
Safetensors
Chinese
neuronspark
snn
spiking-neural-network
neuromorphic
conversational
custom_code
Instructions to use Brain2nd/NeuronSpark-0.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Brain2nd/NeuronSpark-0.9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Brain2nd/NeuronSpark-0.9B", 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-0.9B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Brain2nd/NeuronSpark-0.9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Brain2nd/NeuronSpark-0.9B" # 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-0.9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Brain2nd/NeuronSpark-0.9B
- SGLang
How to use Brain2nd/NeuronSpark-0.9B 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-0.9B" \ --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-0.9B", "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-0.9B" \ --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-0.9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Brain2nd/NeuronSpark-0.9B with Docker Model Runner:
docker model run hf.co/Brain2nd/NeuronSpark-0.9B
File size: 931 Bytes
46977a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | """NeuronSpark 模型配置。"""
from transformers import PretrainedConfig
class NeuronSparkConfig(PretrainedConfig):
"""
SNN 隐状态空间语言模型配置。
Args:
vocab_size: 词表大小
D: 隐层维度
N: 状态扩展因子(每通道隐神经元数)
K: 每 token 最大 SNN 时间步(PonderNet 动态决定有效步数)
num_layers: SNN 解码层数
D_ff: FFN 中间层维度
v_th_min: 动态阈值下限
"""
model_type = "neuronspark"
def __init__(
self,
vocab_size=6144,
D=896,
N=8,
K=16,
num_layers=20,
D_ff=2688,
v_th_min=0.1,
**kwargs,
):
self.D = D
self.N = N
self.K = K
self.num_layers = num_layers
self.D_ff = D_ff
self.v_th_min = v_th_min
super().__init__(vocab_size=vocab_size, **kwargs)
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