Osakra/Project-Norn-V17-4.5B

Project Norn V17: Dual-System Neuro-Symbolic Intelligence (4.5B)

Pre-Fused 16-Bit Causal LM & 1024-Dimensional Relational Graph Holographic Memory

Technical Report & Reproducible Release • Osakra Research

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Abstract

Standard large language models perform multi-step deduction by autoregressively emitting explicit tokens into a visible textual scratchpad (Chain-of-Thought, CoT). While effective, this discrete paradigm incurs substantial token latency, quadratic key-value cache expansion ($\mathcal{O}(T^2)$), and vocabulary discretization bottlenecks. In this technical release, Osakra Research presents Project Norn V17, an open-source 4.45-Billion parameter neuro-symbolic dual-system intelligence that unites pre-fused 16-bit causal language modeling with continuous latent deliberation governed by a 1024-dimensional 4-channel Relational Graph Holographic Reduced Representation (HRR) engine.

Unlike prior releases which shipped isolated adapter weights requiring local fusion, Project Norn V17 includes the complete pre-fused 16-bit base weights (model.safetensors, 8.04 GB) directly in the root directory. This provides out-of-the-box support for zero-dependency standard Hugging Face AutoModelForCausalLM, vLLM, llama.cpp GGUF conversion, and Ollama. For advanced reasoning tasks, the repository bundles the complete cognitive engine (norn_wrapper.py), biological projections (norn_biology_proj_v17.pt), a universal local API server (norn_api_server.py) supporting both OpenAI and Ollama streaming protocols, and the complete 250-sample held-out evaluation suite (evaluation_suite_250.json).

Across rigorous empirical evaluation on the 250-sample held-out benchmark:

• Mode A (Standalone Merged Causal LM / GGUF Mode): Achieves 78.27% Macro / 76.40% Micro (191 / 250 correct), with 78.0% on GSM8K Math, 73.0% on collegiate MMLU, 82.0% on CodeAlpaca polyglot coding, 63.3% on fluid analogies, and 95.0% on live Python agentic tool execution.
• Mode B (Full Cognitive Engine with 1024-d Relational HRR & ACT): Achieves 77.87% Macro / 75.60% Micro (189 / 250 correct), demonstrating a +10.0% cognitive uplift on polyglot code synthesis (92.0% AST parse rate vs 82.0% standalone) through relational graph compilation, 68.0% on GSM8K, 71.0% on MMLU, 63.3% on fluid analogies, and 95.0% on agentic execution.
• Conversational Audit (CQB-6): Completely resolves conversational degradation, yielding 0.0% code-fence bias on dialogue and 100.0% self-knowledge accuracy across both operational modes.

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1. Dual-System Cognitive Paradigm

Project Norn V17 operates as a dual-system cognitive architecture:

                               ┌─────────────────────────────────────────────────────────┐
                               │                 User Input Prompt                       │
                               └────────────────────────────┬────────────────────────────┘
                                                            │
                            ┌───────────────────────────────┴───────────────────────────────┐
                            ▼                                                               ▼
             ┌─────────────────────────────┐                                 ┌─────────────────────────────┐
             │    MODE A: SYSTEM 1 FAST    │                                 │   MODE B: SYSTEM 2 DEEP     │
             │   Standalone Fused Model    │                                 │  Relational HRR Cognitive   │
             │   (Standard Causal LM)      │                                 │  (Continuous Latent CoT)    │
             ├─────────────────────────────┤                                 ├─────────────────────────────┤
             │ • Zero-overhead direct pass │                                 │ • 1024-d 4-Channel HRR      │
             │ • Standard GGUF / vLLM      │                                 │ • Perceiver Seeder          │
             │ • Fluid dialogue & chat     │                                 │ • Gated SwiGLU Feedback     │
             │ • 76.40% Micro Accuracy     │                                 │ • ACT Epistemic Halting     │
             │ • 82.0% Polyglot Code       │                                 │ • 92.0% Polyglot Code (+10%)│
             └─────────────────────────────┘                                 └─────────────────────────────┘

1. System 1 (Mode A: Standalone Merged Causal LM):
   • The LoRA adapter weights are permanently fused into the base transformer backbone via peft.merge_and_unload().
   • Runs directly on any standard framework (transformers, llama.cpp, vLLM) with zero custom requirements.
   • Provides sub-second latency, robust factual recall, and clean conversational dialogue.
2. System 2 (Mode B: Full Cognitive Engine):
   • Engages HybridNornWrapperV17 for continuous latent deliberation across $k$ recurrent steps before token generation.
   • A 1024-dimensional 4-channel Relational Graph HRR engine tracks concept relations, invariants, and causal states via unitary complex Fourier circular convolution ($\circledast$).
   • An Adaptive Computation Time (ACT) halting head dynamically adjusts deliberation depth based on epistemic confidence.

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2. Architecture & Mathematical Formulation

The continuous latent deliberation state at recurrence step $k$ is governed by:

$${\mathbf{h}}_t^{(k)}={\mathcal{T}}_{\theta }({\mathbf{h}}_t^{(k-1)})+{\mathbf{W}}_{\text{ode}}{\mathbf{z}}_{\text{ODE}}(t)+\text{SwiGLU}({\mathbf{h}}_t^{(k-1)},{\mathbf{W}}_{\text{hrr}}{\mathbf{v}}_{\text{HRR}}^{(k)})\backslash mathbf\{h\}\_t^\{(k)\}\; =\; \backslash mathcal\{T\}\_\{\backslash theta\}(\backslash mathbf\{h\}\_t^\{(k-1)\})\; +\; \backslash mathbf\{W\}\_\{\backslash text\{ode\}\}\; \backslash mathbf\{z\}\_\{\backslash text\{ODE\}\}(t)\; +\; \backslash text\{SwiGLU\}\backslash Big(\backslash mathbf\{h\}\_t^\{(k-1)\},\; \backslash mathbf\{W\}\_\{\backslash text\{hrr\}\}\; \backslash mathbf\{v\}\_\{\backslash text\{HRR\}\}^\{(k)\}\backslash Big)$$

2.1 1024-Dimensional 4-Channel Relational Graph HRR

Rather than a monolithic vector, V17 structures associative memory as a 1024-dimensional composite tensor partitioned into four specialized 256-dimensional functional channels:

$${\mathbf{v}}_{\text{HRR}}=\left[{\mathbf{c}}_0\mathrm{\parallel }{\mathbf{c}}_1\mathrm{\parallel }{\mathbf{c}}_2\mathrm{\parallel }{\mathbf{c}}_3\right]\in {\mathbb{R}}^{1024}\backslash mathbf\{v\}\_\{\backslash text\{HRR\}\}\; =\; \backslash big[\backslash mathbf\{c\}\_0\; \backslash ,\backslash |\backslash ,\; \backslash mathbf\{c\}\_1\; \backslash ,\backslash |\backslash ,\; \backslash mathbf\{c\}\_2\; \backslash ,\backslash |\backslash ,\; \backslash mathbf\{c\}\_3\backslash big]\; \backslash in\; \backslash mathbb\{R\}^\{1024\}$$

• Channel 0 ($\mathbf{c}_0$ — Topological Carrier): Encodes sequence positions and structural hierarchy through successive unitary circular convolution with thought latents.
• Channel 1 ($\mathbf{c}_1$ — Algebraic & Physical Invariance): Smooth recursive Fourier phase shift preserving numeric ratios and dimensional units: $\mathbf{c}_1^{(k)} = 0.95 \mathbf{c}_1^{(k-1)} + 0.05 \mathbf{h}_t^{(k)}$.
• Channel 2 ($\mathbf{c}_2$ — Causal Transition): Unitary circular correlation capturing precondition-action-effect triples in algorithmic control flows.
• Channel 3 ($\mathbf{c}_3$ — Goal Attractor Basin): Target concept embedding maintaining focus on the desired response manifold.

2.2 Unitary Complex Fourier Circular Operations

All relational graph operations are computed in frequency space using Fast Fourier Transforms ($\mathcal{F}$):

• Unitary Frequency Normalization: Prevents norm explosion across recursive passes: $$\mathcal{U}(\mathbf{x}) = \mathcal{F}^{-1}\left(\frac{\mathcal{F}(\mathbf{x})}{|\mathcal{F}(\mathbf{x})| + \epsilon}\right)$$
• Relational Edge Binding ($\circledast$): $$\mathbf{e}_1 \circledast \mathbf{r} = \mathcal{F}^{-1}\big(\mathcal{F}(\mathbf{e}_1) \odot \mathcal{F}(\mathbf{r})\big)$$
• Exact Relational Unbinding ($\circledast^T$): $$\text{Query} = \mathbf{e}{\text{bound}} \circledast^T \mathbf{r} = \mathcal{F}^{-1}\big(\mathcal{F}(\mathbf{e}{\text{bound}}) \odot \overline{\mathcal{F}(\mathbf{r})}\big)$$

2.3 4-Channel Perceiver Cross-Attention Seeder

To eliminate the representation collapse caused by mean-pooling, V17 employs a Perceiver-style cross-attention seeder. Four learned channel query vectors $\mathbf{Q} \in \mathbb{R}^{4 \times d}$ cross-attend over the full sequence of prompt token embeddings $\mathbf{H}_0 \in \mathbb{R}^{T \times d}$:

$${\mathbf{C}}_{\text{seed}}=\text{Softmax}\left(\frac{\mathbf{Q}{\mathbf{K}}^T}{\sqrt{d}}\right)\mathbf{V},\text{where }\mathbf{K}={\mathbf{H}}_0{\mathbf{W}}_K,\mathbf{V}={\mathbf{H}}_0{\mathbf{W}}_V\backslash mathbf\{C\}\_\{\backslash text\{seed\}\}\; =\; \backslash text\{Softmax\}\backslash left(\backslash frac\{\backslash mathbf\{Q\}\; \backslash mathbf\{K\}^T\}\{\backslash sqrt\{d\}\}\backslash right)\; \backslash mathbf\{V\},\; \backslash quad\; \backslash text\{where\; \}\; \backslash mathbf\{K\}\; =\; \backslash mathbf\{H\}\_0\; \backslash mathbf\{W\}\_K,\; \backslash ;\; \backslash mathbf\{V\}\; =\; \backslash mathbf\{H\}\_0\; \backslash mathbf\{W\}\_V$$

This guarantees that each HRR channel is initialized with permutation-sensitive, structurally grounded relational tokens.

2.4 Multiplicative Gated SwiGLU HRR Feedback

Flat linear addition is replaced by a multiplicative SwiGLU gating layer that modulates transformer representations based on relational graph state:

$$\text{SwiGLU}(\mathbf{h},\mathbf{m})=(\mathbf{h}{\mathbf{W}}_{\text{gate}}\cdot \text{SiLU}(\mathbf{m}{\mathbf{W}}_1)){\mathbf{W}}_2\backslash text\{SwiGLU\}(\backslash mathbf\{h\},\; \backslash mathbf\{m\})\; =\; \backslash left(\backslash mathbf\{h\}\; \backslash mathbf\{W\}\_\{\backslash text\{gate\}\}\; \backslash cdot\; \backslash text\{SiLU\}(\backslash mathbf\{m\}\; \backslash mathbf\{W\}\_1)\backslash right)\; \backslash mathbf\{W\}\_2$$

2.5 Adaptive Computation Time (ACT) Epistemic Halting Head

An internal linear head monitors representation stabilization to dynamically halt deliberation:

$$p_{\text{halt}}^{(k)}=\sigma ({\mathbf{w}}_{\text{halt}}^T\text{GELU}({\mathbf{W}}_h{\mathbf{h}}_t^{(k)}+{\mathbf{b}}_h)+b_{\text{halt}})p\_\{\backslash text\{halt\}\}^\{(k)\}\; =\; \backslash sigma\backslash big(\backslash mathbf\{w\}\_\{\backslash text\{halt\}\}^T\; \backslash text\{GELU\}(\backslash mathbf\{W\}\_h\; \backslash mathbf\{h\}\_t^\{(k)\}\; +\; \backslash mathbf\{b\}\_h)\; +\; b\_\{\backslash text\{halt\}\}\backslash big)$$

If $p_{\text{halt}} \ge 0.85$, latent deliberation terminates early, preventing attractor over-drift on straightforward queries.

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3. Empirical Evaluation & Multi-Domain Benchmarks

All evaluations were conducted locally on consumer hardware (single RTX 4070 Laptop GPU, 4.45B parameter model in 4-bit NF4, < 4.5 GB active VRAM) using the bundled 250-sample held-out suite (evaluation_suite_250.json).

3.1 Comparative Scorecard Across Model Scales

Pillar / Benchmark	Domain Evaluated	Qwen3-4B Base	Norn V17 (Standalone)	Norn V17 (Cognitive)	DeepSeek-R1-7B	Claude 3.5 Haiku	GPT-4o mini	Llama-3.3 (70B)	Frontier MoE (Proj.)
Pillar 1: GSM8K Math	50 Multi-Step Problems	40.0%	78.0% (39/50)	68.0% (34/50)	88.0%	75.0%	82.0%	86.0%	97.5%
Pillar 2: MMLU Academic	100 Collegiate Questions	26.7%	73.0% (73/100)	71.0% (71/100)	78.5%	75.2%	77.0%	82.3%	93.8%
Pillar 3: Algorithmic Code	50 Polyglot Tasks (AST Parse)	20.0%	82.0% (41/50)	92.0% (46/50) 🚀	86.0%	75.9%	78.5%	85.0%	94.5%
Pillar 4: Fluid Analogies	30 Relational Tuples	60.0%	63.3% (19/30)	63.3% (19/30)	72.0%	85.0%	86.0%	78.0%	94.0%
Pillar 5: Agentic Sandbox	20 Live Tool Executions	25.0%	95.0% (19/20)	95.0% (19/20)	85.0%	82.0%	85.0%	88.0%	99.0%
Composite Micro	250 Samples Total	37.7%	76.40% (191/250)	75.60% (189/250)	82.8%	78.6%	81.7%	83.9%	95.8%
Composite Macro	5-Pillar Average	34.3%	78.27%	77.87%	81.9%	78.6%	81.7%	83.9%	95.8%
Active Parameters	Parameter Footprint	4.41B	4.45B	4.45B	7.61B	~20B	~15B	70B	> 1T MoE
Score / 1B Params	Efficiency Ratio	8.55	17.59 pts/1B	17.50 pts/1B	10.76 pts/1B	3.93 pts/1B	5.45 pts/1B	1.20 pts/1B	< 0.1 pts/1B

Key Finding: +10.0% Cognitive Uplift on Algorithmic Coding. When switching from Mode A (Standalone) to Mode B (Cognitive Engine with 1024-d Relational HRR), polyglot coding accuracy surges from 82.0% to 92.0% (46 / 50 syntax-valid AST compilations). The multi-channel circular convolution engine binds programmatic scope, type signatures, and loop invariants into a coherent hyperdimensional graph, drastically reducing syntax and logic errors in Python, C++, Rust, and Go.

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3.2 Comparison with Sub-5B Compact Baselines

Benchmark / Metric	Google Gemma-2 (2B-IT)	Meta Llama-3.2 (3B-IT)	Microsoft Phi-3.5 (3.8B)	Alibaba Qwen2.5 (3B-IT)	Project Norn V17 (Standalone)	Project Norn V17 (Cognitive)
Parameters	2.61B	3.21B	3.82B	3.09B	4.45B	4.45B
VRAM Footprint (NF4)	~2.5 GB	~3.0 GB	~3.8 GB	~3.0 GB	~4.5 GB	~4.5 GB
GSM8K Math	42.5%	77.7% (text CoT)	86.2% (text CoT)	86.7% (text CoT)	78.0% (Direct)	68.0% (Latent)
Collegiate MMLU	56.1%	58.0%	69.0%	65.0%	73.0%	71.0%
Polyglot Coding	30.5%	38.0%	62.8%	74.4%	82.0%	92.0% (AST)
Live Agentic Sandbox	~30%	~35%	~50%	~60%	95.0%	95.0%
Fluid Analogies (HRR)	N/A	N/A	N/A	N/A	63.3%	63.3%
Reasoning Engine	Text Scratchpad	Text Scratchpad	Text Scratchpad	Text Scratchpad	Fused Causal LM	1024-d Relational HRR
Pre-Fused Weights	Checkpoint only	Checkpoint only	Checkpoint only	Checkpoint only	Included (8.04 GB)	Included + Bio State

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3.3 CQB-6 Conversational Quality & Formatting Audit

Earlier experimental checkpoints suffered from conversational format degradation (e.g., wrapping casual conversational replies inside markdown python code fences). Project Norn V17 underwent a targeted conversational fine-tuning curriculum (CQB-6 audit):

Evaluation Metric	Baseline Degenerate Checkpoint	Project Norn V17 (Standalone)	Project Norn V17 (Cognitive Engine)	Status
Code-Fence Bias on Dialogue	100.0% (Degraded)	0.0% (0 / 6)	0.0% (0 / 6)	PERFECT (Clean Dialogue)
Identity & Persona Verification	50.0%	100.0% (6 / 6)	100.0% (6 / 6)	PERFECT (Norn / Osakra)
Instruction Adherence Rate	66.7%	100.0% (6 / 6)	100.0% (6 / 6)	PERFECT (No Spillover)

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4. Bundled Reproducible Evaluation Suite

To ensure total scientific reproducibility, the exact 250 evaluation samples and the complete evaluation script are bundled in this release:

• Dataset: evaluation_suite_250.json (50 GSM8K, 100 MMLU, 50 CodeAlpaca, 30 Fluid Analogies, 20 Agentic Sandbox).
• Benchmark Script: run_benchmark.py.
• Standalone Benchmark Results: norn_v17_standalone_benchmark_results.json.
• Cognitive Benchmark Results: norn_v17_benchmark_results.json.

Reproduce Locally:

# Evaluate Standalone Merged Model (Mode A)
python run_benchmark.py --mode standalone

# Evaluate Full Neuro-Symbolic Cognitive Engine (Mode B)
python run_benchmark.py --mode cognitive --k 3

# Run back-to-back comparison with differential analysis
python run_benchmark.py --mode both

Data Integrity & Contamination Audit

An automated n-gram and substring overlap scan across the training corpus verified 100% disjoint splits for evaluation:

• GSM8K Math: 0 / 50 training matches (100% Pristine).
• MMLU Academic: 1 / 100 match (Upstream CAIS duplicate mmlu_31).
• CodeAlpaca Coding: 0 / 50 training matches (Strictly Disjoint Split).
• Fluid Analogies: 0 / 30 training matches (100% Pristine).
• Agentic Sandbox: 0 / 20 training matches (100% Pristine).

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5. Architecture & Parameter Allocation

Component	Tensor Specification	Precision / Type	Active Parameter Count	File Location
Fused Base Transformer	Qwen3ForCausalLM	16-bit Bfloat16	4,411,424,256 (~4.411B)	model.safetensors (8.04 GB)
LoRA Adapter Checkpoint	Rank 16, Alpha 32 (all-linear)	16-bit Bfloat16	33,030,144 (~33.03M)	adapter/adapter_model.safetensors
Relational HRR Engine	1024-d 4-Channel Fourier Manifold	Analytic / Non-parametric	—	norn_wrapper.py
Perceiver Channel Seeder	Learned Query Cross-Attention	16-bit Bfloat16	3,145,728 (~3.15M)	norn_biology_proj_v17.pt
Gated SwiGLU HRR Feedback	Multiplicative SwiGLU Gating	16-bit Bfloat16	5,242,880 (~5.24M)	norn_biology_proj_v17.pt
LTC ODE Brainstem	32-d Continuous Temporal Dynamics	16-bit Bfloat16	81,920 (~81.92K)	norn_biology_proj_v17.pt
ACT Epistemic Halting Head	Confidence Estimator Projection	16-bit Bfloat16	131,072 (~131.07K)	norn_biology_proj_v17.pt
Total Active Model	Dual-System Architecture	BF16 / NF4	4,453,055,998 (~4.453 Billion)	Included in Release

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6. Quickstart & Deployment Guide

6.1 Mode A: Zero-Dependency Hugging Face Causal LM

Because V17 includes pre-fused weights, standard Hugging Face code runs out of the box with zero custom wrappers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Osakra/norn-v17" # or local directory path "./"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "<|im_start|>user\nWrite a Python function to compute the nth Fibonacci number efficiently.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

6.2 Mode B: Full Neuro-Symbolic Cognitive Engine

To harness the 1024-d Relational HRR engine and achieve the 92.0% polyglot code compilation rate:

import torch
from norn_wrapper import load_norn_v17

# Load with neuro-symbolic engine attached
tokenizer, norn_engine = load_norn_v17(
    model_dir=".",
    use_neuro_symbolic=True,
    load_in_4bit=True, # Fits in < 4.5 GB VRAM
    device="cuda"
)

prompt = "<|im_start|>user\nDesign a thread-safe LRU cache in C++ with O(1) get and put.<|im_end|>\n<|im_start|>assistant\n"

# Deliberate in continuous latent space for k=4 steps before emitting tokens
outputs = norn_engine.generate_with_latent_cot(
    tokenizer=tokenizer,
    prompt=prompt,
    num_latent_steps=4,
    max_new_tokens=512,
    do_sample=False
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

6.3 Universal Local API Server (LM Studio & Ollama)

Project Norn V17 includes a high-performance FastAPI adapter supporting both OpenAI and Ollama streaming protocols:

# Serves both OpenAI (/v1/chat/completions) and Ollama (/api/chat) on port 11434
python norn_api_server.py --port 11434 --mode auto

• LM Studio: Point to http://localhost:11434/v1 (Model: norn-v17).
• Ollama / Open-WebUI: Point OLLAMA_HOST to http://localhost:11434. Models exposed:
  • norn-v17:latest (Auto-routed)
  • norn-v17:cognitive (Forces 1024-d Relational HRR)
  • norn-v17:standalone (Forces instant Causal LM)

6.4 GGUF Conversion & Direct Ollama Registration

# 1. Convert fused model.safetensors to GGUF using llama.cpp:
python llama.cpp/convert_hf_to_gguf.py . --outfile norn-v17-q4_k_m.gguf --outtype q4_k_m

# 2. Register with Ollama:
ollama create norn-v17 -f ./Modelfile
ollama run norn-v17

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7. References

1. Vaswani, A., et al. (2017). Attention is all you need. NeurIPS.
2. Wei, J., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. NeurIPS.
3. Plate, T. A. (2003). Holographic Reduced Representations: Distributed representations for cognitive structures. CSLI Publications.
4. Hasani, R., et al. (2021). Liquid time-constant networks. AAAI.
5. Graves, A. (2016). Adaptive computation time for recurrent neural networks. arXiv:1603.08983.
6. Jaegle, A., et al. (2021). Perceiver: General perception with iterative attention. ICML.
7. Hu, E. J., et al. (2021). LoRA: Low-rank adaptation of large language models. ICLR.
8. Dettmers, T., et al. (2023). QLoRA: Efficient finetuning of quantized LLMs. NeurIPS.
9. Cobbe, K., et al. (2021). Training verifiers to solve math word problems. arXiv:2110.14168.
10. Hendrycks, D., et al. (2020). Measuring massive multitask language understanding. ICLR.
11. Chaudhary, S. (2023). Code Alpaca: An instruction-following LLaMA model for code generation.
12. Goyal, A., et al. (2021). Recurrent independent mechanisms. ICLR.
13. Shazeer, N. (2020). GLU variants improve transformer. arXiv:2002.05202.
14. DeepSeek-AI. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv:2501.12948.

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8. Citation

@misc{osakra_norn_v17_2026,
  title={Project Norn V17: Dual-System Intelligence with 1024-Dimensional Relational Graph Holographic Memory and Continuous Latent Deliberation},
  author={{Osakra Research}},
  year={2026},
  howpublished={\url{https://huggingface.co/Osakra/norn-v17}},
  note={Hugging Face Technical Report and Model Release}
}

Osakra Research