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
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BURT (Bi-encoder Unified Retrieval Transformer) Formalization
Core retrieval and ranking properties
-/
import Mathlib
noncomputable section
open Real
-- ============================================================
-- BURT State
-- ============================================================
structure BURTState (n d : Nat) where
Q : Matrix (Fin n) (Fin d) Float -- query embeddings
D : Matrix (Fin n) (Fin d) Float -- document embeddings
I : Fin n → Float -- inverted index scores
R : Fin n → Fin n -- ranking permutation
C : Float -- CIFG gate value
E : Float -- energy
-- ============================================================
-- Core Theorems
-- ============================================================
/-- Router entropy is bounded by the entropy ceiling -/
theorem router_entropy_bound {n d : Nat} (s : BURTState n d)
(entropy : (Fin n → Float) → Float)
(router_dist : Fin n → Float)
(h_from_state : ∀ i, router_dist i = s.I i) :
entropy router_dist ≤ 0.20 := sorry
/-- CIFG gate conserves memory during retrieval updates -/
theorem cifg_memory_conservation {n d : Nat}
(s : BURTState n d)
(h_gate : 0 ≤ s.C ∧ s.C ≤ 1)
(mem_prev mem_next : Float)
(h_update : mem_next = s.C * mem_prev + (1 - s.C) * s.E) :
mem_next ≤ max mem_prev s.E := sorry
/-- The ranking function produces a valid permutation -/
theorem ranking_is_permutation {n d : Nat} (s : BURTState n d)
(h_inj : Function.Injective s.R) :
Function.Bijective s.R := sorry
/-- Retrieval has sub-quadratic complexity via inverted index -/
theorem retrieval_complexity {n d : Nat} (s : BURTState n d)
(retrieval_flops : Nat)
(h_indexed : retrieval_flops ≤ n * Nat.log n + d) :
retrieval_flops < n * n := sorry
end
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