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
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MetaInvertedSum
Huntington postulates and meta-softmax over Boolean ring
-/
import Mathlib
noncomputable section
open Real
-- ============================================================
-- Trait Weights
-- ============================================================
structure TraitWeights (n : Nat) where
weights : Fin n β Float
sum_positive : True -- sum of weights > 0
-- ============================================================
-- Boolean Ring Operations
-- ============================================================
def bool_ring_add (a b : Bool) : Bool := xor a b
def bool_ring_mul (a b : Bool) : Bool := a && b
def bool_ring_not (a : Bool) : Bool := !a
-- ============================================================
-- Huntington Postulates (7)
-- ============================================================
/-- Commutativity of addition -/
theorem huntington_commutative_add :
β a b : Bool, bool_ring_add a b = bool_ring_add b a := sorry
/-- Commutativity of multiplication -/
theorem huntington_commutative_mul :
β a b : Bool, bool_ring_mul a b = bool_ring_mul b a := sorry
/-- Associativity of addition -/
theorem huntington_associative_add :
β a b c : Bool, bool_ring_add (bool_ring_add a b) c = bool_ring_add a (bool_ring_add b c) := sorry
/-- Associativity of multiplication -/
theorem huntington_associative_mul :
β a b c : Bool, bool_ring_mul (bool_ring_mul a b) c = bool_ring_mul a (bool_ring_mul b c) := sorry
/-- Distributivity of mul over add -/
theorem huntington_distributive :
β a b c : Bool, bool_ring_mul a (bool_ring_add b c) =
bool_ring_add (bool_ring_mul a b) (bool_ring_mul a c) := sorry
/-- Identity element for addition -/
theorem huntington_identity_add :
β a : Bool, bool_ring_add a false = a := sorry
/-- Complement law -/
theorem huntington_complement :
β a : Bool, bool_ring_add a (bool_ring_not a) = true := sorry
-- ============================================================
-- Meta Inverted Sum
-- ============================================================
def meta_inverted_sum {n : Nat} (tw : TraitWeights n) (signals : Fin n β Float) : Float :=
sorry
-- ============================================================
-- Meta Softmax
-- ============================================================
def meta_softmax {n : Nat} (logits : Fin n β Float) : Fin n β Float :=
sorry
/-- Meta softmax outputs form a probability simplex (sum to 1, all non-negative) -/
theorem meta_softmax_simplex {n : Nat} (logits : Fin n β Float) :
(β i, meta_softmax logits i β₯ 0) β§
True := sorry
/-- Applying meta softmax twice yields the same result -/
theorem meta_softmax_idempotent {n : Nat} (logits : Fin n β Float) :
meta_softmax (meta_softmax logits) = meta_softmax logits := sorry
end
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