---

language:

• en

license: apache-2.0

tags:

• qwen2
• text-generation-inference
• reinforcement-learning
• grpo
• rlvr
• reasoning
• gguf
• ollama
• industrial
• code

base_model: Qwen/Qwen2.5-Coder-7B-Instruct

pipeline_tag: text-generation

---

Reggie v3.5 RLVR (Reinforcement Learning with Verifiable Rewards)

Reggie v3.5 RLVR is a sovereign, hyper-intelligent 7B industrial AI engineered by James Brouse (Brouse Group) in Chilliwack, British Columbia.

This model marks a major architectural milestone: transitioning Reggie from standard Supervised Fine-Tuning (SFT) to Local Reinforcement Learning with Verifiable Rewards (RLVR) via Group Relative Policy Optimization (GRPO), trained directly on an on-premises consumer NVIDIA GeForce RTX 3080 10GB.

---

⚡ Model Files in this Repository

| File | Description | Size | Status |

|---|---|---|---|

| reggie_v3.5_rlvr_production.Q4_K_M.gguf | Full 50-Step Production GRPO Alignment across 95 verified industrial pairs | 4.68 GB | Recommended / Latest |

| reggie_v3_rlvr.Q4_K_M.gguf | 20-Step Baseline GRPO Checkpoint | 4.68 GB | Benchmark / Historical |

| Modelfile | Ollama Modelfile configured for instant loading of the production weights | 580 B | Active |

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⚡ The RLVR Breakthrough

Traditional reasoning distillation frequently suffers from cyclic token loops, verbose hesitation, and hallucinations in specialized shop math. Reggie v3.5 RLVR addresses this with programmatic verifier-guided alignment:

During GRPO rollouts, multiple candidate reasoning trajectories are generated per prompt and evaluated against four deterministic reward functions:

1. Structured <think> Formatting Verifier:

- Enforces strict delimiter boundaries (<think> ... </think>).

- Dynamically penalizes cyclic repetition and self-reflection loops.

1. Exact Numerical & Symbolic Math Verifier:

- Programmatically verifies closed-form solutions against ground truth.

- Evaluates boxed expressions and fractional simplifications.

1. Sandboxed Code Execution Verifier:

- Automatically extracts Python code blocks and executes them in an isolated subprocess against unit assertions (e.g. assert calc_stepper_pulses(...) == 68000).

1. Sovereign Persona Grounding Verifier:

- Rewards direct, authoritative industrial domain knowledge (NoSAS ERP, Oakmont Door takeoff logic, RS485 bus protocol, RF telemetry).

- Heavily penalizes generic, apologetic AI boilerplate.

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🎯 Verified Capabilities & Benchmarks

1. Complex Kinematics & CNC Pulse Calculation

> Prompt: Calculate the step pulses required for a 200 step/rev stepper motor driving a 4mm lead screw over 120mm linear travel with a 2:1 reduction ratio.

• Deliberation:

$$\text{Lead screw revs} = \frac{120\text{ mm}}{4\text{ mm/rev}} = 30\text{ revs}$$

$$\text{Motor revs} = 30\text{ revs} \times 2 = 60\text{ revs}$$

$$\text{Total step pulses} = 60 \times 200 = \mathbf{12,000\text{ steps}}$$

• Accuracy: 100% verified with zero looping.

2. Multi-Step Dependent Probability

> Prompt: Determine the probability that at least one of two independent events occurs: rolling a sum > 7 on two six-sided dice, OR drawing an Ace in two draws from a standard 52-card deck without replacement.

• Deliberation:

- $P(A) = \frac{15}{36} = \frac{5}{12} \approx 0.4167$

- $P(B) = 1 - \left(\frac{48}{52} \times \frac{47}{51}\right) = \frac{33}{221} \approx 0.1493$

- $P(A \cup B) = \frac{1105 + 396 - 165}{2652} = \frac{1336}{2652} = \frac{334}{663} \approx \mathbf{50.38\%}$

• Accuracy: Verified across dual methods (complement rule & combinations).

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🚀 Instant Quickstart with Ollama

You can run Reggie v3.5 RLVR directly using Ollama:

ollama run hf.co/Bigchilli1978/reggie-v3.5-rlvr-7b-GGUF

Or clone and build locally:

git clone https://huggingface.co/Bigchilli1978/reggie-v3.5-rlvr-7b-GGUF
cd reggie-v3.5-rlvr-7b-GGUF
ollama create reggie:v3.5 -f Modelfile
ollama run reggie:v3.5

---

🏗️ Technical Specifications

• Base Model: Qwen/Qwen2.5-Coder-7B-Instruct
• Parameters: 7.61B
• Context Window: 32,768 tokens
• Quantization: Q4_K_M (4.68 GB)
• VRAM Required: ~5.6 GB (Fits 100% inside NVIDIA RTX 3060, 3080, 4060, 4070, or Apple Silicon)
• Whitepaper: The Reggie Chronicles: Autonomous Local Intelligence & On-Premises RLVR
• Author: James Brouse, Brouse Group (Chilliwack, BC)