Unified Strategic Intelligence for Medical & Enterprise Reasoning
Developed by Yoons-AI-LAB, Nexus-VMC-v1.4-RCL is a high-performance Small Language Model (SLM) engineered for the Antigravity Hub ecosystem. It delivers "Large Model" reasoning integrity and tool-calling precision in a compact 1.5B parameter architecture.
The goal of the Nexus evolution is to prove that high-integrity reasoning (Thinking-Chain) and complex functional calling can be internalized into ultra-lightweight models reachable on consumer hardware (8GB RAM).
| Rank | Model Name | Domain | Integrity (Think/Tool) | Speed (TPS) |
|---|---|---|---|---|
| 🥇 1 | Nexus-VMC-v1.4-RCL | Unified (Med/Excel) | 100% / 100% | 130.3 |
| 🥈 2 | hauhau-qwen9b | General/Large | 50% / 0% | 37.2 |
| 🥉 3 | phi3.5:latest | General/SLM | 0% / 0% | 94.1 |
Benchmarked on 4070 Laptop GPU via Ollama API. TPS = Tokens Per Second.
- VMC-RCL Protocol: Native support for reasoning-first execution using
<vmc_think>blocks followed by[TOOL_CALL]JSON integration. - Medical Reasoning: Fine-tuned on specialized clinical scenarios and hospital resource management logic.
- Excel Master: Accurate generation of complex spreadsheet logic, including
SUMIFand data cleaning formulas. - Efficient Deployment: Optimized for Ollama/llama.cpp with a high-fidelity Q8_0 GGUF quantization.
The models are hosted on Hugging Face: 🔗 nebada1101/Nexus-VMC-v1.4-RCL
# 1. Download the .gguf from Hugging Face
# 2. Create the Modelfile provided in this repo
ollama create nexus-v1.4-rcl -f Modelfile
ollama run nexus-v1.4-rclThis repository contains the core infrastructure used to develop and validate the model:
nexus_standardizer.py: Tool for normalizing HF weights for GGUF conversion.weight_cleaner.py: Strips non-standard quantization artifacts from safetensors.benchmark.py: The official Hub multi-domain benchmarking suite.hf_uploader.py: automated deployment script for the Hugging Face Hub.
Apache 2.0 - Developed by Yoons-AI-LAB.
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