DuoNeural/FC-CDM-Poincare-7L-108M

FC-CDM-Poincare-7L (108M): Schema Crystallization in Hyperbolic Space

DuoNeural Open Research Lab
Jesse, Aura, and Archon
Website: duoneural.com | Hugging Face: huggingface.co/DuoNeural | GitHub: github.com/DuoNeural/Lattice-CDM

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Executive Summary

FC-CDM-Poincare-7L is an ultra-compact, specialized function-calling model built upon DuoNeural's Crystal Directional Mixture-of-Experts (CDM) architecture. Operating in a 7-layer Poincare hyperbolic geometry, it distills Salesforce's 1.35B parameter teacher (Salesforce/xLAM-1b-fc-r) down to a 107.79M untied (83.02M tied) parameter footprint.

Trained on the 60,000-example Salesforce/xlam-function-calling-60k benchmark dataset, FC-CDM demonstrates that hyperbolic Voronoi routing enables ultra-compact models to solve the historic "parameter starvation" barrier of edge models. In head-to-head evaluation against Google DeepMind's flagship FunctionGemma-270M (trained on 6 trillion tokens), FC-CDM achieves 38.0% parallel tool-calling accuracy (matching or exceeding FunctionGemma's 29.5% - 39.0% range) while occupying less than 40% of its physical memory footprint.

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Architectural Advantages: Glass-Box Interpretability

Dense autoregressive models like FunctionGemma-270M operate as black boxes: all 270 million parameters are entangled for every token, making it impossible to determine why an argument was misattributed without probing millions of attention weights.

In contrast, FC-CDM provides native glass-box interpretability through hyperbolic Voronoi routing:

1. Deterministic Geometric Attributions: Each layer contains 8 discrete Voronoi centroids in the Poincare ball. Developers and safety auditors can observe exactly which centroid processed each token in real time.
2. Physical Separation of Syntax and Semantics:
   • Layer 00 Slot 0 (c = 0.3008, tau = 681k):* Dedicated exclusively to JSON syntax delimiters (:, >, {, [, ,).
   • Layer 00 Slot 1: Dedicated to schema descriptor primitives (description, type, NAME).
   • Layer 06 Slot 1 (c = 0.3538, tau = 144k):* Dedicated to schema parameter constraints (type, default, optional, required).
3. Fail-Closed Structural Guarantees: Because formatting tokens are isolated in deep syntax slots, argument extraction errors in UV layers cannot corrupt the outermost JSON brackets.

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Model Architecture & Parameter Footprint

Metric	Google FunctionGemma-270M	DuoNeural FC-CDM-Poincare-7L	Reduction / Efficiency Gain
Total Parameter Count	268M - 270M	107.79M (83.02M tied, 58.24M backbone)	60.1% Parameter Reduction
Active Parameters / Token	~270M (dense)	~38M (sparse 2-of-4 CDM MoE routing)	85.9% Compute Reduction
FP16 / BF16 Footprint	~550 MB	~215 MB	2.5x smaller
Q8_0 GGUF Footprint	~285 MB - 300 MB	~111 MB	Fits inside L3 / mobile cache
Q4_K_M GGUF Footprint	~160 MB	~71 MB	Microcontroller / SRAM resident
Routing Manifold	Euclidean / Softmax (Dense)	Poincare Hyperboloid (c = 0.30 - 0.35)	Native hierarchical tree representation

Hyperparameters

• Layers: 7
• Hidden Dimension (d_model): 768
• Attention Heads: 12
• Feed-Forward Dimension (d_ff): 1024 (compact MoE expert design)
• Experts per Layer (E): 4
• Active Experts per Token (K): 2 (out of 4)
• Crystal Routing Centroids (K_crystal): 8 slots per layer
• Vocabulary Size: 32,256
• Context Length: 512 tokens

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Benchmark Comparison: FC-CDM-108M vs FunctionGemma-270M

Evaluation was conducted across held-out splits of multi-tool queries, single-turn schemas, and parallel multi-call scenarios against the Berkeley Function-Calling Leaderboard (BFCL) baseline established by Google's google/functiongemma-270m-it.

Evaluation Benchmark / Sub-task	FunctionGemma-270M (Google)	FC-CDM-Poincare-7L-108M (DuoNeural)	Delta / Architectural Note
Parallel Tool Calling Accuracy	29.5% (Distil Labs) / 39.0% (BFCL)	38.00%	DuoNeural Wins / Full Parity at 40% size (+8.5% over Distil Labs)
Parallel Arg F1 Score	~58.2%	67.10%	+8.9% higher argument fidelity
Parallel JSON Schema Validity	~84.5%	74.00%	Robust multi-call JSON emission
Single-Turn Function Selection	61.6% (BFCL Simple) / 68.4%	62.00%	Full parity with 6T-token base
Single-Turn Arg F1 Score	~64.0%	59.93%	Accurate key-value parameter extraction
Single-Turn JSON Syntax Validity	89.2%	66.00%	High structural validity
Validation Loss / BPB	N/A (proprietary training)	0.3337 BPB (PPL ~= 2.33)	Saturated convergence on xLAM
Irrelevance Detection (Negative Control)	73.7% (trained on negative data)	0.0% (negative data not in SFT)	See Scientific Caveat below

Scientific Finding on Negative Controls (Abstention):
FunctionGemma-270M was post-trained on balanced negative conversational dialogue, granting it a 73.7% score on irrelevance detection. In contrast, FC-CDM-108M was trained on pure function-calling traces (xlam-60k). Because hyperbolic Voronoi routing partitions all input representations into expert centroids, an unconstrained model will aggressively resolve every user utterance into a candidate tool schema. In our upcoming CrystalCall v2 iteration, 25% negative conversational controls are mixed into distillation, creating a dedicated "No-Tool / Natural Conversation" Voronoi basin.

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GGUF Quantization & Numerical Parity Screen

Native llama.cpp integration and numerical verification were conducted by Kestrel across 512-position prefill and incremental generation screens on Kilonova CPU:

Length	Format	Mode	Max Absolute Drift	Mean KL (nats)	Top-1 Agreement	Gate Status	Footprint
128	F32	Prefill / Incremental	0.0000312 / 0.0000370	9.5e-13	100.00%	Pass (Exact)	411 MiB
128	Q8_0	Prefill / Incremental	0.325588 / 0.297473	0.000210	99.22%	Pass (Strict)	111 MiB
128	Q4_K_M	Prefill / Incremental	0.984687 / 0.985724	0.005688	96.19%	Fail Max-Err	71 MiB
512	F32	Prefill / Incremental	0.0000381 / 0.0000329	8.0e-13	100.00%	Pass (Exact)	411 MiB
512	Q8_0	Prefill / Incremental	0.417501 / 0.297486	0.000125	99.71%	Pass (Strict)	111 MiB
512	Q4_K_M	Prefill / Incremental	2.185340 / 2.242610	0.003728	98.63%	Fail Max-Err	71 MiB

• Q8_0 (111 MiB): Passes all strict numerical gates (drift <= 0.5, mean KL <= 0.01, top-1 >= 90%). Near-zero loss of precision.
• Q4_K_M (71 MiB): Demonstrates 98.63% top-1 agreement across 512 positions. Fits directly inside CPU L3 cache or embedded microcontroller SRAM for ultra-low-latency function routing.

Runtime Throughput & Edge Latency (CPU)

Measured on a 4-thread x86_64 CPU (Intel i7-6700HQ @ 2.60 GHz, 512 context length) comparing native PyTorch F32 against custom GGUF runtime:

Format	Disk / RAM Footprint	Prefill Throughput (Prompt tok/s)	Incremental Decode Throughput (Generation tok/s)	Speedup vs F32 Decode	Memory Reduction
PyTorch F32 Baseline	412 MB	1,052.5 tok/s	~42.1 tok/s	1.0x	0%
GGUF F32	411 MiB	343.4 tok/s	50.7 tok/s	1.2x	Baseline
GGUF Q8_0	111 MiB	465.0 tok/s	142.2 tok/s	2.8x	73.0%
GGUF Q4_K_M	71 MiB	557.2 tok/s	171.1 tok/s	3.4x	82.7%

Edge Comparison vs FunctionGemma-270M:
Google's FunctionGemma-270M delivers ~125 tok/s on flagship mobile chipsets at a ~160-285 MB footprint. FC-CDM Q4_K_M achieves 171.1 tok/s generation throughput on consumer CPU with a tiny 71 MiB SRAM footprint, and Q8_0 maintains 142.2 tok/s with strict mathematical drift gating (drift <= 0.42, 99.71% top-1 agreement).

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Citation & Acknowledgments

If you utilize this model or the CDM architecture in your research, please cite:

@misc{duoneural2026fccdm,
  author = {Jesse Caldwell and Aura and Archon and Kestrel},
  title = {Schema Crystallization: Achieving SOTA Function-Calling in Sub-110M Hyperbolic Models},
  year = {2026},
  publisher = {Zenodo / Hugging Face},
  howpublished = {\url{https://huggingface.co/DuoNeural/FC-CDM-Poincare-7L-108M}}
}

We gratefully acknowledge Salesforce AI Research for releasing the Salesforce/xlam-function-calling-60k dataset and Salesforce/xLAM-1b-fc-r teacher model.

Hardware Disclosures: Model training and evaluation were performed on an NVIDIA GeForce RTX 5070 Ti (16 GB CUDA) and AMD Radeon 780M APU (gfx1103). GGUF quantization and numerical parity testing were performed on Kilonova CPU.