Instructions to use Brain2nd/NeuronSpark-0.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

NeuronSpark-0.9B / tokenizer_config.json

Brain2nd

Initial release: NeuronSpark-0.9B pretrained SNN language model

46977a8 verified 2 months ago

raw

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791 Bytes

	{
	"add_bos_token": false,
	"add_eos_token": false,
	"add_prefix_space": false,
	"bos_token": "<\|im_start\|>",
	"eos_token": "<\|im_end\|>",
	"pad_token": "<\|im_end\|>",
	"unk_token": "<unk>",
	"model_max_length": 1000000000000000019884624838656,
	"clean_up_tokenization_spaces": false,
	"tokenizer_class": "PreTrainedTokenizerFast",
	"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}<\|im_start\|>system\n{{ message['content'] }}<\|im_end\|>\n{% elif message['role'] == 'user' %}<\|im_start\|>user\n{{ message['content'] }}<\|im_end\|>\n{% elif message['role'] == 'assistant' %}<\|im_start\|>assistant\n{{ message['content'] }}<\|im_end\|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}"
	}