Instructions to use electron-rare/spikingkiki-122b-a10b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electron-rare/spikingkiki-122b-a10b with Transformers:

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

pipe = pipeline("text-generation", model="electron-rare/spikingkiki-122b-a10b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("electron-rare/spikingkiki-122b-a10b")
model = AutoModelForCausalLM.from_pretrained("electron-rare/spikingkiki-122b-a10b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use electron-rare/spikingkiki-122b-a10b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electron-rare/spikingkiki-122b-a10b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electron-rare/spikingkiki-122b-a10b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/electron-rare/spikingkiki-122b-a10b

SGLang

How to use electron-rare/spikingkiki-122b-a10b 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 "electron-rare/spikingkiki-122b-a10b" \
    --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": "electron-rare/spikingkiki-122b-a10b",
		"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 "electron-rare/spikingkiki-122b-a10b" \
        --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": "electron-rare/spikingkiki-122b-a10b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use electron-rare/spikingkiki-122b-a10b with Docker Model Runner:
```
docker model run hf.co/electron-rare/spikingkiki-122b-a10b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SpikingKiKi 122B (A10B)

Spiking Neural Network (SNN) variant of Qwen3.5 MoE 122B with ~10B active parameters per token, using Leaky Integrate-and-Fire (LIF) neurons in selected layers. Research artifact from Hypneum Lab.

Base architecture: Qwen3.5 MoE (122B total, ~10B active)
Modification: LIF spiking neurons in attention/FFN routing layers
Metadata: see lif_metadata.json for per-layer LIF parameters
Status: research-stage; not benchmarked end-to-end
Shards: 28 safetensors files (full weights, ~244 GB)

Files

model-*.safetensors (28 shards) — full weights
config.json — Qwen3.5 MoE architecture config
lif_metadata.json — LIF layer parameters
chat_template.jinja — standard Qwen3.5 chat template

Research context

Extends the SpikingKiki line (see electron-rare/spikingkiki-27b) to MoE architecture, exploring whether sparse activation routing composes synergistically with spiking neurons for further compute reduction. Related to the GENIAL framework on hypneum-lab.

Org context

This repo is part of electron-rare, the legacy research org. Production ailiance artifacts (gateway, mascarade LoRAs) live at Ailiance-fr (post-2026-05-11 carve-out).

Downloads last month: 7

Safetensors

Model size

122B params

Tensor type

BF16

Model tree for electron-rare/spikingkiki-122b-a10b

Quantizations

2 models