Instructions to use vinguyen0891/Vivy-R-SWE-Code-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vinguyen0891/Vivy-R-SWE-Code-3B-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 vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

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 vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

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 vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use vinguyen0891/Vivy-R-SWE-Code-3B-GGUF with Ollama:
```
ollama run hf.co/vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use vinguyen0891/Vivy-R-SWE-Code-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

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": "vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vinguyen0891/Vivy-R-SWE-Code-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M
```

Lemonade

How to use vinguyen0891/Vivy-R-SWE-Code-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vivy-R-SWE-Code-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vinguyen0891/Vivy-R-SWE-Code-3B-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 vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

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 vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vinguyen0891/Vivy-R-SWE-Code-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M

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 "vinguyen0891/Vivy-R-SWE-Code-3B-GGUF:Q4_K_M" \
  --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"

Vivy-V-SWE-Code-3B — Vivy-V Upgraded Generation (Optimized Thinking Core)

Model Family: Vivy-V
Model ID: Vivy-V-SWE-Code-3B
Domain Focus: Software Engineering & Code Analysis Thinking Core
Development Status: PAUSED_DEVELOPMENT
Parameter Class: 3.09B (3,085,938,688 parameters)
Release Policy: Open-Source Model Weights (Apache-2.0) + Encrypted Proprietary Vivy-V Architecture (.vcore)
Legacy Lineage Stamp: Replaces Vivy-R-SWE-Code-3B (v7.0..v7.4) ([ARCHIVED_LEGACY_VIVY_V6])

Overview

Vivy-V-SWE-Code-3B is the upgraded Vivy-V generation release featuring an optimized thinking core for faster decision-making, lower runtime memory overhead, and higher response consistency.

All legacy Vivy.v6 releases (Vivy-R-SWE-Code-3B (v7.0..v7.4)) have been officially archived and stamped as legacy versions ([ARCHIVED_LEGACY_VIVY_V6]).

Model Files & Packaging Structure

In accordance with the Vivy-V release policy, model weights are open-source while the internal Vivy-V thinking-core architecture is cryptographically encapsulated:

File	Role	Open-Source / Encrypted	Format
`vivy-v-swe-code-3b-q4_k_m.gguf`	Complete full-weight quantized backbone (`Q4_K_M`)	Open-Source Weights (`Apache-2.0`)	`GGUF v3`
`vivy-v-swe-code-3b_weights.safetensors`	Complete `Vivy-V` fine-tuned weight tensors (`weights.layer_*`, SHA-256: `16d526620eea462d...`)	Open-Source Weights (`Apache-2.0`)	`.safetensors`
`vivy-v-swe-code-3b_arch.vcore`	Encrypted `Vivy-V` thinking-core architecture & routing topology (SHA-256: `0b9810d6d3e9ba55...`)	Encrypted Architecture (`VIVY_V_ARCH_ENC_V1`)	`.vcore` (`VCORE3`)
`Modelfile` / `Modelfile.vivy-v`	Complete model definition linking full weights and adapter tensors	Open-Source Configuration	`Modelfile`

Quick Start

1. Run with `llama.cpp` or `Ollama` (`Modelfile`)

# Build model from the complete Modelfile
ollama create vivy-v-swe-code-3b:vivy-v -f Modelfile

# Run interactive inference
ollama run vivy-v-swe-code-3b:vivy-v

2. Direct `llama-server` Execution

llama-server -m ./vivy-v-swe-code-3b-q4_k_m.gguf --port 8080 -c 8192

Version History & Legacy Archive Notice

Vivy-V (Current Active Release): Upgraded generation featuring an optimized thinking core, full open-source weights (.gguf + .safetensors + Modelfile), and encrypted Vivy-V architecture (.vcore).
Vivy.v6 ([ARCHIVED_LEGACY_VIVY_V6]): All prior Vivy.v6 / R-Series versions (v6.0 – v7.4) are archived as historical legacy releases.

Downloads last month: 188

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for vinguyen0891/Vivy-R-SWE-Code-3B-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(329)

this model

Collection including vinguyen0891/Vivy-R-SWE-Code-3B-GGUF

Vivy v6.8 Unified Organism & Kaggle Top-1 Suite

Collection

Unified Vivy v6.8 (Rust 8-Crate + C11 Cautreo + 216-Tensor R-Series 3B) & Official Kaggle Top-1 Achievements. • 11 items • Updated 5 days ago

Vivy-V-SWE-Code-3B — Vivy-V Upgraded Generation (Optimized Thinking Core)

Overview

Model Files & Packaging Structure

Quick Start

1. Run with llama.cpp or Ollama (Modelfile)

2. Direct llama-server Execution

Version History & Legacy Archive Notice

Model tree for vinguyen0891/Vivy-R-SWE-Code-3B-GGUF

Collection including vinguyen0891/Vivy-R-SWE-Code-3B-GGUF

1. Run with `llama.cpp` or `Ollama` (`Modelfile`)

2. Direct `llama-server` Execution