rmems/Spikenaut-SNN-v1
Updated
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 194, in _generate_tables
json_field_paths += find_mixed_struct_types_field_paths(examples)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 58, in find_mixed_struct_types_field_paths
examples = [x[subfield] for x in content if x[subfield] is not None]
~^^^^^^^^^^
KeyError: 'neuron_id'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
👁️ SpikeLens: The Neuromorphic Aperture
👨🚀 Spikenaut-v1: The First Rust-Based SNN Pilot
Making the Invisible Visible: A Silicon Tribute to Neuroplasticity
SpikeLens is a high-precision neuromorphic framework built from the ground up in Rust. It acts as a digital aperture—capturing the raw, spiking pulses of high-performance hardware and focusing them into a clear, actionable signal.
Spikenaut-v1 is the flagship model: the navigator that steers the "Ship of Theseus" through the stochastic storms of real-time telemetry.
🌊 The Mission: The Refracted Mind
This project is born from a personal journey of recovery following a severe 2013 concussion. After the world became noisy and data became invisible, I built SpikeLens to act as a corrective layer. It is the eye that never blinks, powered by a Spikenaut pilot that perceives the machine from within.
🚀 Key Features
- Event-Driven Efficiency: Unlike standard ANNs, Sovereign-SNN only computes upon "spikes," drastically reducing VRAM and power consumption on hardware like the RTX 5080.
- Biologically Inspired Learning: Implements Leaky Integrate-and-Fire (LIF) neurons. Training uses Reward-Modulated STDP, allowing for O(1) memory footprint and true temporal credit assignment.
- Hardware-AI Symbiosis: Directly consumes GPU telemetry (Voltage, Wattage, Hashrate) as sensory input, creating a self-regulating "Nervous System" for compute clusters.
🛠️ Technical Specifications
- Architecture: 4-Channel Spiking Engine (12V-Rail, VDDCR, Power-Draw, Hashrate).
- Optimization: Custom CUDA kernels for Blackwell (sm_120) compatibility.
- Modulators: Integrated Dopamine/Cortisol loops (Reward/Stress) and Acetylcholine (Focus) for dynamic system regulation.
📊 Dataset: Sovereign-Telemetry-v1
This repository includes pre-trained weights (parameters.mem) derived from a month-long training span of Dynex Proof-of-Useful-Work (PoUW) solver cycles.
⚖️ Intellectual Sovereignty
This model is released under GPL v3. Our goal is to democratize neuromorphic tools, moving them out of proprietary labs and into the hands of independent researchers and neuro-recovery survivors.
Developed independently by Raul Montoya Cardenas.
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