Instructions to use nebada1101/Nexus-VMC-v1.4-RCL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nebada1101/Nexus-VMC-v1.4-RCL with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nebada1101/Nexus-VMC-v1.4-RCL",
	filename="nexus-vmc-v1.4.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nebada1101/Nexus-VMC-v1.4-RCL with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0
# Run inference directly in the terminal:
llama-cli -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0
# Run inference directly in the terminal:
llama-cli -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Use Docker

docker model run hf.co/nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

LM Studio
Jan
Ollama
How to use nebada1101/Nexus-VMC-v1.4-RCL with Ollama:
```
ollama run hf.co/nebada1101/Nexus-VMC-v1.4-RCL:Q8_0
```

Unsloth Studio new

How to use nebada1101/Nexus-VMC-v1.4-RCL with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nebada1101/Nexus-VMC-v1.4-RCL to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nebada1101/Nexus-VMC-v1.4-RCL to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nebada1101/Nexus-VMC-v1.4-RCL to start chatting

Pi new

How to use nebada1101/Nexus-VMC-v1.4-RCL with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nebada1101/Nexus-VMC-v1.4-RCL:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nebada1101/Nexus-VMC-v1.4-RCL with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use nebada1101/Nexus-VMC-v1.4-RCL with Docker Model Runner:
```
docker model run hf.co/nebada1101/Nexus-VMC-v1.4-RCL:Q8_0
```

Lemonade

How to use nebada1101/Nexus-VMC-v1.4-RCL with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nebada1101/Nexus-VMC-v1.4-RCL:Q8_0

Run and chat with the model

lemonade run user.Nexus-VMC-v1.4-RCL-Q8_0

List all available models

lemonade list

Nexus-VMC-v1.4-RCL / README.md

nebada1101

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B
	tags:
	- medical
	- tool-use
	- reasoning
	- excel
	- assistant
	- vmc-rcl
	model_name: Nexus-VMC-v1.4-RCL
	---

	# Nexus-VMC-v1.4-RCL (1.5B) - Unified Strategic Intelligence

	Nexus-VMC-v1.4-RCL is a high-performance Small Language Model (SLM) meticulously fine-tuned for the Antigravity Hub. It excels in medical reasoning, Excel data logic, and complex tool-calling via the VMC-RCL (Reasoning-Call-Logic) protocol.

	## Key Features

	- Strategic Reasoning: Native support for `<vmc_think>` blocks, enabling deep analytical processing before task execution.
	- Precision Tool-Calling: 100% adherence to `[TOOL_CALL]` JSON schemas in comparative benchmarking.
	- Unified Domain Mastery: Expert-level performance in Medical clinical reasoning, Excel formula generation (SUMIF, etc.), and Document RAG.
	- Ultra-Efficient Inference: Optimized for local deployment on 8GB RAM environments (Laptop GPUs, CPU inference).

	## Benchmark Results (Phase 1.4 RCL)

	Benchmarked against SLM baselines and 9B-class models on a 4070 Laptop GPU (Ollama API).

	\| Model \| Avg TPS \| Med Reasoning \| Excel Logic \| Hub Protocol Adherence \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| Nexus-VMC-v1.4-RCL \| 130.3 \| PASS (Strategic) \| PASS (SUMIF) \| Native (S-Tier) \|
	\| hauhau-qwen9b \| 37.2 \| PASS (Text) \| PASS (Text) \| B-Tier \|
	\| phi3.5:latest \| 94.1 \| PARTIAL (Python) \| PASS (Formula) \| C-Tier \|
	\| qwen2.5:1.5b \| 196.4 \| FAIL (Manual) \| FAIL (Repeated) \| D-Tier \|

	## Usage Examples

	### VMC-RCL Protocol
	```text
	User: 병원의 재원 할당을 최적화하기 위해 현재 환자 수를 분석해줘.

	Assistant:
	<vmc_think>
	환자 수 데이터를 기반으로 한 최적화 분석이 필요함.
	hospital_warehouse 도구를 호출하여 실시간 데이터를 가져오는 논리적 흐름 설정.
	</vmc_think>
	[TOOL_CALL]
	{"name": "mcp_tool", "kwargs": {"target": "hospital_warehouse", "query": "current_patient_census"}}
	```

	## How to Run locally (Ollama)

	1. Download the `nexus-vmc-v1.4.Q8_0.gguf` file.
	2. Create a `Modelfile`:
	```text
	FROM ./nexus-vmc-v1.4.Q8_0.gguf
	TEMPLATE "<\|im_start\|>user\n{{ .Prompt }}<\|im_end\|>\n<\|im_start\|>assistant\n"
	PARAMETER stop "<\|im_end\|>"
	```
	3. Run `ollama create nexus-v1.4 -f Modelfile`
	4. Run `ollama run nexus-v1.4`

	## License
	[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)