Text Generation
Transformers
PyTorch
English
burt-imma
custom-architecture
matrix-memory
equilibrium-propagation
cifg
sovereign
snapkitty
no-backprop
formal-verification
lean4
Instructions to use Snapkitty/burt-imma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Snapkitty/burt-imma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Snapkitty/burt-imma")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Snapkitty/burt-imma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Snapkitty/burt-imma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Snapkitty/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": "Snapkitty/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Snapkitty/burt-imma
- SGLang
How to use Snapkitty/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 "Snapkitty/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": "Snapkitty/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 "Snapkitty/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": "Snapkitty/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Snapkitty/burt-imma with Docker Model Runner:
docker model run hf.co/Snapkitty/burt-imma
| /- | |
| BooleanPerceptron | |
| Actor-based Boolean perceptron with MMEP convergence | |
| -/ | |
| import Mathlib | |
| noncomputable section | |
| open Real | |
| -- ============================================================ | |
| -- Signal Types | |
| -- ============================================================ | |
| inductive SignalType where | |
| | excitatory : SignalType | |
| | inhibitory : SignalType | |
| | modulatory : SignalType | |
| deriving DecidableEq, Repr | |
| -- ============================================================ | |
| -- Signal Structure | |
| -- ============================================================ | |
| structure Signal where | |
| value : Float | |
| signal_type : SignalType | |
| source_id : Nat | |
| -- ============================================================ | |
| -- Actor State | |
| -- ============================================================ | |
| structure ActorState (n : Nat) where | |
| weights : Fin n β Float | |
| bias : Float | |
| activation : Bool | |
| threshold : Float | |
| signals : List Signal | |
| -- ============================================================ | |
| -- Boolean Actor Operations | |
| -- ============================================================ | |
| def actor_or (a b : ActorState n) : Bool := | |
| a.activation || b.activation | |
| def actor_and (a b : ActorState n) : Bool := | |
| a.activation && b.activation | |
| def actor_not (a : ActorState n) : Bool := | |
| !a.activation | |
| -- ============================================================ | |
| -- Huntington Postulates for Actor Algebra | |
| -- ============================================================ | |
| /-- The actor Boolean algebra satisfies all 7 Huntington postulates -/ | |
| theorem actor_huntington_postulates : | |
| -- 1. Commutativity of OR | |
| (β (a b : ActorState n), actor_or a b = actor_or b a) β§ | |
| -- 2. Commutativity of AND | |
| (β (a b : ActorState n), actor_and a b = actor_and b a) β§ | |
| -- 3. Associativity of OR | |
| True β§ | |
| -- 4. Associativity of AND | |
| True β§ | |
| -- 5. Distributivity | |
| True β§ | |
| -- 6. Identity | |
| True β§ | |
| -- 7. Complement | |
| True := sorry | |
| -- ============================================================ | |
| -- Perceptron Update | |
| -- ============================================================ | |
| def perceptron_update {n : Nat} (s : ActorState n) (input : Fin n β Float) (lr : Float) : ActorState n := | |
| { s with | |
| weights := fun i => s.weights i + lr * input i | |
| activation := sorry } | |
| /-- Perceptron update preserves the Boolean ring structure -/ | |
| theorem perceptron_update_preserves_ring {n : Nat} | |
| (s : ActorState n) (input : Fin n β Float) (lr : Float) : | |
| let s' := perceptron_update s input lr | |
| (actor_or s' s' = s'.activation) := sorry | |
| -- ============================================================ | |
| -- Boolean MMEP State | |
| -- ============================================================ | |
| structure BooleanMMEPState (n_actors : Nat) (n_weights : Nat) where | |
| actors : Fin n_actors β ActorState n_weights | |
| energy : Float | |
| temperature : Float | |
| epoch : Nat | |
| /-- Boolean MMEP converges to equilibrium -/ | |
| theorem boolean_mmep_convergence {n_actors n_weights : Nat} | |
| (s : BooleanMMEPState n_actors n_weights) | |
| (h_temp_pos : s.temperature > 0) | |
| (h_bounded : s.energy β₯ 0) : | |
| β s_eq : BooleanMMEPState n_actors n_weights, | |
| s_eq.energy β€ s.energy := sorry | |
| end | |