Instructions to use SNAPKITTYWEST/burt-imma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SNAPKITTYWEST/burt-imma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SNAPKITTYWEST/burt-imma")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SNAPKITTYWEST/burt-imma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SNAPKITTYWEST/burt-imma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SNAPKITTYWEST/burt-imma" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SNAPKITTYWEST/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SNAPKITTYWEST/burt-imma
- SGLang
How to use SNAPKITTYWEST/burt-imma 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 "SNAPKITTYWEST/burt-imma" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SNAPKITTYWEST/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "SNAPKITTYWEST/burt-imma" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SNAPKITTYWEST/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SNAPKITTYWEST/burt-imma with Docker Model Runner:
docker model run hf.co/SNAPKITTYWEST/burt-imma
BURT-IMMA
Matrix-Memory Equilibrium Propagation - a 13-layer architecture that forks the backprop lineage.
SnapKitty West / SNAPKITTYWEST - Evidence or Silence - 2026
Model Description
13-layer architecture. Learning rule is Matrix-Memory Equilibrium Propagation (MMEP): two-phase, locally computable, Hebbian-style update. No gradient tape. No weight transport.
Core memory cell (CIFG):
C_t = f_t * C_{t-1} + (1-f_t) * (v_t outer k_t)
| Component | Description |
|---|---|
| CIFG Matrix Memory | Full d x d state, sum-inversion retrieval |
| MMEP Learning Rule | Two-phase local update, no backprop |
| Entropy-Constrained Router | H(alpha) <= 0.20 nats (sovereign_entropy.tex) |
| Spectral Projection | Contractive relaxation guarantee |
| SmoothLeaky Activation | 4-axiom formal specification |
| Boolean Perceptron Actors | Huntington postulate-verified routing |
| 7 CUDA Kernels | sm_86 (RTX 3080) |
| Lean 4 Formalization | Machine-checked proof ledger |
Training
- Hardware: RTX 3080 sm_86, 7 custom CUDA kernels
- Learning rule: MMEP (no Adam, no SGD)
- Corpus: SNAPKITTYWEST/sovereign-training-corpus
- Weights: Architecture + code released; pretrained weights pending
Papers - 11 Total (8 Zenodo + 3 In-Repo LaTeX)
| File | Lines | Venue | Title |
|---|---|---|---|
| gdr_kernels.tex | 460 | SC / MLSys | GDR-9: Formally Verified Generalized Delta Rule Kernel Stack Across Nine ISAs |
| liquidops_kernel.tex | 566 | PLDI / ICFP | LiquidOps: Refinement-Verified NAND-Canonical Kernel for Data-Plane ISA Compilation |
| sovereign_entropy.tex | 394 | FM / CAV | Sovereign Entropy: Formally Verified 0.20-Nat Bound for Deterministic Agent Outputs |
GDR-9 - Nine-ISA kernel stack (CUDA, NASM, RISC-V, ARM, WASM, APL, Haskell, Lean 4, P4). Generalized delta rule + drain invariants. Cross-ISA equivalence proved in Lean 4. Target: SC/MLSys.
LiquidOps - NAND-canonical verified compiler. LiquidHaskell refinement types enforce correctness end-to-end. P4 backend. Target: PLDI/ICFP.
Sovereign Entropy - Lean 4 machine-checked proof: routing entropy H(alpha) <= 0.20 nats. SPARK Ada contract + ERE gate. Governs BURT-IMMA router and sovereign-memory-twin verdict threshold. Target: FM/CAV.
Zenodo (8 papers, published 2026-07-01) - PIRTM, octonions, Coxeter/Weyl, PH-DAE, EmojiScript, Goldilocks, Resonance disclosure, DMZ F2 decomposition. DOIs in sovereign-engine-v2 README.
License
Apache-2.0
- Downloads last month
- -