Image-Text-to-Text
GGUF
quantization
quantized
apex
custom-quantization
unsloth-studio
Mixture of Experts
multimodal
vision
agentic
computer-use
llama.cpp
qwen35moe
imatrix
conversational
Instructions to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF: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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF: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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
Use Docker
docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
- Ollama
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Ollama:
ollama run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
- Unsloth Desktop
- Pi
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Docker Model Runner:
docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
- Lemonade
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
Run and chat with the model
lemonade run user.Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF: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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Move navigation to top and clarify optional support
Browse files
README.md
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quantized_by: IsValorum
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---
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> [!NOTE]
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> ### ARCHITECTURE SELECTION GUIDE — MINIPLUS V1 & V2.1 EDITIONS
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> This repository hosts the **MiniPlus V1** edition of **Nex-N2.5-mini**. Our releases are precision-engineered for specific hardware budgets and memory topologies. **V1 is NOT obsolete or inferior; it represents our leanest, most agile operating profile:**
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---
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## <a id="quick-navigation"></a>Quick Navigation Index
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- [Model Files & Technical Specifications](#model-specifications)
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- [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
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- [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
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- [Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
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- [Everyday Laptop Guidance (DDR4 / DDR5 RAM)](#laptop-benchmarks)
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- [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
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- [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
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- [Recommended Generation Parameters (Creator Official)](#generation-parameters)
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- [Model Inherent Behavior vs. Quantization Fidelity Notice](#quantization-fidelity)
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- [Optional Support](#optional-support)
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> [!IMPORTANT]
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> ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP
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> These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do:
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## Optional Support
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> [!NOTE]
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> ### Optional Support —
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> Every
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quantized_by: IsValorum
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---
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## <a id="quick-navigation"></a>Quick Navigation Index
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- [Model Files & Technical Specifications](#model-specifications)
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- [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
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- [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
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- [Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
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- [Everyday Laptop Guidance (DDR4 / DDR5 RAM)](#laptop-benchmarks)
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- [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
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- [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
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- [Recommended Generation Parameters (Creator Official)](#generation-parameters)
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- [Model Inherent Behavior vs. Quantization Fidelity Notice](#quantization-fidelity)
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- [Optional Support](#optional-support)
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> [!NOTE]
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> ### ARCHITECTURE SELECTION GUIDE — MINIPLUS V1 & V2.1 EDITIONS
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> This repository hosts the **MiniPlus V1** edition of **Nex-N2.5-mini**. Our releases are precision-engineered for specific hardware budgets and memory topologies. **V1 is NOT obsolete or inferior; it represents our leanest, most agile operating profile:**
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---
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> [!IMPORTANT]
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> ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP
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> These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do:
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## Optional Support
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> [!NOTE]
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> ### Optional Support — MiniPlus & NanoPlus Will Always Remain Free
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> Every MiniPlus and NanoPlus release is and will permanently remain free. If you find this work useful and would like to help cover evaluation, hosting, and future handcrafted quantizations, optional support is available through [Ko-fi](https://ko-fi.com/isvalorum). No model files, updates, or features are paywalled.
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