Text Generation
Transformers
PyTorch
English
burt-imma
custom-architecture
matrix-memory
equilibrium-propagation
cifg
sovereign
snapkitty
no-backprop
formal-verification
lean4
agents
python
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")# pip install -U transformers accelerate # 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
File size: 3,349 Bytes
b88c26d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | /-
SuperpositionedInduction
Quantum-inspired superposition with decoherence and convergence
-/
import Mathlib
noncomputable section
open Real Matrix
-- ============================================================
-- Core Definitions
-- ============================================================
variable {n : Nat}
/-- Interference matrix is positive semi-definite -/
def is_psd (M : Matrix (Fin n) (Fin n) β) : Prop :=
β v : Fin n β β, Finset.univ.sum (fun i =>
Finset.univ.sum (fun j => v i * M i j * v j)) β₯ 0
/-- Orthogonality loss measures deviation from orthogonality -/
def orthogonality_loss (M : Matrix (Fin n) (Fin n) β) : β :=
sorry
/-- Validity score for a superposition state -/
def validity_score (amplitudes : Fin n β β) : β :=
Finset.univ.sum (fun i => amplitudes i ^ 2)
/-- Threshold operation: zero out amplitudes below threshold -/
def threshold_op (amplitudes : Fin n β β) (t : β) : Fin n β β :=
fun i => if amplitudes i ^ 2 β₯ t then amplitudes i else 0
/-- Decoherence map (contraction) -/
def decoherence_map (M : Matrix (Fin n) (Fin n) β) (gamma : β) : Matrix (Fin n) (Fin n) β :=
fun i j => if i = j then M i j else gamma * M i j
-- ============================================================
-- Theorems (8)
-- ============================================================
/-- The interference matrix is positive semi-definite -/
theorem interference_psd (M : Matrix (Fin n) (Fin n) β)
(h_hermitian : M = Mα΅)
(h_diag_pos : β i, M i i β₯ 0) :
is_psd M := sorry
/-- Orthogonality loss is non-negative -/
theorem orthogonality_loss_nonneg (M : Matrix (Fin n) (Fin n) β) :
orthogonality_loss M β₯ 0 := sorry
/-- Orthogonality loss is zero iff columns are orthogonal -/
theorem orthogonality_loss_zero_iff (M : Matrix (Fin n) (Fin n) β)
(h_square : True) :
orthogonality_loss M = 0 β (β i j, i β j β Mα΅ i j = 0) := sorry
/-- Validity score is bounded in [0, 1] for normalized states -/
theorem validity_bounded (amplitudes : Fin n β β)
(h_norm : Finset.univ.sum (fun i => amplitudes i ^ 2) β€ 1) :
validity_score amplitudes β€ 1 := sorry
/-- Thresholding preserves validity (doesn't increase norm) -/
theorem threshold_preserves_valid (amplitudes : Fin n β β) (t : β)
(h_t_pos : t > 0)
(h_valid : validity_score amplitudes β€ 1) :
validity_score (threshold_op amplitudes t) β€ 1 := sorry
/-- Decoherence is a contraction mapping -/
theorem decoherence_contraction (M : Matrix (Fin n) (Fin n) β) (gamma : β)
(h_gamma : 0 < gamma β§ gamma < 1) :
β i j, i β j β
|decoherence_map M gamma i j| < |M i j| := sorry
/-- Iterative decoherence converges to diagonal -/
theorem iterative_convergence (M : Matrix (Fin n) (Fin n) β) (gamma : β)
(h_gamma : 0 < gamma β§ gamma < 1)
(steps : Nat) :
β i j, i β j β
|(Nat.iterate (decoherence_map Β· gamma) steps M) i j| β€
gamma ^ steps * |M i j| := sorry
/-- Superposition respects linearity: combining states is linear -/
theorem superposition_linearity
(a1 a2 : Fin n β β) (c1 c2 : β) :
(fun i => c1 * a1 i + c2 * a2 i) =
(fun i => c1 * a1 i + c2 * a2 i) := sorry
end
|