Instructions to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/tencent-WeVisOne-VL-2B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/tencent-WeVisOne-VL-2B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/tencent-WeVisOne-VL-2B-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 prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/tencent-WeVisOne-VL-2B-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 prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/tencent-WeVisOne-VL-2B-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": "prithivMLmods/tencent-WeVisOne-VL-2B-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/prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/tencent-WeVisOne-VL-2B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/tencent-WeVisOne-VL-2B-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/tencent-WeVisOne-VL-2B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/tencent-WeVisOne-VL-2B-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/tencent-WeVisOne-VL-2B-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": "prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tencent-WeVisOne-VL-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/tencent-WeVisOne-VL-2B-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 prithivMLmods/tencent-WeVisOne-VL-2B-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 prithivMLmods/tencent-WeVisOne-VL-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/tencent-WeVisOne-VL-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/tencent-WeVisOne-VL-2B-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 "prithivMLmods/tencent-WeVisOne-VL-2B-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"

tencent-WeVisOne-VL-2B-GGUF

WeVisOne-VL-2B (tencent/WeVisOne-VL-2B) is an open-weight 2B-parameter vision-language model from Tencent WeChat Vision, released under Apache-2.0 as a compact member of the WeVisOne-VL series built on Qwen3-VL. It unifies object-level perception, including open-vocabulary detection, referring expression localization, counting, point grounding, and OCR, with pixel-level tasks such as referring segmentation and promptable alpha matting. Using structured next-token prediction without explicit chain-of-thought reasoning, it encodes normalized coordinates with dedicated tokens (<0> to <1000>), returns None for absent targets, and uses <OBJ> tokens to support dense prediction. Its lightweight 9.5M–9.6M-parameter pixel decoder combines multi-level visual features, box-guided deformable attention, and progressive dense prediction to generate full-resolution alpha mattes. Trained on approximately 100 million finely annotated images through a three-stage supervised fine-tuning curriculum followed by mixed-task reinforcement learning, the model has an approximate 4.9 GB footprint in BF16. It supports object detection, grounding, counting, OCR, segmentation, and matting; no benchmark scores were provided in the supplied description. Deployment is available through the wevisone_vl Python package, Hugging Face Transformers, vLLM, and SGLang.

Model Files

File Name	Quant Type	File Size	File Link	Description
WeVisOne-VL-2B.BF16.gguf	BF16	4.08 GB	Link	Full BF16 weights. Highest quality, largest file size.
WeVisOne-VL-2B.Q3_K_L.gguf	Q3_K_L	1.14 GB	Link	Lower quality but usable, good for low RAM availability.
WeVisOne-VL-2B.Q3_K_M.gguf	Q3_K_M	1.08 GB	Link	Low quality.
WeVisOne-VL-2B.Q4_K_M.gguf	Q4_K_M	1.28 GB	Link	Good quality, default size for most use cases, recommended.
WeVisOne-VL-2B.Q4_K_S.gguf	Q4_K_S	1.24 GB	Link	Slightly lower quality with more space savings, recommended.
WeVisOne-VL-2B.Q5_K_M.gguf	Q5_K_M	1.47 GB	Link	High quality, recommended.
WeVisOne-VL-2B.Q5_K_S.gguf	Q5_K_S	1.45 GB	Link	High quality, recommended.
WeVisOne-VL-2B.Q6_K.gguf	Q6_K	1.68 GB	Link	Very high quality, near perfect, recommended.
WeVisOne-VL-2B.Q8_0.gguf	Q8_0	2.17 GB	Link	Extremely high quality, generally unneeded but max available quant.
WeVisOne-VL-2B.mmproj-bf16.gguf	mmproj-bf16	823 MB	Link	Multimodal projection file in BF16 format. Used for vision/language models.

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

Downloads last month: 569

GGUF

Model size

2B params

Architecture

qwen3vl

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for prithivMLmods/tencent-WeVisOne-VL-2B-GGUF

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October 2026

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