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
File size: 8,773 Bytes
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// Visualize with: dot -Tpng constraint_graph.dot -o constraint_graph.png
// Or: dot -Tsvg constraint_graph.dot -o constraint_graph.svg
digraph BURT_IMMA {
// Global graph attributes
rankdir=TB;
fontname="Helvetica";
fontsize=12;
label="BURT-IMMA Constraint Graph\nProject: BURT-IMMA | Contact: jessica@collectivekitty.com | License: BSL-1.1";
labelloc=t;
compound=true;
nodesep=0.8;
ranksep=1.0;
// Node defaults
node [
shape=box,
style="rounded,filled",
fontname="Helvetica",
fontsize=10,
fillcolor="#E8F4FD",
color="#2C3E50"
];
// Edge defaults
edge [
fontname="Helvetica",
fontsize=8,
color="#34495E"
];
// =============================================
// Subgraph: Feature Extraction
// =============================================
subgraph cluster_feature_extraction {
label="Feature Extraction";
style="rounded,dashed";
color="#3498DB";
fontcolor="#3498DB";
fontsize=11;
Input [
label="Input\n[batch, seq_len]",
shape=ellipse,
fillcolor="#AED6F1"
];
SmoothLeaky [
label="SmoothLeaky\nActivation\n\nk=1.0, alpha=0.01\nSmooth for EP",
fillcolor="#D4EFDF"
];
GatesNorm [
label="GatesNorm\nNormalization\n\ngamma, beta learnable\nPer-expert dim",
fillcolor="#D4EFDF"
];
}
// =============================================
// Subgraph: Memory System
// =============================================
subgraph cluster_memory_system {
label="Memory System";
style="rounded,dashed";
color="#E74C3C";
fontcolor="#E74C3C";
fontsize=11;
CIFGMemory [
label="CIFGMemory\n\nC_global: [d, d]\nC_expert: [K, d_e, d_e]\nf = sigma(W_f * [h,x])\nC_new = f*C + (1-f)*candidate",
fillcolor="#FADBD8",
shape=box3d
];
}
// =============================================
// Subgraph: Routing
// =============================================
subgraph cluster_routing {
label="Routing (Sparse MoE)";
style="rounded,dashed";
color="#F39C12";
fontcolor="#F39C12";
fontsize=11;
GatesRouter [
label="GatesRouter\n\ntop_k=2\nentropy_bound=0.20\nL1 sparsity",
fillcolor="#FEF9E7"
];
Expert1 [
label="Expert 1\n[d_expert=256]",
fillcolor="#FDF2E9"
];
Expert2 [
label="Expert 2\n[d_expert=256]",
fillcolor="#FDF2E9"
];
Expert3 [
label="Expert 3\n[d_expert=256]",
fillcolor="#FDF2E9"
];
Expert4 [
label="Expert 4\n[d_expert=256]",
fillcolor="#FDF2E9"
];
}
// =============================================
// Subgraph: Output Head
// =============================================
subgraph cluster_output_head {
label="Output Head";
style="rounded,dashed";
color="#8E44AD";
fontcolor="#8E44AD";
fontsize=11;
InductionHeads [
label="Superpositioned\nInduction Heads\n\nnum_heads=8\nCopy pattern detection\nMemory superposition",
fillcolor="#E8DAEF"
];
QInterference [
label="Quantum\nInterference\nResolver\n\nAmplitude combination\nConstructive/destructive",
fillcolor="#E8DAEF"
];
Output [
label="Output\n[batch, seq_len, vocab]\n\nConstrained Softmax\nentropy <= 0.20",
shape=ellipse,
fillcolor="#D2B4DE"
];
}
// =============================================
// Data Flow Edges
// =============================================
// Feature extraction flow
Input -> SmoothLeaky [
label="embed + pos_encode"
];
SmoothLeaky -> GatesNorm [
label="activated features"
];
// Memory interaction
GatesNorm -> CIFGMemory [
label="h (hidden state)",
style=bold
];
CIFGMemory -> InductionHeads [
label="memory context\nC_global @ h",
color="#E74C3C"
];
// Routing flow
GatesNorm -> GatesRouter [
label="routing logits"
];
GatesRouter -> Expert1 [
label="alpha_1",
style=dashed,
constraint=false
];
GatesRouter -> Expert2 [
label="alpha_2",
style=dashed,
constraint=false
];
GatesRouter -> Expert3 [
label="alpha_3",
style=dashed,
constraint=false
];
GatesRouter -> Expert4 [
label="alpha_4",
style=dashed,
constraint=false
];
// Expert to interference
Expert1 -> QInterference [
label="out_1"
];
Expert2 -> QInterference [
label="out_2"
];
Expert3 -> QInterference [
label="out_3"
];
Expert4 -> QInterference [
label="out_4"
];
// Expert memory update
Expert1 -> CIFGMemory [
label="update C_expert[1]",
style=dotted,
color="#E74C3C",
constraint=false
];
Expert2 -> CIFGMemory [
label="update C_expert[2]",
style=dotted,
color="#E74C3C",
constraint=false
];
// Induction heads
GatesNorm -> InductionHeads [
label="Q, K, V projections"
];
// Output assembly
InductionHeads -> QInterference [
label="attention output",
style=bold
];
QInterference -> Output [
label="resolved output",
style=bold
];
// Feedback (memory write from output)
Output -> CIFGMemory [
label="memory write\n(CIFG gated)",
style=dotted,
color="#E74C3C",
dir=back,
constraint=false
];
// =============================================
// Constraint Annotations
// =============================================
// Constraint nodes (diamond shape)
node [
shape=diamond,
fillcolor="#FDEBD0",
color="#D35400",
fontsize=9,
width=1.5,
height=0.8
];
C_entropy [
label="ENTROPY\n<= 0.20 nats"
];
C_spectral [
label="SPECTRAL\nsigma_max <= 0.95"
];
C_huntington [
label="HUNTINGTON\nPostulates"
];
// Constraint edges
C_entropy -> GatesRouter [
style=bold,
color="#D35400",
label="enforced",
dir=both
];
C_entropy -> Output [
style=bold,
color="#D35400",
label="enforced",
dir=both
];
C_spectral -> CIFGMemory [
style=bold,
color="#D35400",
label="sigma_max(C)",
dir=both
];
C_spectral -> Expert1 [
style=bold,
color="#D35400",
label="sigma_max(W)",
dir=both
];
C_spectral -> InductionHeads [
style=bold,
color="#D35400",
label="sigma_max(W_QKV)",
dir=both
];
C_huntington -> QInterference [
style=bold,
color="#D35400",
label="Boolean lattice",
dir=both
];
// =============================================
// Legend
// =============================================
subgraph cluster_legend {
label="Legend";
style="rounded";
color="#7F8C8D";
fontcolor="#7F8C8D";
fontsize=10;
node [shape=plaintext, fillcolor=white, fontsize=8];
legend [label=<
<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4">
<TR><TD COLSPAN="2"><B>Edge Styles</B></TD></TR>
<TR><TD>Solid bold</TD><TD>Primary data flow</TD></TR>
<TR><TD>Dashed</TD><TD>Routing decisions</TD></TR>
<TR><TD>Dotted (red)</TD><TD>Memory updates</TD></TR>
<TR><TD>Bold (orange)</TD><TD>Constraint enforcement</TD></TR>
<TR><TD COLSPAN="2"><B>Constraints</B></TD></TR>
<TR><TD>entropy_bound</TD><TD>0.20 nats</TD></TR>
<TR><TD>lambda_max</TD><TD>0.95</TD></TR>
<TR><TD>huntington</TD><TD>true (all postulates)</TD></TR>
</TABLE>
>];
}
}
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