Instructions to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ReminiScenceAI/Vintern-1B-v3_5-GGUF",
	filename="Vintern-1B-v3_5-Q4_K_M.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ReminiScenceAI/Vintern-1B-v3_5-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 ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ReminiScenceAI/Vintern-1B-v3_5-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 ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ReminiScenceAI/Vintern-1B-v3_5-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": "ReminiScenceAI/Vintern-1B-v3_5-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/ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

Ollama
How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with Ollama:
```
ollama run hf.co/ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M
```

Unsloth Studio new

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF 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 ReminiScenceAI/Vintern-1B-v3_5-GGUF 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 ReminiScenceAI/Vintern-1B-v3_5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ReminiScenceAI/Vintern-1B-v3_5-GGUF to start chatting

Pi new

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

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": "ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ReminiScenceAI/Vintern-1B-v3_5-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 ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with Docker Model Runner:
```
docker model run hf.co/ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M
```

Lemonade

How to use ReminiScenceAI/Vintern-1B-v3_5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ReminiScenceAI/Vintern-1B-v3_5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vintern-1B-v3_5-GGUF-Q4_K_M

List all available models

lemonade list

Vintern-1B-v3.5 GGUF

Description

This repository contains GGUF quantized weights for Vintern-1B-v3.5, developed by 5CD-AI.

Vintern-1B-v3.5 is a state-of-the-art Multimodal Large Language Model (MLLM) optimized for the Vietnamese language. Despite its compact size of 1 billion parameters, it demonstrates exceptional performance in document understanding, OCR, and detailed image description, making it ideal for edge computing and local deployment.

Key Improvements in v3.5

Superior Vietnamese Support: Fine-tuned to understand Vietnamese cultural nuances and complex linguistic structures.
Efficient Architecture: Based on a lightweight backbone, offering high-speed inference without compromising visual reasoning.
GGUF Compatibility: Optimized for local execution via llama.cpp, LM Studio, and other GGUF-supported ecosystems.

Available Quantization Methods

File	Quantization	Description
`vintern-1b-v3.5-q4_k_m.gguf`	Q4_K_M	Recommended. Balanced performance and accuracy.
`vintern-1b-v3.5-q8_0.gguf`	Q8_0	High precision, near-original quality but larger file size.
`vintern-1b-v3.5-q5_k_m.gguf`	Q5_K_M	Better accuracy than Q4 with a slight increase in RAM usage.

How to Use

1. Requirements

To run this Vision-Language Model, you need both the Main GGUF file and the Multi-Modal Projector (mmproj) file.

2. Using llama.cpp

Download the latest version of llama.cpp, following instruction from Llama.cpp's GitHub and run:

./llama-cli -m vintern-1b-v3.5-q4_k_m.gguf \
  --mmproj vintern-1b-v3.5-mmproj-f16.gguf \
  --image path/to/your/image.jpg \
  -p "Describe this image in detail in Vietnamese."

Downloads last month: 39

GGUF

Model size

0.6B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

View +1 variant